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{"cells":[{"metadata":{},"cell_type":"markdown","source":"# ** DIscBIO: a user-friendly pipeline for biomarker discovery in single-cell transcriptomics**"},{"metadata":{},"cell_type":"markdown","source":"# CONQUER Notebook"},{"metadata":{},"cell_type":"markdown","source":"CONQUER is a collection of analysis-ready public scRNA-seq data sets. It can be reached through their home page: http://imlspenticton.uzh.ch:3838/conquer/"},{"metadata":{},"cell_type":"markdown","source":"DIscBIO consists of four successive steps: data pre-processing, cellular clustering and pseudo-temporal ordering, determining differential expressed genes and identifying biomarkers."},{"metadata":{},"cell_type":"markdown","source":"![DIsccBIO](DiscBIO.png)"},{"metadata":{},"cell_type":"markdown","source":"## Required Packages"},{"metadata":{"trusted":false},"cell_type":"code","source":"library(MultiAssayExperiment)\nlibrary(SummarizedExperiment)\nlibrary(DIscBIO)\nlibrary(partykit)\nlibrary(enrichR)","execution_count":1,"outputs":[{"output_type":"stream","text":"Loading required package: SummarizedExperiment\n\nLoading required package: GenomicRanges\n\nLoading required package: stats4\n\nLoading required package: BiocGenerics\n\nLoading required package: parallel\n\n\nAttaching package: ‘BiocGenerics’\n\n\nThe following objects are masked from ‘package:parallel’:\n\n    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n    clusterExport, clusterMap, parApply, parCapply, parLapply,\n    parLapplyLB, parRapply, parSapply, parSapplyLB\n\n\nThe following objects are masked from ‘package:stats’:\n\n    IQR, mad, sd, var, xtabs\n\n\nThe following objects are masked from ‘package:base’:\n\n    anyDuplicated, append, as.data.frame, basename, cbind, colnames,\n    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,\n    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,\n    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n    union, unique, unsplit, which, which.max, which.min\n\n\nLoading required package: S4Vectors\n\n\nAttaching package: ‘S4Vectors’\n\n\nThe following object is masked from ‘package:base’:\n\n    expand.grid\n\n\nLoading required package: IRanges\n\nLoading required package: GenomeInfoDb\n\nLoading required package: Biobase\n\nWelcome to Bioconductor\n\n    Vignettes contain introductory material; view with\n    'browseVignettes()'. To cite Bioconductor, see\n    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n\n\nLoading required package: DelayedArray\n\nLoading required package: matrixStats\n\n\nAttaching package: ‘matrixStats’\n\n\nThe following objects are masked from ‘package:Biobase’:\n\n    anyMissing, rowMedians\n\n\n\nAttaching package: ‘DelayedArray’\n\n\nThe following objects are masked from ‘package:matrixStats’:\n\n    colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges\n\n\nThe following objects are masked from ‘package:base’:\n\n    aperm, apply, rowsum\n\n\nLoading required package: SingleCellExperiment\n\n\n\nLoading required package: grid\n\nLoading required package: libcoin\n\nLoading required package: mvtnorm\n\n\nAttaching package: ‘partykit’\n\n\nThe following object is masked from ‘package:SummarizedExperiment’:\n\n    width\n\n\nThe following object is masked from ‘package:DelayedArray’:\n\n    width\n\n\nThe following object is masked from ‘package:GenomicRanges’:\n\n    width\n\n\nThe following object is masked from ‘package:IRanges’:\n\n    width\n\n\nThe following object is masked from ‘package:S4Vectors’:\n\n    width\n\n\nThe following object is masked from ‘package:BiocGenerics’:\n\n    width\n\n\nWelcome to enrichR\nChecking connection ... \nConnection is Live!\n\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"## Loading dataset"},{"metadata":{},"cell_type":"markdown","source":"To use a data set provided in the conquer database, download the corresponding R object into your current working directory.\nHere we will be using the GSE41265 dataset as an example. \n\nGSE41265:  Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells.     \nShalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., ... & Trombetta, J. J. (2013). Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature, 498(7453), 236-240.\nDOI: 10.1038/nature12172\n\nOrganism: Mus musculus\n"},{"metadata":{"trusted":false},"cell_type":"code","source":"GSE41265 <- readRDS(\"GSE41265.rds\")\nDataset=assays(experiments(GSE41265)[[\"gene\"]])[[\"count\"]]\nrownames(Dataset) <- as.list(sub(\"*\\\\..*\", \"\", unlist(rownames(Dataset))))   #### To adjust the gene Ensembl id\ncat(paste0(\"This dataset contains:\",\"\\n\",\"Genes: \",length(Dataset[,1]),\"\\n\",\"cells: \",length(Dataset[1,]),\"\\n\"))","execution_count":2,"outputs":[{"output_type":"stream","text":"This dataset contains:\nGenes: 45686\ncells: 18\n","name":"stdout"}]},{"metadata":{"trusted":false},"cell_type":"code","source":"sc<- DISCBIO(Dataset)       # The DISCBIO class is the central object storing all information generated throughout the pipeline ","execution_count":3,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## 1. Data Pre-processing"},{"metadata":{},"cell_type":"markdown","source":"Prior to applying data analysis methods, it is standard to pre-process the raw read counts resulted from the sequencing. The preprocessing approach depends on the existence or absence of ERCC spike-ins. In both cases, it includes normalization of read counts and gene filtering. \n\n#### Normalization of read counts\nTo account for RNA composition and sequencing depth among samples (single-cells), the normalization method “median of ratios” is used. This method takes the ratio of the gene instantaneous median to the total counts for all genes in that cell (column median). The gene instantaneous median is the product of multiplying the median of the total counts across all cells (row median) with the read of the target gene in each cell. This normalization method makes it possible to compare the normalized counts for each gene equally between samples.\n\n#### Gene filtering\nThe key idea in filtering genes is to appoint the genes that manifest abundant variation across samples. Filtering genes is a critical step due to its dramatic impact on the downstream analysis. In case the raw data includes ERCC spike-ins, genes will be filtered based on variability in comparison to a noise level estimated from the ERCC spike-ins according to an algorithm developed by Brennecke et al (Brennecke et al., 2013). This algorithm utilizes the dependence of technical noise on the average read count and fits a model to the ERCC spike-ins. Further gene filtering can be implemented based on gene expression.\nIn case the raw data does not include ERCC spike-ins, genes will be only filtered based on minimum expression in certain number of cells.\n"},{"metadata":{},"cell_type":"markdown","source":"![DataPre-processing](Data-Preprocessing.png)"},{"metadata":{},"cell_type":"markdown","source":"#### Checking the ERCC spike-ins"},{"metadata":{"trusted":false},"cell_type":"code","source":"ERCC<-subset(sc@expdataAll, grepl('^ERCC-', rownames(sc@expdataAll)))\ncat(paste0(\"This dataset contains \",length(ERCC[,1]),\" ERCC spike-ins.\", \"\\n\"))\nS<-summary(rowMeans(ERCC,na.rm=TRUE))            # It gives an idea about the overall expression of the ERCCs\ncat(paste0(\"Here is a summary of the expression of the ERCCs across cells: \", \"\\n\"))\nprint(S)   ","execution_count":4,"outputs":[{"output_type":"stream","text":"This dataset contains 92 ERCC spike-ins.\nHere is a summary of the expression of the ERCCs across cells: \n   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n 0.0000  0.0000  0.0000  0.1017  0.0000  9.3597 \n","name":"stdout"}]},{"metadata":{},"cell_type":"markdown","source":"### 1.2. Filtering and normalizing the raw data (not based on the ERCCs)"},{"metadata":{},"cell_type":"markdown","source":"To normalize and filter the raw data we use the function Normalizedata() by giving the parameters minexpr and minnumber some values. This function will discard cells with less than mintotal transcripts. Genes that are not expressed at minexpr transcripts in at least minnumber cells are discarded. Furthermore, it will normalize the count reads using the normalization method “median of ratios”.\n          \nTo Finalize the preprocessing the function FinalPreprocessing() should be implemented by setting the parameter \"GeneFlitering\" to ExpF."},{"metadata":{"trusted":false},"cell_type":"code","source":"# Estimating a value for the \"minexpr\" parameter\nwithoutERCC<-subset(sc@expdataAll, grepl('^ENS', rownames(sc@expdataAll)))\ncat(paste0(\"This dataset contains \",length(withoutERCC[,1]),\" genes\", \"\\n\"))\n\nS<-summary(rowMeans(withoutERCC,na.rm=TRUE))            # It gives an idea about the overall expression of the genes\nprint(S)                                                 \nminexpr= S[4]                                       # S2[3] is referring to the median whereas S2[4] is referring to the mean","execution_count":5,"outputs":[{"output_type":"stream","text":"This dataset contains 45594 genes\n    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. \n     0.0      0.0      0.1    408.2     35.3 473333.4 \n","name":"stdout"}]},{"metadata":{"trusted":false},"cell_type":"code","source":"# Estimating a value for the \"minnumber\" parameters\nminnumber= round(length(Dataset[1,])*.3)                             # To be expressed in at 30% of the cells.\nprint(minnumber)","execution_count":6,"outputs":[{"output_type":"stream","text":"[1] 5\n","name":"stdout"}]},{"metadata":{"trusted":false},"cell_type":"code","source":"sc<-Normalizedata(sc, mintotal=1000, minexpr=minexpr, minnumber=minnumber, maxexpr=Inf, downsample=FALSE, dsn=1, rseed=17000) \nsc<-FinalPreprocessing(sc,GeneFlitering=\"ExpF\",export = TRUE)        # The GeneFiltering should be set to \"ExpF\"","execution_count":7,"outputs":[{"output_type":"stream","text":"The gene filtering method = Noise filtering\n\nThe Filtered Normalized dataset contains:\nGenes: 4232\ncells: 18\n\n\n\nThe Filtered Normalized dataset was saved as: filteredDataset.Rdata\n\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"## 2. Cellular Clustering and Pseudo Time ordering"},{"metadata":{},"cell_type":"markdown","source":"Cellular clustering is performed according to the gene expression profiles to detect cellular sub-population with unique properties. After clustering, pseudo-temporal ordering is generated to indicate the cellular differentiation degree. "},{"metadata":{},"cell_type":"markdown","source":"![PSCAN](Clustering.png)"},{"metadata":{},"cell_type":"markdown","source":"## 2.1. K-means Clustering"},{"metadata":{},"cell_type":"markdown","source":"Rare cell type Identification algorithm (RaceID) was used to cluster the pre-processed data using k-means on a similarity distance matrix, which was based on Pearson correlation and the similarity matrix was computed as “1 – Pearson correlation”. The approach of the proposed clustering, i.e., applying k-means on a similarity distance matrix using the Euclidean metric, improves cluster separation. RaceID estimates the number of clusters by finding the minimal clusters' number at the saturation level of gap statistics, which standardizes the within-cluster dispersion. \n\nThe Clustexp() functions has several parameters:\n- object: the outcome of running the DISCBIO() function.\n- clustnr Maximum number of clusters for the derivation of the cluster number by the saturation of mean within-cluster dispersion. Default is 20.\n- bootnr A numeric value of booststrapping runs for \\code{clusterboot}. Default is 50.\n- metric Is the method to transform the input data to a distance object. \n- Metric has to be one of the following: [\"spearman\",\"pearson\",\"kendall\",\"euclidean\",\"maximum\",\"manhattan\",\"canberra\",\"binary\",\"minkowski\"]. \n- do.gap A logical vector that allows generating the number of clusters based on the gap statistics. Default is TRUE.\n- SE.method The SE.method determines the first local maximum of the gap statistics. \n- The SE.method has to be one of the following:[\"firstSEmax\",\"Tibs2001SEmax\",\"globalSEmax\",\"firstmax\",\"globalmax\"]. Default is \"Tibs2001SEmax\"\n- SE.factor A numeric value of the fraction of the standard deviation by which the local maximum is required to differ from the neighboring points it is compared to. Default is 0.25.\n- B.gap Number of bootstrap runs for the calculation of the gap statistics. Default is 50\n- cln Number of clusters to be used. Default is \\code{NULL} and the cluster number is inferred by the saturation criterion.\n- rseed Integer number. Random seed to enforce reproducible clustering results. Default is 17000.\n- quiet if `TRUE`, intermediate output is suppressed"},{"metadata":{},"cell_type":"markdown","source":"![DIsccBIO](KM1.png)"},{"metadata":{},"cell_type":"markdown","source":"#### 2.1.1. Defining the Cells in the clusters generated by k-means clustering"},{"metadata":{"trusted":false},"cell_type":"code","source":"sc<- Clustexp(sc,cln=3,quiet=T,clustnr=8,rseed=17000)    \nplotGap(sc)                                               ### Plotting gap statistics","execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"Plot with title “Gap Statistics”","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nh2ITEhLOnTvXrl07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Cg4q+gtuzt7c+c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\nhB0AQCSEHQBAJIQdAEAkhB0AQCT+B4xFeTrTynunAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### Defining outlier cells based on K-means Clustering"},{"metadata":{},"cell_type":"markdown","source":"Outlier identification is implemented using a background model based on distribution of transcript counts within a cluster. The background model is computed using the mean and the variance of the expression of each gene in a cluster. Outliers are defined as cells with a minimum of “outlg” outlier genes. Here we are setting the minimum number of outlier genes (the “outlg” parameter) to 5% of the number of filtered genes, this is based on the recommendation of De Vienne et al. (De Vienne et al., 2012). "},{"metadata":{"scrolled":false,"trusted":false},"cell_type":"code","source":"outlg<-round(length(sc@fdata[,1]) * 0.05)     # The cell will be considered as an outlier if it has a minimum of 5% of the number of filtered genes as outlier genes.\nOutliers<- FindOutliers(sc, K=3, outminc=5,outlg=outlg,plot = TRUE, quiet = FALSE)","execution_count":9,"outputs":[{"output_type":"stream","text":"The following cells are considered outliers: \n\n\n","name":"stderr"},{"output_type":"stream","text":"named integer(0)\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"plot without title","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### 2.1.2- Evaluating the stability and consistancy of the clusters"},{"metadata":{},"cell_type":"markdown","source":"DIscBIO enables the robustness assessment of the detected clusters in terms of stability and consistency using Jaccard’s similarity statistics and silhouette coefficients. Jaccard’s similarity index provides a comparison of members among clusters to evaluate the stability of the clusters with a range from 0% to 100%. The higher the percentage, the more stable the cluster is. Silhouette coefficients estimate how close each sample in one cluster is to samples in the neighboring clusters, reflecting the consistency of each cluster with a range of [-1, 1]. The higher the cluster mean coefficient, the more consistent the cluster is."},{"metadata":{"trusted":false},"cell_type":"code","source":"# Silhouette plot\nplotSilhouette(sc,K=3)       # K is the number of clusters","execution_count":10,"outputs":[{"output_type":"display_data","data":{"text/plain":"Plot with title “Silhouette plot of (x = kpart, dist = distances)”","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nqUeD/SOfU2hnZ1e7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dn5wEDBqxcuXLK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\n0jklvCUAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{"trusted":false},"cell_type":"code","source":"# Jaccard Plot\nJaccard(sc,Clustering=\"K-means\", K=3, plot = TRUE)     # Jaccard ","execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/html":"<style>\n.list-inline {list-style: none; margin:0; padding: 0}\n.list-inline>li {display: inline-block}\n.list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n</style>\n<ol class=list-inline><li>0.623</li><li>0.364</li><li>0.363</li></ol>\n","text/markdown":"1. 0.623\n2. 0.364\n3. 0.363\n\n\n","text/latex":"\\begin{enumerate*}\n\\item 0.623\n\\item 0.364\n\\item 0.363\n\\end{enumerate*}\n","text/plain":"[1] 0.623 0.364 0.363"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"plot without 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YQcAEBH7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"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### 2.1.3. Cluster plotting using tSNE maps"},{"metadata":{},"cell_type":"markdown","source":"Here you visualize the K-means clusters using t-distributed stochastic neighbor embedding (tSNE), which is a non-linear dimensionality reduction method that places neighbor cells close to each other. "},{"metadata":{"trusted":false},"cell_type":"code","source":"############ Plotting the clusters\nsc<- comptSNE(sc,rseed=15555,quiet = F)\nplottSNE(sc)","execution_count":12,"outputs":[{"output_type":"stream","text":"This function may take time\n\nsigma summary: Min. : 33554432 |1st Qu. : 33554432 |Median : 33554432 |Mean : 33554432 |3rd Qu. : 33554432 |Max. : 33554432 |\n\nEpoch: Iteration #500 error is: 0.386825821591046\n\nEpoch: Iteration #1000 error is: 0.384437812270035\n\nEpoch: Iteration #1500 error is: 0.384437805740511\n\nEpoch: Iteration #2000 error is: 0.384437794799795\n\nEpoch: Iteration #2500 error is: 0.384437780930006\n\nEpoch: Iteration #3000 error is: 0.384437764367191\n\nEpoch: Iteration #3500 error is: 0.384437744620368\n\nEpoch: Iteration #4000 error is: 0.384437721116231\n\nEpoch: Iteration #4500 error is: 0.384437693446411\n\nEpoch: Iteration #5000 error is: 0.384437661303349\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"plot without title","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\ngh0AQCQEOwCASPwPQ6266hzyHmYAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### 2.1.4. Cellular pseudo-time ordering based on k-means clusters "},{"metadata":{"trusted":false},"cell_type":"code","source":"sc<-pseudoTimeOrdering(sc,quiet = TRUE, export = FALSE)\nplotOrderTsne(sc)","execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"Plot with title “Pseudo-time ordering of k-means clustering”","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nhB0AQAhhBwAQQtgBAIQQdgAAIYQdAEAIYQcA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OBQLAGPt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\nV80fOzrpWQCAhAX+GbtMJtPd3V1WFuaFP54+0bHk+X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CAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCA\nQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsA\ngEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7\nAIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAI\nOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBA\nCDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCA\nQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsA\ngEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7\nAIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAI\nOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBA\nCDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCA\nQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgEAIOwCAQAg7AIBACDsAgED8H7H5z+o1vOA8AAAA\nAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### 2.1.5 Plotting the gene expression of a particular gene in a tSNE map"},{"metadata":{"trusted":false},"cell_type":"code","source":"g='ENSMUSG00000000028'                   #### Plotting the log expression of  Cdc45\nplotExptSNE(sc,g)","execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"Plot with title 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dv5jW9XffG5JvVASgQ7AAB8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\nAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"## 3. Determining differentially expressed genes (DEGs)"},{"metadata":{},"cell_type":"markdown","source":"![DIsccBIO](KM2.png)"},{"metadata":{},"cell_type":"markdown","source":"## 3.2 Identifying DEGs using binomial differential expression"},{"metadata":{},"cell_type":"markdown","source":"The function ClustDiffGenes identifies differentially regulated genes for each cluster of the K-means clustering in \ncomparison to the ensemble of all cells. It returns a list with a data.frame element for each cluster that contains the mean expression across all cells not in the cluster (mean.ncl) and in the cluster (mean.cl), the fold-change in the cluster versus all remaining cells (fc), and the p-value for differential expression between all cells in a cluster and all remaining cells. The p-value is computed based on the overlap of negative binomials fitted to the count distributions within the two groups akin to DESeq."},{"metadata":{"trusted":false},"cell_type":"code","source":"ClustDiff<-ClustDiffGenes(sc,K=3,export = T,pValue = 0.05,fdr = 0.001)\n#### To show the result table\nhead(ClustDiff[[1]])                  # The first component \nhead(ClustDiff[[2]])                  # The second component","execution_count":16,"outputs":[{"output_type":"stream","text":"'select()' returned 1:many mapping between keys and columns\n\n'select()' returned 1:many mapping between keys and columns\n\n'select()' returned 1:many mapping between keys and columns\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A matrix: 6 × 2 of type chr</caption>\n<thead>\n\t<tr><th scope=col>DEGsE</th><th scope=col>DEGsS</th></tr>\n</thead>\n<tbody>\n\t<tr><td>ENSMUSG00000000001</td><td>ENSMUSG00000000001</td></tr>\n\t<tr><td>ENSMUSG00000000028</td><td>ENSMUSG00000000028</td></tr>\n\t<tr><td>ENSMUSG00000000056</td><td>ENSMUSG00000000056</td></tr>\n\t<tr><td>ENSMUSG00000000058</td><td>ENSMUSG00000000058</td></tr>\n\t<tr><td>ENSMUSG00000000078</td><td>ENSMUSG00000000078</td></tr>\n\t<tr><td>ENSMUSG00000000085</td><td>ENSMUSG00000000085</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA matrix: 6 × 2 of type chr\n\n| DEGsE | DEGsS |\n|---|---|\n| ENSMUSG00000000001 | ENSMUSG00000000001 |\n| ENSMUSG00000000028 | ENSMUSG00000000028 |\n| ENSMUSG00000000056 | ENSMUSG00000000056 |\n| ENSMUSG00000000058 | ENSMUSG00000000058 |\n| ENSMUSG00000000078 | ENSMUSG00000000078 |\n| ENSMUSG00000000085 | ENSMUSG00000000085 |\n\n","text/latex":"A matrix: 6 × 2 of type chr\n\\begin{tabular}{ll}\n DEGsE & DEGsS\\\\\n\\hline\n\t ENSMUSG00000000001 & ENSMUSG00000000001\\\\\n\t ENSMUSG00000000028 & ENSMUSG00000000028\\\\\n\t ENSMUSG00000000056 & ENSMUSG00000000056\\\\\n\t ENSMUSG00000000058 & ENSMUSG00000000058\\\\\n\t ENSMUSG00000000078 & ENSMUSG00000000078\\\\\n\t ENSMUSG00000000085 & ENSMUSG00000000085\\\\\n\\end{tabular}\n","text/plain":"     DEGsE              DEGsS             \n[1,] ENSMUSG00000000001 ENSMUSG00000000001\n[2,] ENSMUSG00000000028 ENSMUSG00000000028\n[3,] ENSMUSG00000000056 ENSMUSG00000000056\n[4,] ENSMUSG00000000058 ENSMUSG00000000058\n[5,] ENSMUSG00000000078 ENSMUSG00000000078\n[6,] ENSMUSG00000000085 ENSMUSG00000000085"},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A data.frame: 3 × 6</caption>\n<thead>\n\t<tr><th></th><th scope=col>Target Cluster</th><th scope=col>VS</th><th scope=col>Gene number</th><th scope=col>File name</th><th scope=col>Gene number</th><th scope=col>File name</th></tr>\n\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>Cluster 1</td><td>Remaining Clusters</td><td>5653</td><td>Up-DEG-cluster1.csv</td><td>5841</td><td>Down-DEG-cluster1.csv</td></tr>\n\t<tr><th scope=row>2</th><td>Cluster 2</td><td>Remaining Clusters</td><td>3188</td><td>Up-DEG-cluster2.csv</td><td>5436</td><td>Down-DEG-cluster2.csv</td></tr>\n\t<tr><th scope=row>3</th><td>Cluster 3</td><td>Remaining Clusters</td><td>3094</td><td>Up-DEG-cluster3.csv</td><td>4624</td><td>Down-DEG-cluster3.csv</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 3 × 6\n\n| <!--/--> | Target Cluster &lt;chr&gt; | VS &lt;chr&gt; | Gene number &lt;int&gt; | File name &lt;chr&gt; | Gene number &lt;int&gt; | File name &lt;chr&gt; |\n|---|---|---|---|---|---|---|\n| 1 | Cluster 1 | Remaining Clusters | 5653 | Up-DEG-cluster1.csv | 5841 | Down-DEG-cluster1.csv |\n| 2 | Cluster 2 | Remaining Clusters | 3188 | Up-DEG-cluster2.csv | 5436 | Down-DEG-cluster2.csv |\n| 3 | Cluster 3 | Remaining Clusters | 3094 | Up-DEG-cluster3.csv | 4624 | Down-DEG-cluster3.csv |\n\n","text/latex":"A data.frame: 3 × 6\n\\begin{tabular}{r|llllll}\n  & Target Cluster & VS & Gene number & File name & Gene number & File name\\\\\n  & <chr> & <chr> & <int> & <chr> & <int> & <chr>\\\\\n\\hline\n\t1 & Cluster 1 & Remaining Clusters & 5653 & Up-DEG-cluster1.csv & 5841 & Down-DEG-cluster1.csv\\\\\n\t2 & Cluster 2 & Remaining Clusters & 3188 & Up-DEG-cluster2.csv & 5436 & Down-DEG-cluster2.csv\\\\\n\t3 & Cluster 3 & Remaining Clusters & 3094 & Up-DEG-cluster3.csv & 4624 & Down-DEG-cluster3.csv\\\\\n\\end{tabular}\n","text/plain":"  Target Cluster VS                 Gene number File name           Gene number\n1 Cluster 1      Remaining Clusters 5653        Up-DEG-cluster1.csv 5841       \n2 Cluster 2      Remaining Clusters 3188        Up-DEG-cluster2.csv 5436       \n3 Cluster 3      Remaining Clusters 3094        Up-DEG-cluster3.csv 4624       \n  File name            \n1 Down-DEG-cluster1.csv\n2 Down-DEG-cluster2.csv\n3 Down-DEG-cluster3.csv"},"metadata":{}}]},{"metadata":{},"cell_type":"markdown","source":"### Plotting the DEGs"},{"metadata":{},"cell_type":"markdown","source":"Volcano plots are used to readily show the DEGs by plotting significance versus fold-change on the y and x axes, respectively."},{"metadata":{"trusted":false},"cell_type":"code","source":"name<-ClustDiff[[2]][2,4]    ############ Selecting the \"Up-DEG-cluster2.csv \" from the DEGs' binomial table  ##############\nU<-read.csv(file=paste0(name),head=TRUE,sep=\",\")\nVplot<-VolcanoPlot(U,value=0.0001,name=name,FS=0.7,fc=0.75)","execution_count":17,"outputs":[{"output_type":"display_data","data":{"text/plain":"plot without title","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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RVlF4lIHiE0H1D+8EA3ZG6b7/OoeaLs+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\nwdraes4ckv+KLi4ufD4/KSkpICBgzZo1u3fvTklJwdXmgI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3N9ff3HzJkSOfOnYcMGeLv75+YmIgokjxo0KDCQkJdMRSxaADAqFGjcF//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\nyrFTOJMnT27btu3GjRvBf9ux27ZtG8qmiHKNBefNmze4joU0o0aN8vT0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FekG84oLDFRVRcrBg4mOjtIMvi9NTPhjxlQVFxMEkXbyZKax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\n6tSpctkLvLy86K0q0uPBgwfq+kwDFoul+P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Rzc1Or1aaMFHOnEROZ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\naSdgYIcgCIIgCNJO+B/goXXL9nU8GQAAAABJRU5ErkJggg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"## 4. Identifying biomarkers (decision trees and networking analysis) "},{"metadata":{},"cell_type":"markdown","source":"There are several methods to identify biomarkers, among them are decision trees and hub detection through networking analysis. The outcome of STRING analysis is stored in tab separated values (TSV) files. These TSV files served as an input to check both the connectivity degree and the betweenness centrality, which reflects the communication flow in the defined PPI networks "},{"metadata":{},"cell_type":"markdown","source":"![DIsccBIO](KM3.png)"},{"metadata":{},"cell_type":"markdown","source":"Decision trees are one of the most efficient classification techniques in biomarkers discovery. Here we use it to predict the sub-population of a target cell based on transcriptomic data. Two types of decision trees can be performed: classification and regression trees (CART) and J48. The decision tree analysis is implemented over a training dataset, which consisted of the DEGs obtained by either SAMseq or the binomial differential expression. The performance of the generated trees can be evaluated for error estimation by ten-fold cross validation assessment using the \"J48DTeval\" and \"RpartEVAL\" functions. The decision tree analysis requires the dataset to be class vectored by applying the “ClassVectoringDT” function."},{"metadata":{"trusted":false},"cell_type":"code","source":"##########################################################   Creating a gene list that includes up regluated genes in cluster 2\nDEGs=ClustDiff[[2]][2,4]                                   # UP-regulated genes in cluster 2 \ndata<-read.csv(file=paste0(DEGs),head=TRUE,sep=\",\")\nsigDEG<-data[,1:2]\ncolnames(sigDEG)<-c(\"DEGsE\",\"DEGsS\")\n#############################################################\n\nFirst=\"CL2\"\nSecond=\"CL3\"\nDATAforDT<-ClassVectoringDT(sc,Clustering=\"K-means\",K=3,First=First,Second=Second,sigDEG)\n","execution_count":18,"outputs":[{"output_type":"stream","text":"The DEGs filtered normalized dataset contains:\nGenes: 3188\ncells: 15\n\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"### 4.1. J48 Decision Tree"},{"metadata":{"trusted":false},"cell_type":"code","source":"j48dt<-J48DT(DATAforDT)           #J48 Decision Tree\nsummary(j48dt) \nrm(j48dt)","execution_count":19,"outputs":[{"output_type":"stream","text":"J48 pruned tree\n------------------\n\nENSMUSG00000021477 <= 17473.326946: CL3 (8.0)\nENSMUSG00000021477 > 17473.326946: CL2 (7.0)\n\nNumber of Leaves  : \t2\n\nSize of the tree : \t3\n\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"\n=== Summary ===\n\nCorrectly Classified Instances          15              100      %\nIncorrectly Classified Instances         0                0      %\nKappa statistic                          1     \nMean absolute error                      0     \nRoot mean squared error                  0     \nRelative absolute error                  0      %\nRoot relative squared error              0      %\nTotal Number of Instances               15     \n\n=== Confusion Matrix ===\n\n a b   <-- classified as\n 7 0 | a = CL2\n 0 8 | b = CL3"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"plot without title","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAMAAADKOT/pAAADAFBMVEUAAAABAQECAgIDAwME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\nF0JyebBVTLsIrakOyZrheG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QpJy+Nwa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\ngg=="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### 4.1.1. Evaluating the performance of the J48 Decision Tree"},{"metadata":{"trusted":false},"cell_type":"code","source":"j48dt<-J48DTeval(DATAforDT,num.folds=10,First=First,Second=Second)","execution_count":20,"outputs":[{"output_type":"stream","text":"Fold 1 of 10\n\nFold 2 of 10\n\nFold 3 of 10\n\nFold 4 of 10\n\nFold 5 of 10\n\nFold 6 of 10\n\nFold 7 of 10\n\nFold 8 of 10\n\nFold 9 of 10\n\nFold 10 of 10\n\n","name":"stderr"},{"output_type":"stream","text":"TP FN FP TN \n 3  4  3  5 \n             CL2 CL3\nPredictedCL2   3   3\nPredictedCL3   4   5\n","name":"stdout"},{"output_type":"stream","text":"J48 SN: 0.43\nJ48 SP: 0.62\nJ48 ACC: 0.53\nJ48 MCC: 0.05\n\n\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"### 4.2. RPART Decision Tree"},{"metadata":{"trusted":false},"cell_type":"code","source":"rpartDT<-RpartDT(DATAforDT)\nrm(rpartDT)","execution_count":21,"outputs":[{"output_type":"stream","text":"n= 15 \n\nnode), split, n, loss, yval, (yprob)\n      * denotes terminal node\n\n1) root 15 7 CL3 (0.4666667 0.5333333)  \n  2) ENSMUSG00000021477>=20037.52 7 0 CL2 (1.0000000 0.0000000) *\n  3) ENSMUSG00000021477< 20037.52 8 0 CL3 (0.0000000 1.0000000) *\n","name":"stdout"},{"output_type":"display_data","data":{"text/plain":"plot without title","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nuSueW95nVN8Vzy0v31B23t+dlyTJey+/u3Je8YT7v9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IOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcA\nEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAA\nghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBA\nEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAI\nQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABB\nCDsAgCCEHQBAEMIOACAIYQcAEISwAwAIQtgBAAQh7AAAghB2AABBCDsAgCCEHQBAEMIOACAI\nYQcAEMR/AxAXGn3mLzPbAAAAAElFTkSuQmCC"},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"#### 4.2.1. Evaluating the performance of the RPART Decision Tree"},{"metadata":{"trusted":false},"cell_type":"code","source":"rpartEVAL<-RpartEVAL(DATAforDT,num.folds=10,First=First,Second=Second)","execution_count":22,"outputs":[{"output_type":"stream","text":"Fold 1 of 10\n\nFold 2 of 10\n\nFold 3 of 10\n\nFold 4 of 10\n\nFold 5 of 10\n\nFold 6 of 10\n\nFold 7 of 10\n\nFold 8 of 10\n\nFold 9 of 10\n\nFold 10 of 10\n\n","name":"stderr"},{"output_type":"stream","text":"TP FN FP TN \n 0  7  5  3 \n             CL2 CL3\nPredictedCL2   0   5\nPredictedCL3   7   3\n","name":"stdout"},{"output_type":"stream","text":"Rpart SN: 0\nRpart SP: 0.38\nRpart ACC: 0.2\nRpart MCC: -0.66\n\n\n","name":"stderr"}]},{"metadata":{},"cell_type":"markdown","source":"### 4.3. Networking Analysis"},{"metadata":{"trusted":false},"cell_type":"code","source":"DEGs=\"UP-regulated-cl2\"\nFileName=paste0(DEGs)\ndata<-ClustDiff[[1]] [1:120,2]             \nppi<-PPI(data,FileName,species = \"10090\")   ###### The Taxonomy ID of Mus musculus: 10090, it can be obtained from \"https://www.ncbi.nlm.nih.gov/taxonomy\"\nppi\nnetworking<-NetAnalysis(ppi)\nnetworking                            ##### In case the Examine response components = 200 and an error \"linkmat[i, ]\" appeared, that means there are no PPI.","execution_count":23,"outputs":[{"output_type":"stream","text":"Examine response components = 200\t(200 means successful)\n\n\n\n\u001b[36m──\u001b[39m \u001b[1m\u001b[1mColumn specification\u001b[1m\u001b[22m \u001b[36m────────────────────────────────────────────────────────\u001b[39m\ncols(\n  stringId_A = \u001b[31mcol_character()\u001b[39m,\n  stringId_B = \u001b[31mcol_character()\u001b[39m,\n  preferredName_A = \u001b[31mcol_character()\u001b[39m,\n  preferredName_B = \u001b[31mcol_character()\u001b[39m,\n  ncbiTaxonId = \u001b[32mcol_double()\u001b[39m,\n  score = \u001b[32mcol_double()\u001b[39m,\n  nscore = \u001b[32mcol_double()\u001b[39m,\n  fscore = \u001b[32mcol_double()\u001b[39m,\n  pscore = \u001b[32mcol_double()\u001b[39m,\n  ascore = \u001b[32mcol_double()\u001b[39m,\n  escore = \u001b[32mcol_double()\u001b[39m,\n  dscore = \u001b[32mcol_double()\u001b[39m,\n  tscore = \u001b[32mcol_double()\u001b[39m\n)\n\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A data.frame: 170 × 13</caption>\n<thead>\n\t<tr><th></th><th scope=col>stringId_A</th><th scope=col>stringId_B</th><th scope=col>preferredName_A</th><th scope=col>preferredName_B</th><th scope=col>ncbiTaxonId</th><th scope=col>score</th><th scope=col>nscore</th><th scope=col>fscore</th><th scope=col>pscore</th><th scope=col>ascore</th><th scope=col>escore</th><th scope=col>dscore</th><th scope=col>tscore</th></tr>\n\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>1</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000085906</td><td>Gnai3</td><td>Fmr1   </td><td>10090</td><td>0.411</td><td>0</td><td>0</td><td>0</td><td>0.144</td><td>0.000</td><td>0.0</td><td>0.340</td></tr>\n\t<tr><th scope=row>2</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000085906</td><td>Gnai3</td><td>Fmr1   </td><td>10090</td><td>0.411</td><td>0</td><td>0</td><td>0</td><td>0.144</td><td>0.000</td><td>0.0</td><td>0.340</td></tr>\n\t<tr><th scope=row>3</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000000153</td><td>Gnai3</td><td>Gna12  </td><td>10090</td><td>0.906</td><td>0</td><td>0</td><td>0</td><td>0.060</td><td>0.050</td><td>0.9</td><td>0.445</td></tr>\n\t<tr><th scope=row>4</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000000153</td><td>Gnai3</td><td>Gna12  </td><td>10090</td><td>0.906</td><td>0</td><td>0</td><td>0</td><td>0.060</td><td>0.050</td><td>0.9</td><td>0.445</td></tr>\n\t<tr><th scope=row>5</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000101236</td><td>Gnai3</td><td>Oprm1  </td><td>10090</td><td>0.917</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.196</td><td>0.9</td><td>0.059</td></tr>\n\t<tr><th scope=row>6</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000101236</td><td>Gnai3</td><td>Oprm1  </td><td>10090</td><td>0.917</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.196</td><td>0.9</td><td>0.059</td></tr>\n\t<tr><th scope=row>7</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000000574</td><td>Gnai3</td><td>Gpcr2  </td><td>10090</td><td>0.928</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.251</td><td>0.9</td><td>0.124</td></tr>\n\t<tr><th scope=row>8</th><td>10090.ENSMUSP00000000001</td><td>10090.ENSMUSP00000000574</td><td>Gnai3</td><td>Gpcr2  </td><td>10090</td><td>0.928</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.251</td><td>0.9</td><td>0.124</td></tr>\n\t<tr><th scope=row>9</th><td>10090.ENSMUSP00000000028</td><td>10090.ENSMUSP00000001258</td><td>Cdc45</td><td>Uhrf1  </td><td>10090</td><td>0.698</td><td>0</td><td>0</td><td>0</td><td>0.642</td><td>0.000</td><td>0.0</td><td>0.190</td></tr>\n\t<tr><th scope=row>10</th><td>10090.ENSMUSP00000000028</td><td>10090.ENSMUSP00000001258</td><td>Cdc45</td><td>Uhrf1  </td><td>10090</td><td>0.698</td><td>0</td><td>0</td><td>0</td><td>0.642</td><td>0.000</td><td>0.0</td><td>0.190</td></tr>\n\t<tr><th scope=row>11</th><td>10090.ENSMUSP00000000028</td><td>10090.ENSMUSP00000000767</td><td>Cdc45</td><td>Rpa1   </td><td>10090</td><td>0.983</td><td>0</td><td>0</td><td>0</td><td>0.527</td><td>0.262</td><td>0.9</td><td>0.588</td></tr>\n\t<tr><th scope=row>12</th><td>10090.ENSMUSP00000000028</td><td>10090.ENSMUSP00000000767</td><td>Cdc45</td><td>Rpa1   </td><td>10090</td><td>0.983</td><td>0</td><td>0</td><td>0</td><td>0.527</td><td>0.262</td><td>0.9</td><td>0.588</td></tr>\n\t<tr><th scope=row>13</th><td>10090.ENSMUSP00000000058</td><td>10090.ENSMUSP00000001327</td><td>Cav2 </td><td>Itgb7  </td><td>10090</td><td>0.600</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.6</td><td>0.000</td></tr>\n\t<tr><th scope=row>14</th><td>10090.ENSMUSP00000000058</td><td>10090.ENSMUSP00000001327</td><td>Cav2 </td><td>Itgb7  </td><td>10090</td><td>0.600</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.6</td><td>0.000</td></tr>\n\t<tr><th scope=row>15</th><td>10090.ENSMUSP00000000058</td><td>10090.ENSMUSP00000105709</td><td>Cav2 </td><td>Calm1  </td><td>10090</td><td>0.843</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.050</td><td>0.8</td><td>0.240</td></tr>\n\t<tr><th scope=row>16</th><td>10090.ENSMUSP00000000058</td><td>10090.ENSMUSP00000105709</td><td>Cav2 </td><td>Calm1  </td><td>10090</td><td>0.843</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.050</td><td>0.8</td><td>0.240</td></tr>\n\t<tr><th scope=row>17</th><td>10090.ENSMUSP00000000080</td><td>10090.ENSMUSP00000000450</td><td>Klf6 </td><td>Pparg  </td><td>10090</td><td>0.432</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.102</td><td>0.0</td><td>0.393</td></tr>\n\t<tr><th scope=row>18</th><td>10090.ENSMUSP00000000080</td><td>10090.ENSMUSP00000000450</td><td>Klf6 </td><td>Pparg  </td><td>10090</td><td>0.432</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.102</td><td>0.0</td><td>0.393</td></tr>\n\t<tr><th scope=row>19</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000101032</td><td>Ngfr </td><td>Icosl  </td><td>10090</td><td>0.419</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.0</td><td>0.419</td></tr>\n\t<tr><th scope=row>20</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000101032</td><td>Ngfr </td><td>Icosl  </td><td>10090</td><td>0.419</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.0</td><td>0.419</td></tr>\n\t<tr><th scope=row>21</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000000187</td><td>Ngfr </td><td>Fgf6   </td><td>10090</td><td>0.646</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.6</td><td>0.152</td></tr>\n\t<tr><th scope=row>22</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000000187</td><td>Ngfr </td><td>Fgf6   </td><td>10090</td><td>0.646</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.6</td><td>0.152</td></tr>\n\t<tr><th scope=row>23</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000000500</td><td>Ngfr </td><td>Pdgfb  </td><td>10090</td><td>0.656</td><td>0</td><td>0</td><td>0</td><td>0.060</td><td>0.000</td><td>0.6</td><td>0.160</td></tr>\n\t<tr><th scope=row>24</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000000500</td><td>Ngfr </td><td>Pdgfb  </td><td>10090</td><td>0.656</td><td>0</td><td>0</td><td>0</td><td>0.060</td><td>0.000</td><td>0.6</td><td>0.160</td></tr>\n\t<tr><th scope=row>25</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000105709</td><td>Ngfr </td><td>Calm1  </td><td>10090</td><td>0.814</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.806</td><td>0.0</td><td>0.083</td></tr>\n\t<tr><th scope=row>26</th><td>10090.ENSMUSP00000000122</td><td>10090.ENSMUSP00000105709</td><td>Ngfr </td><td>Calm1  </td><td>10090</td><td>0.814</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.806</td><td>0.0</td><td>0.083</td></tr>\n\t<tr><th scope=row>27</th><td>10090.ENSMUSP00000000153</td><td>10090.ENSMUSP00000000776</td><td>Gna12</td><td>Tubgcp3</td><td>10090</td><td>0.613</td><td>0</td><td>0</td><td>0</td><td>0.062</td><td>0.000</td><td>0.0</td><td>0.605</td></tr>\n\t<tr><th scope=row>28</th><td>10090.ENSMUSP00000000153</td><td>10090.ENSMUSP00000000776</td><td>Gna12</td><td>Tubgcp3</td><td>10090</td><td>0.613</td><td>0</td><td>0</td><td>0</td><td>0.062</td><td>0.000</td><td>0.0</td><td>0.605</td></tr>\n\t<tr><th scope=row>29</th><td>10090.ENSMUSP00000000175</td><td>10090.ENSMUSP00000000349</td><td>Sdhd </td><td>Dbt    </td><td>10090</td><td>0.411</td><td>0</td><td>0</td><td>0</td><td>0.162</td><td>0.000</td><td>0.0</td><td>0.327</td></tr>\n\t<tr><th scope=row>30</th><td>10090.ENSMUSP00000000175</td><td>10090.ENSMUSP00000000349</td><td>Sdhd </td><td>Dbt    </td><td>10090</td><td>0.411</td><td>0</td><td>0</td><td>0</td><td>0.162</td><td>0.000</td><td>0.0</td><td>0.327</td></tr>\n\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n\t<tr><th scope=row>141</th><td>10090.ENSMUSP00000001156</td><td>10090.ENSMUSP00000119552</td><td>Cfp    </td><td>Spg7   </td><td>10090</td><td>0.516</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.00</td><td>0.516</td></tr>\n\t<tr><th scope=row>142</th><td>10090.ENSMUSP00000001156</td><td>10090.ENSMUSP00000119552</td><td>Cfp    </td><td>Spg7   </td><td>10090</td><td>0.516</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.00</td><td>0.516</td></tr>\n\t<tr><th scope=row>143</th><td>10090.ENSMUSP00000001179</td><td>10090.ENSMUSP00000118015</td><td>Pcnt   </td><td>Ift20  </td><td>10090</td><td>0.476</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.00</td><td>0.476</td></tr>\n\t<tr><th scope=row>144</th><td>10090.ENSMUSP00000001179</td><td>10090.ENSMUSP00000118015</td><td>Pcnt   </td><td>Ift20  </td><td>10090</td><td>0.476</td><td>0</td><td>0</td><td>0</td><td>0.000</td><td>0.000</td><td>0.00</td><td>0.476</td></tr>\n\t<tr><th scope=row>145</th><td>10090.ENSMUSP00000001186</td><td>10090.ENSMUSP00000132735</td><td>Snrnp27</td><td>Hnrnpd </td><td>10090</td><td>0.902</td><td>0</td><td>0</td><td>0</td><td>0.061</td><td>0.000</td><td>0.90</td><td>0.000</td></tr>\n\t<tr><th scope=row>146</th><td>10090.ENSMUSP00000001186</td><td>10090.ENSMUSP00000132735</td><td>Snrnp27</td><td>Hnrnpd </td><td>10090</td><td>0.902</td><td>0</td><td>0</td><td>0</td><td>0.061</td><td>0.000</td><td>0.90</td><td>0.000</td></tr>\n\t<tr><th scope=row>147</th><td>10090.ENSMUSP00000001254</td><td>10090.ENSMUSP00000099483</td><td>Slc26a3</td><td>Car4   </td><td>10090</td><td>0.422</td><td>0</td><td>0</td><td>0</td><td>0.060</td><td>0.000</td><td>0.00</td><td>0.410</td></tr>\n\t<tr><th scope=row>148</th><td>10090.ENSMUSP00000001254</td><td>10090.ENSMUSP00000099483</td><td>Slc26a3</td><td>Car4   </td><td>10090</td><td>0.422</td><td>0</td><td>0</td><td>0</td><td>0.060</td><td>0.000</td><td>0.00</td><td>0.410</td></tr>\n\t<tr><th scope=row>149</th><td>10090.ENSMUSP00000001258</td><td>10090.ENSMUSP00000121562</td><td>Uhrf1  </td><td>Dnmt3l </td><td>10090</td><td>0.707</td><td>0</td><td>0</td><td>0</td><td>0.069</td><td>0.000</td><td>0.00</td><td>0.699</td></tr>\n\t<tr><th scope=row>150</th><td>10090.ENSMUSP00000001258</td><td>10090.ENSMUSP00000121562</td><td>Uhrf1  </td><td>Dnmt3l </td><td>10090</td><td>0.707</td><td>0</td><td>0</td><td>0</td><td>0.069</td><td>0.000</td><td>0.00</td><td>0.699</td></tr>\n\t<tr><th scope=row>151</th><td>10090.ENSMUSP00000001327</td><td>10090.ENSMUSP00000009396</td><td>Itgb7  </td><td>Tspan32</td><td>10090</td><td>0.749</td><td>0</td><td>0</td><td>0</td><td>0.141</td><td>0.000</td><td>0.72</td><td>0.000</td></tr>\n\t<tr><th scope=row>152</th><td>10090.ENSMUSP00000001327</td><td>10090.ENSMUSP00000009396</td><td>Itgb7  </td><td>Tspan32</td><td>10090</td><td>0.749</td><td>0</td><td>0</td><td>0</td><td>0.141</td><td>0.000</td><td>0.72</td><td>0.000</td></tr>\n\t<tr><th scope=row>153</th><td>10090.ENSMUSP00000001327</td><td>10090.ENSMUSP00000023128</td><td>Itgb7  </td><td>Itga5  </td><td>10090</td><td>0.936</td><td>0</td><td>0</td><td>0</td><td>0.077</td><td>0.360</td><td>0.80</td><td>0.523</td></tr>\n\t<tr><th scope=row>154</th><td>10090.ENSMUSP00000001327</td><td>10090.ENSMUSP00000023128</td><td>Itgb7  </td><td>Itga5  </td><td>10090</td><td>0.936</td><td>0</td><td>0</td><td>0</td><td>0.077</td><td>0.360</td><td>0.80</td><td>0.523</td></tr>\n\t<tr><th scope=row>155</th><td>10090.ENSMUSP00000009396</td><td>10090.ENSMUSP00000023128</td><td>Tspan32</td><td>Itga5  </td><td>10090</td><td>0.722</td><td>0</td><td>0</td><td>0</td><td>0.050</td><td>0.000</td><td>0.72</td><td>0.000</td></tr>\n\t<tr><th scope=row>156</th><td>10090.ENSMUSP00000009396</td><td>10090.ENSMUSP00000023128</td><td>Tspan32</td><td>Itga5  </td><td>10090</td><td>0.722</td><td>0</td><td>0</td><td>0</td><td>0.050</td><td>0.000</td><td>0.72</td><td>0.000</td></tr>\n\t<tr><th scope=row>157</th><td>10090.ENSMUSP00000009530</td><td>10090.ENSMUSP00000023128</td><td>Timp1  </td><td>Itga5  </td><td>10090</td><td>0.436</td><td>0</td><td>0</td><td>0</td><td>0.175</td><td>0.059</td><td>0.00</td><td>0.332</td></tr>\n\t<tr><th scope=row>158</th><td>10090.ENSMUSP00000009530</td><td>10090.ENSMUSP00000023128</td><td>Timp1  </td><td>Itga5  </td><td>10090</td><td>0.436</td><td>0</td><td>0</td><td>0</td><td>0.175</td><td>0.059</td><td>0.00</td><td>0.332</td></tr>\n\t<tr><th scope=row>159</th><td>10090.ENSMUSP00000009530</td><td>10090.ENSMUSP00000087119</td><td>Timp1  </td><td>Mmp14  </td><td>10090</td><td>0.970</td><td>0</td><td>0</td><td>0</td><td>0.195</td><td>0.758</td><td>0.00</td><td>0.861</td></tr>\n\t<tr><th scope=row>160</th><td>10090.ENSMUSP00000009530</td><td>10090.ENSMUSP00000087119</td><td>Timp1  </td><td>Mmp14  </td><td>10090</td><td>0.970</td><td>0</td><td>0</td><td>0</td><td>0.195</td><td>0.758</td><td>0.00</td><td>0.861</td></tr>\n\t<tr><th scope=row>161</th><td>10090.ENSMUSP00000085523</td><td>10090.ENSMUSP00000113022</td><td>Ndufa9 </td><td>Atp5f1 </td><td>10090</td><td>0.912</td><td>0</td><td>0</td><td>0</td><td>0.845</td><td>0.179</td><td>0.00</td><td>0.369</td></tr>\n\t<tr><th scope=row>162</th><td>10090.ENSMUSP00000085523</td><td>10090.ENSMUSP00000113022</td><td>Ndufa9 </td><td>Atp5f1 </td><td>10090</td><td>0.912</td><td>0</td><td>0</td><td>0</td><td>0.845</td><td>0.179</td><td>0.00</td><td>0.369</td></tr>\n\t<tr><th scope=row>163</th><td>10090.ENSMUSP00000103528</td><td>10090.ENSMUSP00000109397</td><td>Trim25 </td><td>Mx1    </td><td>10090</td><td>0.950</td><td>0</td><td>0</td><td>0</td><td>0.153</td><td>0.049</td><td>0.90</td><td>0.461</td></tr>\n\t<tr><th scope=row>164</th><td>10090.ENSMUSP00000103528</td><td>10090.ENSMUSP00000109397</td><td>Trim25 </td><td>Mx1    </td><td>10090</td><td>0.950</td><td>0</td><td>0</td><td>0</td><td>0.153</td><td>0.049</td><td>0.90</td><td>0.461</td></tr>\n\t<tr><th scope=row>165</th><td>10090.ENSMUSP00000103710</td><td>10090.ENSMUSP00000125960</td><td>Usp32  </td><td>Mcm3ap </td><td>10090</td><td>0.491</td><td>0</td><td>0</td><td>0</td><td>0.080</td><td>0.456</td><td>0.00</td><td>0.066</td></tr>\n\t<tr><th scope=row>166</th><td>10090.ENSMUSP00000103710</td><td>10090.ENSMUSP00000125960</td><td>Usp32  </td><td>Mcm3ap </td><td>10090</td><td>0.491</td><td>0</td><td>0</td><td>0</td><td>0.080</td><td>0.456</td><td>0.00</td><td>0.066</td></tr>\n\t<tr><th scope=row>167</th><td>10090.ENSMUSP00000109312</td><td>10090.ENSMUSP00000120014</td><td>Myg1   </td><td>Nhp2   </td><td>10090</td><td>0.662</td><td>0</td><td>0</td><td>0</td><td>0.662</td><td>0.000</td><td>0.00</td><td>0.000</td></tr>\n\t<tr><th scope=row>168</th><td>10090.ENSMUSP00000109312</td><td>10090.ENSMUSP00000120014</td><td>Myg1   </td><td>Nhp2   </td><td>10090</td><td>0.662</td><td>0</td><td>0</td><td>0</td><td>0.662</td><td>0.000</td><td>0.00</td><td>0.000</td></tr>\n\t<tr><th scope=row>169</th><td>10090.ENSMUSP00000119552</td><td>10090.ENSMUSP00000120014</td><td>Spg7   </td><td>Nhp2   </td><td>10090</td><td>0.425</td><td>0</td><td>0</td><td>0</td><td>0.061</td><td>0.000</td><td>0.00</td><td>0.413</td></tr>\n\t<tr><th scope=row>170</th><td>10090.ENSMUSP00000119552</td><td>10090.ENSMUSP00000120014</td><td>Spg7   </td><td>Nhp2   </td><td>10090</td><td>0.425</td><td>0</td><td>0</td><td>0</td><td>0.061</td><td>0.000</td><td>0.00</td><td>0.413</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 170 × 13\n\n| <!--/--> | stringId_A &lt;chr&gt; | stringId_B &lt;chr&gt; | preferredName_A &lt;chr&gt; | preferredName_B &lt;chr&gt; | ncbiTaxonId &lt;int&gt; | score &lt;dbl&gt; | nscore &lt;int&gt; | fscore &lt;int&gt; | pscore &lt;dbl&gt; | ascore &lt;dbl&gt; | escore &lt;dbl&gt; | dscore &lt;dbl&gt; | tscore &lt;dbl&gt; |\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n| 1 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000085906 | Gnai3 | Fmr1    | 10090 | 0.411 | 0 | 0 | 0 | 0.144 | 0.000 | 0.0 | 0.340 |\n| 2 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000085906 | Gnai3 | Fmr1    | 10090 | 0.411 | 0 | 0 | 0 | 0.144 | 0.000 | 0.0 | 0.340 |\n| 3 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000000153 | Gnai3 | Gna12   | 10090 | 0.906 | 0 | 0 | 0 | 0.060 | 0.050 | 0.9 | 0.445 |\n| 4 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000000153 | Gnai3 | Gna12   | 10090 | 0.906 | 0 | 0 | 0 | 0.060 | 0.050 | 0.9 | 0.445 |\n| 5 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000101236 | Gnai3 | Oprm1   | 10090 | 0.917 | 0 | 0 | 0 | 0.000 | 0.196 | 0.9 | 0.059 |\n| 6 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000101236 | Gnai3 | Oprm1   | 10090 | 0.917 | 0 | 0 | 0 | 0.000 | 0.196 | 0.9 | 0.059 |\n| 7 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000000574 | Gnai3 | Gpcr2   | 10090 | 0.928 | 0 | 0 | 0 | 0.000 | 0.251 | 0.9 | 0.124 |\n| 8 | 10090.ENSMUSP00000000001 | 10090.ENSMUSP00000000574 | Gnai3 | Gpcr2   | 10090 | 0.928 | 0 | 0 | 0 | 0.000 | 0.251 | 0.9 | 0.124 |\n| 9 | 10090.ENSMUSP00000000028 | 10090.ENSMUSP00000001258 | Cdc45 | Uhrf1   | 10090 | 0.698 | 0 | 0 | 0 | 0.642 | 0.000 | 0.0 | 0.190 |\n| 10 | 10090.ENSMUSP00000000028 | 10090.ENSMUSP00000001258 | Cdc45 | Uhrf1   | 10090 | 0.698 | 0 | 0 | 0 | 0.642 | 0.000 | 0.0 | 0.190 |\n| 11 | 10090.ENSMUSP00000000028 | 10090.ENSMUSP00000000767 | Cdc45 | Rpa1    | 10090 | 0.983 | 0 | 0 | 0 | 0.527 | 0.262 | 0.9 | 0.588 |\n| 12 | 10090.ENSMUSP00000000028 | 10090.ENSMUSP00000000767 | Cdc45 | Rpa1    | 10090 | 0.983 | 0 | 0 | 0 | 0.527 | 0.262 | 0.9 | 0.588 |\n| 13 | 10090.ENSMUSP00000000058 | 10090.ENSMUSP00000001327 | Cav2  | Itgb7   | 10090 | 0.600 | 0 | 0 | 0 | 0.000 | 0.000 | 0.6 | 0.000 |\n| 14 | 10090.ENSMUSP00000000058 | 10090.ENSMUSP00000001327 | Cav2  | Itgb7   | 10090 | 0.600 | 0 | 0 | 0 | 0.000 | 0.000 | 0.6 | 0.000 |\n| 15 | 10090.ENSMUSP00000000058 | 10090.ENSMUSP00000105709 | Cav2  | Calm1   | 10090 | 0.843 | 0 | 0 | 0 | 0.000 | 0.050 | 0.8 | 0.240 |\n| 16 | 10090.ENSMUSP00000000058 | 10090.ENSMUSP00000105709 | Cav2  | Calm1   | 10090 | 0.843 | 0 | 0 | 0 | 0.000 | 0.050 | 0.8 | 0.240 |\n| 17 | 10090.ENSMUSP00000000080 | 10090.ENSMUSP00000000450 | Klf6  | Pparg   | 10090 | 0.432 | 0 | 0 | 0 | 0.000 | 0.102 | 0.0 | 0.393 |\n| 18 | 10090.ENSMUSP00000000080 | 10090.ENSMUSP00000000450 | Klf6  | Pparg   | 10090 | 0.432 | 0 | 0 | 0 | 0.000 | 0.102 | 0.0 | 0.393 |\n| 19 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000101032 | Ngfr  | Icosl   | 10090 | 0.419 | 0 | 0 | 0 | 0.000 | 0.000 | 0.0 | 0.419 |\n| 20 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000101032 | Ngfr  | Icosl   | 10090 | 0.419 | 0 | 0 | 0 | 0.000 | 0.000 | 0.0 | 0.419 |\n| 21 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000000187 | Ngfr  | Fgf6    | 10090 | 0.646 | 0 | 0 | 0 | 0.000 | 0.000 | 0.6 | 0.152 |\n| 22 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000000187 | Ngfr  | Fgf6    | 10090 | 0.646 | 0 | 0 | 0 | 0.000 | 0.000 | 0.6 | 0.152 |\n| 23 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000000500 | Ngfr  | Pdgfb   | 10090 | 0.656 | 0 | 0 | 0 | 0.060 | 0.000 | 0.6 | 0.160 |\n| 24 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000000500 | Ngfr  | Pdgfb   | 10090 | 0.656 | 0 | 0 | 0 | 0.060 | 0.000 | 0.6 | 0.160 |\n| 25 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000105709 | Ngfr  | Calm1   | 10090 | 0.814 | 0 | 0 | 0 | 0.000 | 0.806 | 0.0 | 0.083 |\n| 26 | 10090.ENSMUSP00000000122 | 10090.ENSMUSP00000105709 | Ngfr  | Calm1   | 10090 | 0.814 | 0 | 0 | 0 | 0.000 | 0.806 | 0.0 | 0.083 |\n| 27 | 10090.ENSMUSP00000000153 | 10090.ENSMUSP00000000776 | Gna12 | Tubgcp3 | 10090 | 0.613 | 0 | 0 | 0 | 0.062 | 0.000 | 0.0 | 0.605 |\n| 28 | 10090.ENSMUSP00000000153 | 10090.ENSMUSP00000000776 | Gna12 | Tubgcp3 | 10090 | 0.613 | 0 | 0 | 0 | 0.062 | 0.000 | 0.0 | 0.605 |\n| 29 | 10090.ENSMUSP00000000175 | 10090.ENSMUSP00000000349 | Sdhd  | Dbt     | 10090 | 0.411 | 0 | 0 | 0 | 0.162 | 0.000 | 0.0 | 0.327 |\n| 30 | 10090.ENSMUSP00000000175 | 10090.ENSMUSP00000000349 | Sdhd  | Dbt     | 10090 | 0.411 | 0 | 0 | 0 | 0.162 | 0.000 | 0.0 | 0.327 |\n| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n| 141 | 10090.ENSMUSP00000001156 | 10090.ENSMUSP00000119552 | Cfp     | Spg7    | 10090 | 0.516 | 0 | 0 | 0 | 0.000 | 0.000 | 0.00 | 0.516 |\n| 142 | 10090.ENSMUSP00000001156 | 10090.ENSMUSP00000119552 | Cfp     | Spg7    | 10090 | 0.516 | 0 | 0 | 0 | 0.000 | 0.000 | 0.00 | 0.516 |\n| 143 | 10090.ENSMUSP00000001179 | 10090.ENSMUSP00000118015 | Pcnt    | Ift20   | 10090 | 0.476 | 0 | 0 | 0 | 0.000 | 0.000 | 0.00 | 0.476 |\n| 144 | 10090.ENSMUSP00000001179 | 10090.ENSMUSP00000118015 | Pcnt    | Ift20   | 10090 | 0.476 | 0 | 0 | 0 | 0.000 | 0.000 | 0.00 | 0.476 |\n| 145 | 10090.ENSMUSP00000001186 | 10090.ENSMUSP00000132735 | Snrnp27 | Hnrnpd  | 10090 | 0.902 | 0 | 0 | 0 | 0.061 | 0.000 | 0.90 | 0.000 |\n| 146 | 10090.ENSMUSP00000001186 | 10090.ENSMUSP00000132735 | Snrnp27 | Hnrnpd  | 10090 | 0.902 | 0 | 0 | 0 | 0.061 | 0.000 | 0.90 | 0.000 |\n| 147 | 10090.ENSMUSP00000001254 | 10090.ENSMUSP00000099483 | Slc26a3 | Car4    | 10090 | 0.422 | 0 | 0 | 0 | 0.060 | 0.000 | 0.00 | 0.410 |\n| 148 | 10090.ENSMUSP00000001254 | 10090.ENSMUSP00000099483 | Slc26a3 | Car4    | 10090 | 0.422 | 0 | 0 | 0 | 0.060 | 0.000 | 0.00 | 0.410 |\n| 149 | 10090.ENSMUSP00000001258 | 10090.ENSMUSP00000121562 | Uhrf1   | Dnmt3l  | 10090 | 0.707 | 0 | 0 | 0 | 0.069 | 0.000 | 0.00 | 0.699 |\n| 150 | 10090.ENSMUSP00000001258 | 10090.ENSMUSP00000121562 | Uhrf1   | Dnmt3l  | 10090 | 0.707 | 0 | 0 | 0 | 0.069 | 0.000 | 0.00 | 0.699 |\n| 151 | 10090.ENSMUSP00000001327 | 10090.ENSMUSP00000009396 | Itgb7   | Tspan32 | 10090 | 0.749 | 0 | 0 | 0 | 0.141 | 0.000 | 0.72 | 0.000 |\n| 152 | 10090.ENSMUSP00000001327 | 10090.ENSMUSP00000009396 | Itgb7   | Tspan32 | 10090 | 0.749 | 0 | 0 | 0 | 0.141 | 0.000 | 0.72 | 0.000 |\n| 153 | 10090.ENSMUSP00000001327 | 10090.ENSMUSP00000023128 | Itgb7   | Itga5   | 10090 | 0.936 | 0 | 0 | 0 | 0.077 | 0.360 | 0.80 | 0.523 |\n| 154 | 10090.ENSMUSP00000001327 | 10090.ENSMUSP00000023128 | Itgb7   | Itga5   | 10090 | 0.936 | 0 | 0 | 0 | 0.077 | 0.360 | 0.80 | 0.523 |\n| 155 | 10090.ENSMUSP00000009396 | 10090.ENSMUSP00000023128 | Tspan32 | Itga5   | 10090 | 0.722 | 0 | 0 | 0 | 0.050 | 0.000 | 0.72 | 0.000 |\n| 156 | 10090.ENSMUSP00000009396 | 10090.ENSMUSP00000023128 | Tspan32 | Itga5   | 10090 | 0.722 | 0 | 0 | 0 | 0.050 | 0.000 | 0.72 | 0.000 |\n| 157 | 10090.ENSMUSP00000009530 | 10090.ENSMUSP00000023128 | Timp1   | Itga5   | 10090 | 0.436 | 0 | 0 | 0 | 0.175 | 0.059 | 0.00 | 0.332 |\n| 158 | 10090.ENSMUSP00000009530 | 10090.ENSMUSP00000023128 | Timp1   | Itga5   | 10090 | 0.436 | 0 | 0 | 0 | 0.175 | 0.059 | 0.00 | 0.332 |\n| 159 | 10090.ENSMUSP00000009530 | 10090.ENSMUSP00000087119 | Timp1   | Mmp14   | 10090 | 0.970 | 0 | 0 | 0 | 0.195 | 0.758 | 0.00 | 0.861 |\n| 160 | 10090.ENSMUSP00000009530 | 10090.ENSMUSP00000087119 | Timp1   | Mmp14   | 10090 | 0.970 | 0 | 0 | 0 | 0.195 | 0.758 | 0.00 | 0.861 |\n| 161 | 10090.ENSMUSP00000085523 | 10090.ENSMUSP00000113022 | Ndufa9  | Atp5f1  | 10090 | 0.912 | 0 | 0 | 0 | 0.845 | 0.179 | 0.00 | 0.369 |\n| 162 | 10090.ENSMUSP00000085523 | 10090.ENSMUSP00000113022 | Ndufa9  | Atp5f1  | 10090 | 0.912 | 0 | 0 | 0 | 0.845 | 0.179 | 0.00 | 0.369 |\n| 163 | 10090.ENSMUSP00000103528 | 10090.ENSMUSP00000109397 | Trim25  | Mx1     | 10090 | 0.950 | 0 | 0 | 0 | 0.153 | 0.049 | 0.90 | 0.461 |\n| 164 | 10090.ENSMUSP00000103528 | 10090.ENSMUSP00000109397 | Trim25  | Mx1     | 10090 | 0.950 | 0 | 0 | 0 | 0.153 | 0.049 | 0.90 | 0.461 |\n| 165 | 10090.ENSMUSP00000103710 | 10090.ENSMUSP00000125960 | Usp32   | Mcm3ap  | 10090 | 0.491 | 0 | 0 | 0 | 0.080 | 0.456 | 0.00 | 0.066 |\n| 166 | 10090.ENSMUSP00000103710 | 10090.ENSMUSP00000125960 | Usp32   | Mcm3ap  | 10090 | 0.491 | 0 | 0 | 0 | 0.080 | 0.456 | 0.00 | 0.066 |\n| 167 | 10090.ENSMUSP00000109312 | 10090.ENSMUSP00000120014 | Myg1    | Nhp2    | 10090 | 0.662 | 0 | 0 | 0 | 0.662 | 0.000 | 0.00 | 0.000 |\n| 168 | 10090.ENSMUSP00000109312 | 10090.ENSMUSP00000120014 | Myg1    | Nhp2    | 10090 | 0.662 | 0 | 0 | 0 | 0.662 | 0.000 | 0.00 | 0.000 |\n| 169 | 10090.ENSMUSP00000119552 | 10090.ENSMUSP00000120014 | Spg7    | Nhp2    | 10090 | 0.425 | 0 | 0 | 0 | 0.061 | 0.000 | 0.00 | 0.413 |\n| 170 | 10090.ENSMUSP00000119552 | 10090.ENSMUSP00000120014 | Spg7    | Nhp2    | 10090 | 0.425 | 0 | 0 | 0 | 0.061 | 0.000 | 0.00 | 0.413 |\n\n","text/latex":"A data.frame: 170 × 13\n\\begin{tabular}{r|lllllllllllll}\n  & stringId\\_A & stringId\\_B & preferredName\\_A & preferredName\\_B & ncbiTaxonId & score & nscore & fscore & pscore & ascore & escore & dscore & tscore\\\\\n  & <chr> & <chr> & <chr> & <chr> & <int> & <dbl> & <int> & <int> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n\\hline\n\t1 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000085906 & Gnai3 & Fmr1    & 10090 & 0.411 & 0 & 0 & 0 & 0.144 & 0.000 & 0.0 & 0.340\\\\\n\t2 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000085906 & Gnai3 & Fmr1    & 10090 & 0.411 & 0 & 0 & 0 & 0.144 & 0.000 & 0.0 & 0.340\\\\\n\t3 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000000153 & Gnai3 & Gna12   & 10090 & 0.906 & 0 & 0 & 0 & 0.060 & 0.050 & 0.9 & 0.445\\\\\n\t4 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000000153 & Gnai3 & Gna12   & 10090 & 0.906 & 0 & 0 & 0 & 0.060 & 0.050 & 0.9 & 0.445\\\\\n\t5 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000101236 & Gnai3 & Oprm1   & 10090 & 0.917 & 0 & 0 & 0 & 0.000 & 0.196 & 0.9 & 0.059\\\\\n\t6 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000101236 & Gnai3 & Oprm1   & 10090 & 0.917 & 0 & 0 & 0 & 0.000 & 0.196 & 0.9 & 0.059\\\\\n\t7 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000000574 & Gnai3 & Gpcr2   & 10090 & 0.928 & 0 & 0 & 0 & 0.000 & 0.251 & 0.9 & 0.124\\\\\n\t8 & 10090.ENSMUSP00000000001 & 10090.ENSMUSP00000000574 & Gnai3 & Gpcr2   & 10090 & 0.928 & 0 & 0 & 0 & 0.000 & 0.251 & 0.9 & 0.124\\\\\n\t9 & 10090.ENSMUSP00000000028 & 10090.ENSMUSP00000001258 & Cdc45 & Uhrf1   & 10090 & 0.698 & 0 & 0 & 0 & 0.642 & 0.000 & 0.0 & 0.190\\\\\n\t10 & 10090.ENSMUSP00000000028 & 10090.ENSMUSP00000001258 & Cdc45 & Uhrf1   & 10090 & 0.698 & 0 & 0 & 0 & 0.642 & 0.000 & 0.0 & 0.190\\\\\n\t11 & 10090.ENSMUSP00000000028 & 10090.ENSMUSP00000000767 & Cdc45 & Rpa1    & 10090 & 0.983 & 0 & 0 & 0 & 0.527 & 0.262 & 0.9 & 0.588\\\\\n\t12 & 10090.ENSMUSP00000000028 & 10090.ENSMUSP00000000767 & Cdc45 & Rpa1    & 10090 & 0.983 & 0 & 0 & 0 & 0.527 & 0.262 & 0.9 & 0.588\\\\\n\t13 & 10090.ENSMUSP00000000058 & 10090.ENSMUSP00000001327 & Cav2  & Itgb7   & 10090 & 0.600 & 0 & 0 & 0 & 0.000 & 0.000 & 0.6 & 0.000\\\\\n\t14 & 10090.ENSMUSP00000000058 & 10090.ENSMUSP00000001327 & Cav2  & Itgb7   & 10090 & 0.600 & 0 & 0 & 0 & 0.000 & 0.000 & 0.6 & 0.000\\\\\n\t15 & 10090.ENSMUSP00000000058 & 10090.ENSMUSP00000105709 & Cav2  & Calm1   & 10090 & 0.843 & 0 & 0 & 0 & 0.000 & 0.050 & 0.8 & 0.240\\\\\n\t16 & 10090.ENSMUSP00000000058 & 10090.ENSMUSP00000105709 & Cav2  & Calm1   & 10090 & 0.843 & 0 & 0 & 0 & 0.000 & 0.050 & 0.8 & 0.240\\\\\n\t17 & 10090.ENSMUSP00000000080 & 10090.ENSMUSP00000000450 & Klf6  & Pparg   & 10090 & 0.432 & 0 & 0 & 0 & 0.000 & 0.102 & 0.0 & 0.393\\\\\n\t18 & 10090.ENSMUSP00000000080 & 10090.ENSMUSP00000000450 & Klf6  & Pparg   & 10090 & 0.432 & 0 & 0 & 0 & 0.000 & 0.102 & 0.0 & 0.393\\\\\n\t19 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000101032 & Ngfr  & Icosl   & 10090 & 0.419 & 0 & 0 & 0 & 0.000 & 0.000 & 0.0 & 0.419\\\\\n\t20 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000101032 & Ngfr  & Icosl   & 10090 & 0.419 & 0 & 0 & 0 & 0.000 & 0.000 & 0.0 & 0.419\\\\\n\t21 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000000187 & Ngfr  & Fgf6    & 10090 & 0.646 & 0 & 0 & 0 & 0.000 & 0.000 & 0.6 & 0.152\\\\\n\t22 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000000187 & Ngfr  & Fgf6    & 10090 & 0.646 & 0 & 0 & 0 & 0.000 & 0.000 & 0.6 & 0.152\\\\\n\t23 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000000500 & Ngfr  & Pdgfb   & 10090 & 0.656 & 0 & 0 & 0 & 0.060 & 0.000 & 0.6 & 0.160\\\\\n\t24 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000000500 & Ngfr  & Pdgfb   & 10090 & 0.656 & 0 & 0 & 0 & 0.060 & 0.000 & 0.6 & 0.160\\\\\n\t25 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000105709 & Ngfr  & Calm1   & 10090 & 0.814 & 0 & 0 & 0 & 0.000 & 0.806 & 0.0 & 0.083\\\\\n\t26 & 10090.ENSMUSP00000000122 & 10090.ENSMUSP00000105709 & Ngfr  & Calm1   & 10090 & 0.814 & 0 & 0 & 0 & 0.000 & 0.806 & 0.0 & 0.083\\\\\n\t27 & 10090.ENSMUSP00000000153 & 10090.ENSMUSP00000000776 & Gna12 & Tubgcp3 & 10090 & 0.613 & 0 & 0 & 0 & 0.062 & 0.000 & 0.0 & 0.605\\\\\n\t28 & 10090.ENSMUSP00000000153 & 10090.ENSMUSP00000000776 & Gna12 & Tubgcp3 & 10090 & 0.613 & 0 & 0 & 0 & 0.062 & 0.000 & 0.0 & 0.605\\\\\n\t29 & 10090.ENSMUSP00000000175 & 10090.ENSMUSP00000000349 & Sdhd  & Dbt     & 10090 & 0.411 & 0 & 0 & 0 & 0.162 & 0.000 & 0.0 & 0.327\\\\\n\t30 & 10090.ENSMUSP00000000175 & 10090.ENSMUSP00000000349 & Sdhd  & Dbt     & 10090 & 0.411 & 0 & 0 & 0 & 0.162 & 0.000 & 0.0 & 0.327\\\\\n\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t141 & 10090.ENSMUSP00000001156 & 10090.ENSMUSP00000119552 & Cfp     & Spg7    & 10090 & 0.516 & 0 & 0 & 0 & 0.000 & 0.000 & 0.00 & 0.516\\\\\n\t142 & 10090.ENSMUSP00000001156 & 10090.ENSMUSP00000119552 & Cfp     & Spg7    & 10090 & 0.516 & 0 & 0 & 0 & 0.000 & 0.000 & 0.00 & 0.516\\\\\n\t143 & 10090.ENSMUSP00000001179 & 10090.ENSMUSP00000118015 & Pcnt    & Ift20   & 10090 & 0.476 & 0 & 0 & 0 & 0.000 & 0.000 & 0.00 & 0.476\\\\\n\t144 & 10090.ENSMUSP00000001179 & 10090.ENSMUSP00000118015 & Pcnt    & Ift20   & 10090 & 0.476 & 0 & 0 & 0 & 0.000 & 0.000 & 0.00 & 0.476\\\\\n\t145 & 10090.ENSMUSP00000001186 & 10090.ENSMUSP00000132735 & Snrnp27 & Hnrnpd  & 10090 & 0.902 & 0 & 0 & 0 & 0.061 & 0.000 & 0.90 & 0.000\\\\\n\t146 & 10090.ENSMUSP00000001186 & 10090.ENSMUSP00000132735 & Snrnp27 & Hnrnpd  & 10090 & 0.902 & 0 & 0 & 0 & 0.061 & 0.000 & 0.90 & 0.000\\\\\n\t147 & 10090.ENSMUSP00000001254 & 10090.ENSMUSP00000099483 & Slc26a3 & Car4    & 10090 & 0.422 & 0 & 0 & 0 & 0.060 & 0.000 & 0.00 & 0.410\\\\\n\t148 & 10090.ENSMUSP00000001254 & 10090.ENSMUSP00000099483 & Slc26a3 & Car4    & 10090 & 0.422 & 0 & 0 & 0 & 0.060 & 0.000 & 0.00 & 0.410\\\\\n\t149 & 10090.ENSMUSP00000001258 & 10090.ENSMUSP00000121562 & Uhrf1   & Dnmt3l  & 10090 & 0.707 & 0 & 0 & 0 & 0.069 & 0.000 & 0.00 & 0.699\\\\\n\t150 & 10090.ENSMUSP00000001258 & 10090.ENSMUSP00000121562 & Uhrf1   & Dnmt3l  & 10090 & 0.707 & 0 & 0 & 0 & 0.069 & 0.000 & 0.00 & 0.699\\\\\n\t151 & 10090.ENSMUSP00000001327 & 10090.ENSMUSP00000009396 & Itgb7   & Tspan32 & 10090 & 0.749 & 0 & 0 & 0 & 0.141 & 0.000 & 0.72 & 0.000\\\\\n\t152 & 10090.ENSMUSP00000001327 & 10090.ENSMUSP00000009396 & Itgb7   & Tspan32 & 10090 & 0.749 & 0 & 0 & 0 & 0.141 & 0.000 & 0.72 & 0.000\\\\\n\t153 & 10090.ENSMUSP00000001327 & 10090.ENSMUSP00000023128 & Itgb7   & Itga5   & 10090 & 0.936 & 0 & 0 & 0 & 0.077 & 0.360 & 0.80 & 0.523\\\\\n\t154 & 10090.ENSMUSP00000001327 & 10090.ENSMUSP00000023128 & Itgb7   & Itga5   & 10090 & 0.936 & 0 & 0 & 0 & 0.077 & 0.360 & 0.80 & 0.523\\\\\n\t155 & 10090.ENSMUSP00000009396 & 10090.ENSMUSP00000023128 & Tspan32 & Itga5   & 10090 & 0.722 & 0 & 0 & 0 & 0.050 & 0.000 & 0.72 & 0.000\\\\\n\t156 & 10090.ENSMUSP00000009396 & 10090.ENSMUSP00000023128 & Tspan32 & Itga5   & 10090 & 0.722 & 0 & 0 & 0 & 0.050 & 0.000 & 0.72 & 0.000\\\\\n\t157 & 10090.ENSMUSP00000009530 & 10090.ENSMUSP00000023128 & Timp1   & Itga5   & 10090 & 0.436 & 0 & 0 & 0 & 0.175 & 0.059 & 0.00 & 0.332\\\\\n\t158 & 10090.ENSMUSP00000009530 & 10090.ENSMUSP00000023128 & Timp1   & Itga5   & 10090 & 0.436 & 0 & 0 & 0 & 0.175 & 0.059 & 0.00 & 0.332\\\\\n\t159 & 10090.ENSMUSP00000009530 & 10090.ENSMUSP00000087119 & Timp1   & Mmp14   & 10090 & 0.970 & 0 & 0 & 0 & 0.195 & 0.758 & 0.00 & 0.861\\\\\n\t160 & 10090.ENSMUSP00000009530 & 10090.ENSMUSP00000087119 & Timp1   & Mmp14   & 10090 & 0.970 & 0 & 0 & 0 & 0.195 & 0.758 & 0.00 & 0.861\\\\\n\t161 & 10090.ENSMUSP00000085523 & 10090.ENSMUSP00000113022 & Ndufa9  & Atp5f1  & 10090 & 0.912 & 0 & 0 & 0 & 0.845 & 0.179 & 0.00 & 0.369\\\\\n\t162 & 10090.ENSMUSP00000085523 & 10090.ENSMUSP00000113022 & Ndufa9  & Atp5f1  & 10090 & 0.912 & 0 & 0 & 0 & 0.845 & 0.179 & 0.00 & 0.369\\\\\n\t163 & 10090.ENSMUSP00000103528 & 10090.ENSMUSP00000109397 & Trim25  & Mx1     & 10090 & 0.950 & 0 & 0 & 0 & 0.153 & 0.049 & 0.90 & 0.461\\\\\n\t164 & 10090.ENSMUSP00000103528 & 10090.ENSMUSP00000109397 & Trim25  & Mx1     & 10090 & 0.950 & 0 & 0 & 0 & 0.153 & 0.049 & 0.90 & 0.461\\\\\n\t165 & 10090.ENSMUSP00000103710 & 10090.ENSMUSP00000125960 & Usp32   & Mcm3ap  & 10090 & 0.491 & 0 & 0 & 0 & 0.080 & 0.456 & 0.00 & 0.066\\\\\n\t166 & 10090.ENSMUSP00000103710 & 10090.ENSMUSP00000125960 & Usp32   & Mcm3ap  & 10090 & 0.491 & 0 & 0 & 0 & 0.080 & 0.456 & 0.00 & 0.066\\\\\n\t167 & 10090.ENSMUSP00000109312 & 10090.ENSMUSP00000120014 & Myg1    & Nhp2    & 10090 & 0.662 & 0 & 0 & 0 & 0.662 & 0.000 & 0.00 & 0.000\\\\\n\t168 & 10090.ENSMUSP00000109312 & 10090.ENSMUSP00000120014 & Myg1    & Nhp2    & 10090 & 0.662 & 0 & 0 & 0 & 0.662 & 0.000 & 0.00 & 0.000\\\\\n\t169 & 10090.ENSMUSP00000119552 & 10090.ENSMUSP00000120014 & Spg7    & Nhp2    & 10090 & 0.425 & 0 & 0 & 0 & 0.061 & 0.000 & 0.00 & 0.413\\\\\n\t170 & 10090.ENSMUSP00000119552 & 10090.ENSMUSP00000120014 & Spg7    & Nhp2    & 10090 & 0.425 & 0 & 0 & 0 & 0.061 & 0.000 & 0.00 & 0.413\\\\\n\\end{tabular}\n","text/plain":"    stringId_A               stringId_B               preferredName_A\n1   10090.ENSMUSP00000000001 10090.ENSMUSP00000085906 Gnai3          \n2   10090.ENSMUSP00000000001 10090.ENSMUSP00000085906 Gnai3          \n3   10090.ENSMUSP00000000001 10090.ENSMUSP00000000153 Gnai3          \n4   10090.ENSMUSP00000000001 10090.ENSMUSP00000000153 Gnai3          \n5   10090.ENSMUSP00000000001 10090.ENSMUSP00000101236 Gnai3          \n6   10090.ENSMUSP00000000001 10090.ENSMUSP00000101236 Gnai3          \n7   10090.ENSMUSP00000000001 10090.ENSMUSP00000000574 Gnai3          \n8   10090.ENSMUSP00000000001 10090.ENSMUSP00000000574 Gnai3          \n9   10090.ENSMUSP00000000028 10090.ENSMUSP00000001258 Cdc45          \n10  10090.ENSMUSP00000000028 10090.ENSMUSP00000001258 Cdc45          \n11  10090.ENSMUSP00000000028 10090.ENSMUSP00000000767 Cdc45          \n12  10090.ENSMUSP00000000028 10090.ENSMUSP00000000767 Cdc45          \n13  10090.ENSMUSP00000000058 10090.ENSMUSP00000001327 Cav2           \n14  10090.ENSMUSP00000000058 10090.ENSMUSP00000001327 Cav2           \n15  10090.ENSMUSP00000000058 10090.ENSMUSP00000105709 Cav2           \n16  10090.ENSMUSP00000000058 10090.ENSMUSP00000105709 Cav2           \n17  10090.ENSMUSP00000000080 10090.ENSMUSP00000000450 Klf6           \n18  10090.ENSMUSP00000000080 10090.ENSMUSP00000000450 Klf6           \n19  10090.ENSMUSP00000000122 10090.ENSMUSP00000101032 Ngfr           \n20  10090.ENSMUSP00000000122 10090.ENSMUSP00000101032 Ngfr           \n21  10090.ENSMUSP00000000122 10090.ENSMUSP00000000187 Ngfr           \n22  10090.ENSMUSP00000000122 10090.ENSMUSP00000000187 Ngfr           \n23  10090.ENSMUSP00000000122 10090.ENSMUSP00000000500 Ngfr           \n24  10090.ENSMUSP00000000122 10090.ENSMUSP00000000500 Ngfr           \n25  10090.ENSMUSP00000000122 10090.ENSMUSP00000105709 Ngfr           \n26  10090.ENSMUSP00000000122 10090.ENSMUSP00000105709 Ngfr           \n27  10090.ENSMUSP00000000153 10090.ENSMUSP00000000776 Gna12          \n28  10090.ENSMUSP00000000153 10090.ENSMUSP00000000776 Gna12          \n29  10090.ENSMUSP00000000175 10090.ENSMUSP00000000349 Sdhd           \n30  10090.ENSMUSP00000000175 10090.ENSMUSP00000000349 Sdhd           \n⋮   ⋮                        ⋮                        ⋮              \n141 10090.ENSMUSP00000001156 10090.ENSMUSP00000119552 Cfp            \n142 10090.ENSMUSP00000001156 10090.ENSMUSP00000119552 Cfp            \n143 10090.ENSMUSP00000001179 10090.ENSMUSP00000118015 Pcnt           \n144 10090.ENSMUSP00000001179 10090.ENSMUSP00000118015 Pcnt           \n145 10090.ENSMUSP00000001186 10090.ENSMUSP00000132735 Snrnp27        \n146 10090.ENSMUSP00000001186 10090.ENSMUSP00000132735 Snrnp27        \n147 10090.ENSMUSP00000001254 10090.ENSMUSP00000099483 Slc26a3        \n148 10090.ENSMUSP00000001254 10090.ENSMUSP00000099483 Slc26a3        \n149 10090.ENSMUSP00000001258 10090.ENSMUSP00000121562 Uhrf1          \n150 10090.ENSMUSP00000001258 10090.ENSMUSP00000121562 Uhrf1          \n151 10090.ENSMUSP00000001327 10090.ENSMUSP00000009396 Itgb7          \n152 10090.ENSMUSP00000001327 10090.ENSMUSP00000009396 Itgb7          \n153 10090.ENSMUSP00000001327 10090.ENSMUSP00000023128 Itgb7          \n154 10090.ENSMUSP00000001327 10090.ENSMUSP00000023128 Itgb7          \n155 10090.ENSMUSP00000009396 10090.ENSMUSP00000023128 Tspan32        \n156 10090.ENSMUSP00000009396 10090.ENSMUSP00000023128 Tspan32        \n157 10090.ENSMUSP00000009530 10090.ENSMUSP00000023128 Timp1          \n158 10090.ENSMUSP00000009530 10090.ENSMUSP00000023128 Timp1          \n159 10090.ENSMUSP00000009530 10090.ENSMUSP00000087119 Timp1          \n160 10090.ENSMUSP00000009530 10090.ENSMUSP00000087119 Timp1          \n161 10090.ENSMUSP00000085523 10090.ENSMUSP00000113022 Ndufa9         \n162 10090.ENSMUSP00000085523 10090.ENSMUSP00000113022 Ndufa9         \n163 10090.ENSMUSP00000103528 10090.ENSMUSP00000109397 Trim25         \n164 10090.ENSMUSP00000103528 10090.ENSMUSP00000109397 Trim25         \n165 10090.ENSMUSP00000103710 10090.ENSMUSP00000125960 Usp32          \n166 10090.ENSMUSP00000103710 10090.ENSMUSP00000125960 Usp32          \n167 10090.ENSMUSP00000109312 10090.ENSMUSP00000120014 Myg1           \n168 10090.ENSMUSP00000109312 10090.ENSMUSP00000120014 Myg1           \n169 10090.ENSMUSP00000119552 10090.ENSMUSP00000120014 Spg7           \n170 10090.ENSMUSP00000119552 10090.ENSMUSP00000120014 Spg7           \n    preferredName_B ncbiTaxonId score nscore fscore pscore ascore escore dscore\n1   Fmr1            10090       0.411 0      0      0      0.144  0.000  0.0   \n2   Fmr1            10090       0.411 0      0      0      0.144  0.000  0.0   \n3   Gna12           10090       0.906 0      0      0      0.060  0.050  0.9   \n4   Gna12           10090       0.906 0      0      0      0.060  0.050  0.9   \n5   Oprm1           10090       0.917 0      0      0      0.000  0.196  0.9   \n6   Oprm1           10090       0.917 0      0      0      0.000  0.196  0.9   \n7   Gpcr2           10090       0.928 0      0      0      0.000  0.251  0.9   \n8   Gpcr2           10090       0.928 0      0      0      0.000  0.251  0.9   \n9   Uhrf1           10090       0.698 0      0      0      0.642  0.000  0.0   \n10  Uhrf1           10090       0.698 0      0      0      0.642  0.000  0.0   \n11  Rpa1            10090       0.983 0      0      0      0.527  0.262  0.9   \n12  Rpa1            10090       0.983 0      0      0      0.527  0.262  0.9   \n13  Itgb7           10090       0.600 0      0      0      0.000  0.000  0.6   \n14  Itgb7           10090       0.600 0      0      0      0.000  0.000  0.6   \n15  Calm1           10090       0.843 0      0      0      0.000  0.050  0.8   \n16  Calm1           10090       0.843 0      0      0      0.000  0.050  0.8   \n17  Pparg           10090       0.432 0      0      0      0.000  0.102  0.0   \n18  Pparg           10090       0.432 0      0      0      0.000  0.102  0.0   \n19  Icosl           10090       0.419 0      0      0      0.000  0.000  0.0   \n20  Icosl           10090       0.419 0      0      0      0.000  0.000  0.0   \n21  Fgf6            10090       0.646 0      0      0      0.000  0.000  0.6   \n22  Fgf6            10090       0.646 0      0      0      0.000  0.000  0.6   \n23  Pdgfb           10090       0.656 0      0      0      0.060  0.000  0.6   \n24  Pdgfb           10090       0.656 0      0      0      0.060  0.000  0.6   \n25  Calm1           10090       0.814 0      0      0      0.000  0.806  0.0   \n26  Calm1           10090       0.814 0      0      0      0.000  0.806  0.0   \n27  Tubgcp3         10090       0.613 0      0      0      0.062  0.000  0.0   \n28  Tubgcp3         10090       0.613 0      0      0      0.062  0.000  0.0   \n29  Dbt             10090       0.411 0      0      0      0.162  0.000  0.0   \n30  Dbt             10090       0.411 0      0      0      0.162  0.000  0.0   \n⋮   ⋮               ⋮           ⋮     ⋮      ⋮      ⋮      ⋮      ⋮      ⋮     \n141 Spg7            10090       0.516 0      0      0      0.000  0.000  0.00  \n142 Spg7            10090       0.516 0      0      0      0.000  0.000  0.00  \n143 Ift20           10090       0.476 0      0      0      0.000  0.000  0.00  \n144 Ift20           10090       0.476 0      0      0      0.000  0.000  0.00  \n145 Hnrnpd          10090       0.902 0      0      0      0.061  0.000  0.90  \n146 Hnrnpd          10090       0.902 0      0      0      0.061  0.000  0.90  \n147 Car4            10090       0.422 0      0      0      0.060  0.000  0.00  \n148 Car4            10090       0.422 0      0      0      0.060  0.000  0.00  \n149 Dnmt3l          10090       0.707 0      0      0      0.069  0.000  0.00  \n150 Dnmt3l          10090       0.707 0      0      0      0.069  0.000  0.00  \n151 Tspan32         10090       0.749 0      0      0      0.141  0.000  0.72  \n152 Tspan32         10090       0.749 0      0      0      0.141  0.000  0.72  \n153 Itga5           10090       0.936 0      0      0      0.077  0.360  0.80  \n154 Itga5           10090       0.936 0      0      0      0.077  0.360  0.80  \n155 Itga5           10090       0.722 0      0      0      0.050  0.000  0.72  \n156 Itga5           10090       0.722 0      0      0      0.050  0.000  0.72  \n157 Itga5           10090       0.436 0      0      0      0.175  0.059  0.00  \n158 Itga5           10090       0.436 0      0      0      0.175  0.059  0.00  \n159 Mmp14           10090       0.970 0      0      0      0.195  0.758  0.00  \n160 Mmp14           10090       0.970 0      0      0      0.195  0.758  0.00  \n161 Atp5f1          10090       0.912 0      0      0      0.845  0.179  0.00  \n162 Atp5f1          10090       0.912 0      0      0      0.845  0.179  0.00  \n163 Mx1             10090       0.950 0      0      0      0.153  0.049  0.90  \n164 Mx1             10090       0.950 0      0      0      0.153  0.049  0.90  \n165 Mcm3ap          10090       0.491 0      0      0      0.080  0.456  0.00  \n166 Mcm3ap          10090       0.491 0      0      0      0.080  0.456  0.00  \n167 Nhp2            10090       0.662 0      0      0      0.662  0.000  0.00  \n168 Nhp2            10090       0.662 0      0      0      0.662  0.000  0.00  \n169 Nhp2            10090       0.425 0      0      0      0.061  0.000  0.00  \n170 Nhp2            10090       0.425 0      0      0      0.061  0.000  0.00  \n    tscore\n1   0.340 \n2   0.340 \n3   0.445 \n4   0.445 \n5   0.059 \n6   0.059 \n7   0.124 \n8   0.124 \n9   0.190 \n10  0.190 \n11  0.588 \n12  0.588 \n13  0.000 \n14  0.000 \n15  0.240 \n16  0.240 \n17  0.393 \n18  0.393 \n19  0.419 \n20  0.419 \n21  0.152 \n22  0.152 \n23  0.160 \n24  0.160 \n25  0.083 \n26  0.083 \n27  0.605 \n28  0.605 \n29  0.327 \n30  0.327 \n⋮   ⋮     \n141 0.516 \n142 0.516 \n143 0.476 \n144 0.476 \n145 0.000 \n146 0.000 \n147 0.410 \n148 0.410 \n149 0.699 \n150 0.699 \n151 0.000 \n152 0.000 \n153 0.523 \n154 0.523 \n155 0.000 \n156 0.000 \n157 0.332 \n158 0.332 \n159 0.861 \n160 0.861 \n161 0.369 \n162 0.369 \n163 0.461 \n164 0.461 \n165 0.066 \n166 0.066 \n167 0.000 \n168 0.000 \n169 0.413 \n170 0.413 "},"metadata":{}},{"output_type":"stream","text":"Number of nodes: 80\n\nNumber of links: 85\n\nLink Density: 1.0625\n\nThe connectance of the graph: 0.0134493670886076\n\nMean Distences1.57432432432432\n\nAverage Path Length1.57432432432432\n\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A data.frame: 80 × 3</caption>\n<thead>\n\t<tr><th></th><th scope=col>names</th><th scope=col>degree</th><th scope=col>betweenness</th></tr>\n\t<tr><th></th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><th scope=row>10</th><td>Itgb2   </td><td>16</td><td> 0</td></tr>\n\t<tr><th scope=row>47</th><td>Timp1   </td><td>16</td><td>21</td></tr>\n\t<tr><th scope=row>14</th><td>Pparg   </td><td>12</td><td>18</td></tr>\n\t<tr><th scope=row>7</th><td>Sdhd    </td><td>10</td><td> 0</td></tr>\n\t<tr><th scope=row>63</th><td>Itga5   </td><td>10</td><td> 0</td></tr>\n\t<tr><th scope=row>1</th><td>Gnai3   </td><td> 8</td><td> 0</td></tr>\n\t<tr><th scope=row>5</th><td>Ngfr    </td><td> 8</td><td> 0</td></tr>\n\t<tr><th scope=row>35</th><td>S100a4  </td><td> 8</td><td> 0</td></tr>\n\t<tr><th scope=row>38</th><td>Sec24b  </td><td> 8</td><td> 3</td></tr>\n\t<tr><th scope=row>45</th><td>Itgb7   </td><td> 8</td><td> 2</td></tr>\n\t<tr><th scope=row>23</th><td>Rab5b   </td><td> 6</td><td> 0</td></tr>\n\t<tr><th scope=row>30</th><td>Smarcb1 </td><td> 6</td><td> 4</td></tr>\n\t<tr><th scope=row>46</th><td>Tspan32 </td><td> 6</td><td> 0</td></tr>\n\t<tr><th scope=row>52</th><td>Spg7    </td><td> 6</td><td> 5</td></tr>\n\t<tr><th scope=row>54</th><td>Oprm1   </td><td> 6</td><td> 0</td></tr>\n\t<tr><th scope=row>59</th><td>Atp5f1  </td><td> 6</td><td> 0</td></tr>\n\t<tr><th scope=row>60</th><td>Mxd1    </td><td> 6</td><td> 0</td></tr>\n\t<tr><th scope=row>69</th><td>Nhp2    </td><td> 6</td><td> 0</td></tr>\n\t<tr><th scope=row>2</th><td>Cdc45   </td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>3</th><td>Cav2    </td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>6</th><td>Gna12   </td><td> 4</td><td> 3</td></tr>\n\t<tr><th scope=row>9</th><td>Mnt     </td><td> 4</td><td> 1</td></tr>\n\t<tr><th scope=row>11</th><td>Dbt     </td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>12</th><td>Trappc10</td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>15</th><td>Raf1    </td><td> 4</td><td> 3</td></tr>\n\t<tr><th scope=row>16</th><td>Pdgfb   </td><td> 4</td><td> 3</td></tr>\n\t<tr><th scope=row>17</th><td>Gabra2  </td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>18</th><td>Gpcr2   </td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>20</th><td>Hk2     </td><td> 4</td><td> 1</td></tr>\n\t<tr><th scope=row>22</th><td>Kat2b   </td><td> 4</td><td> 0</td></tr>\n\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n\t<tr><th scope=row>4</th><td>Klf6    </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>8</th><td>Ccnd2   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>13</th><td>Mkrn2   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>19</th><td>C1d     </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>21</th><td>Haao    </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>24</th><td>Rpl13   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>26</th><td>Serpinf1</td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>28</th><td>Polr3d  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>29</th><td>Ddx3x   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>32</th><td>Oxa1l   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>42</th><td>Snrnp27 </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>43</th><td>Slc26a3 </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>50</th><td>Usp32   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>51</th><td>Myg1    </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>56</th><td>Icosl   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>57</th><td>Fgf6    </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>61</th><td>Cd52    </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>62</th><td>Dnajc5  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>65</th><td>Gabrg1  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>66</th><td>Slc22a18</td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>67</th><td>Vps9d1  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>70</th><td>Uxt     </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>73</th><td>S100a3  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>74</th><td>Tom1l2  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>75</th><td>Ift20   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>76</th><td>Hnrnpd  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>77</th><td>Car4    </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>78</th><td>Dnmt3l  </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>79</th><td>Mmp14   </td><td>2</td><td>0</td></tr>\n\t<tr><th scope=row>80</th><td>Mx1     </td><td>2</td><td>0</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 80 × 3\n\n| <!--/--> | names &lt;chr&gt; | degree &lt;dbl&gt; | betweenness &lt;dbl&gt; |\n|---|---|---|---|\n| 10 | Itgb2    | 16 |  0 |\n| 47 | Timp1    | 16 | 21 |\n| 14 | Pparg    | 12 | 18 |\n| 7 | Sdhd     | 10 |  0 |\n| 63 | Itga5    | 10 |  0 |\n| 1 | Gnai3    |  8 |  0 |\n| 5 | Ngfr     |  8 |  0 |\n| 35 | S100a4   |  8 |  0 |\n| 38 | Sec24b   |  8 |  3 |\n| 45 | Itgb7    |  8 |  2 |\n| 23 | Rab5b    |  6 |  0 |\n| 30 | Smarcb1  |  6 |  4 |\n| 46 | Tspan32  |  6 |  0 |\n| 52 | Spg7     |  6 |  5 |\n| 54 | Oprm1    |  6 |  0 |\n| 59 | Atp5f1   |  6 |  0 |\n| 60 | Mxd1     |  6 |  0 |\n| 69 | Nhp2     |  6 |  0 |\n| 2 | Cdc45    |  4 |  0 |\n| 3 | Cav2     |  4 |  0 |\n| 6 | Gna12    |  4 |  3 |\n| 9 | Mnt      |  4 |  1 |\n| 11 | Dbt      |  4 |  0 |\n| 12 | Trappc10 |  4 |  0 |\n| 15 | Raf1     |  4 |  3 |\n| 16 | Pdgfb    |  4 |  3 |\n| 17 | Gabra2   |  4 |  0 |\n| 18 | Gpcr2    |  4 |  0 |\n| 20 | Hk2      |  4 |  1 |\n| 22 | Kat2b    |  4 |  0 |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| 4 | Klf6     | 2 | 0 |\n| 8 | Ccnd2    | 2 | 0 |\n| 13 | Mkrn2    | 2 | 0 |\n| 19 | C1d      | 2 | 0 |\n| 21 | Haao     | 2 | 0 |\n| 24 | Rpl13    | 2 | 0 |\n| 26 | Serpinf1 | 2 | 0 |\n| 28 | Polr3d   | 2 | 0 |\n| 29 | Ddx3x    | 2 | 0 |\n| 32 | Oxa1l    | 2 | 0 |\n| 42 | Snrnp27  | 2 | 0 |\n| 43 | Slc26a3  | 2 | 0 |\n| 50 | Usp32    | 2 | 0 |\n| 51 | Myg1     | 2 | 0 |\n| 56 | Icosl    | 2 | 0 |\n| 57 | Fgf6     | 2 | 0 |\n| 61 | Cd52     | 2 | 0 |\n| 62 | Dnajc5   | 2 | 0 |\n| 65 | Gabrg1   | 2 | 0 |\n| 66 | Slc22a18 | 2 | 0 |\n| 67 | Vps9d1   | 2 | 0 |\n| 70 | Uxt      | 2 | 0 |\n| 73 | S100a3   | 2 | 0 |\n| 74 | Tom1l2   | 2 | 0 |\n| 75 | Ift20    | 2 | 0 |\n| 76 | Hnrnpd   | 2 | 0 |\n| 77 | Car4     | 2 | 0 |\n| 78 | Dnmt3l   | 2 | 0 |\n| 79 | Mmp14    | 2 | 0 |\n| 80 | Mx1      | 2 | 0 |\n\n","text/latex":"A data.frame: 80 × 3\n\\begin{tabular}{r|lll}\n  & names & degree & betweenness\\\\\n  & <chr> & <dbl> & <dbl>\\\\\n\\hline\n\t10 & Itgb2    & 16 &  0\\\\\n\t47 & Timp1    & 16 & 21\\\\\n\t14 & Pparg    & 12 & 18\\\\\n\t7 & Sdhd     & 10 &  0\\\\\n\t63 & Itga5    & 10 &  0\\\\\n\t1 & Gnai3    &  8 &  0\\\\\n\t5 & Ngfr     &  8 &  0\\\\\n\t35 & S100a4   &  8 &  0\\\\\n\t38 & Sec24b   &  8 &  3\\\\\n\t45 & Itgb7    &  8 &  2\\\\\n\t23 & Rab5b    &  6 &  0\\\\\n\t30 & Smarcb1  &  6 &  4\\\\\n\t46 & Tspan32  &  6 &  0\\\\\n\t52 & Spg7     &  6 &  5\\\\\n\t54 & Oprm1    &  6 &  0\\\\\n\t59 & Atp5f1   &  6 &  0\\\\\n\t60 & Mxd1     &  6 &  0\\\\\n\t69 & Nhp2     &  6 &  0\\\\\n\t2 & Cdc45    &  4 &  0\\\\\n\t3 & Cav2     &  4 &  0\\\\\n\t6 & Gna12    &  4 &  3\\\\\n\t9 & Mnt      &  4 &  1\\\\\n\t11 & Dbt      &  4 &  0\\\\\n\t12 & Trappc10 &  4 &  0\\\\\n\t15 & Raf1     &  4 &  3\\\\\n\t16 & Pdgfb    &  4 &  3\\\\\n\t17 & Gabra2   &  4 &  0\\\\\n\t18 & Gpcr2    &  4 &  0\\\\\n\t20 & Hk2      &  4 &  1\\\\\n\t22 & Kat2b    &  4 &  0\\\\\n\t⋮ & ⋮ & ⋮ & ⋮\\\\\n\t4 & Klf6     & 2 & 0\\\\\n\t8 & Ccnd2    & 2 & 0\\\\\n\t13 & Mkrn2    & 2 & 0\\\\\n\t19 & C1d      & 2 & 0\\\\\n\t21 & Haao     & 2 & 0\\\\\n\t24 & Rpl13    & 2 & 0\\\\\n\t26 & Serpinf1 & 2 & 0\\\\\n\t28 & Polr3d   & 2 & 0\\\\\n\t29 & Ddx3x    & 2 & 0\\\\\n\t32 & Oxa1l    & 2 & 0\\\\\n\t42 & Snrnp27  & 2 & 0\\\\\n\t43 & Slc26a3  & 2 & 0\\\\\n\t50 & Usp32    & 2 & 0\\\\\n\t51 & Myg1     & 2 & 0\\\\\n\t56 & Icosl    & 2 & 0\\\\\n\t57 & Fgf6     & 2 & 0\\\\\n\t61 & Cd52     & 2 & 0\\\\\n\t62 & Dnajc5   & 2 & 0\\\\\n\t65 & Gabrg1   & 2 & 0\\\\\n\t66 & Slc22a18 & 2 & 0\\\\\n\t67 & Vps9d1   & 2 & 0\\\\\n\t70 & Uxt      & 2 & 0\\\\\n\t73 & S100a3   & 2 & 0\\\\\n\t74 & Tom1l2   & 2 & 0\\\\\n\t75 & Ift20    & 2 & 0\\\\\n\t76 & Hnrnpd   & 2 & 0\\\\\n\t77 & Car4     & 2 & 0\\\\\n\t78 & Dnmt3l   & 2 & 0\\\\\n\t79 & Mmp14    & 2 & 0\\\\\n\t80 & Mx1      & 2 & 0\\\\\n\\end{tabular}\n","text/plain":"   names    degree betweenness\n10 Itgb2    16      0         \n47 Timp1    16     21         \n14 Pparg    12     18         \n7  Sdhd     10      0         \n63 Itga5    10      0         \n1  Gnai3     8      0         \n5  Ngfr      8      0         \n35 S100a4    8      0         \n38 Sec24b    8      3         \n45 Itgb7     8      2         \n23 Rab5b     6      0         \n30 Smarcb1   6      4         \n46 Tspan32   6      0         \n52 Spg7      6      5         \n54 Oprm1     6      0         \n59 Atp5f1    6      0         \n60 Mxd1      6      0         \n69 Nhp2      6      0         \n2  Cdc45     4      0         \n3  Cav2      4      0         \n6  Gna12     4      3         \n9  Mnt       4      1         \n11 Dbt       4      0         \n12 Trappc10  4      0         \n15 Raf1      4      3         \n16 Pdgfb     4      3         \n17 Gabra2    4      0         \n18 Gpcr2     4      0         \n20 Hk2       4      1         \n22 Kat2b     4      0         \n⋮  ⋮        ⋮      ⋮          \n4  Klf6     2      0          \n8  Ccnd2    2      0          \n13 Mkrn2    2      0          \n19 C1d      2      0          \n21 Haao     2      0          \n24 Rpl13    2      0          \n26 Serpinf1 2      0          \n28 Polr3d   2      0          \n29 Ddx3x    2      0          \n32 Oxa1l    2      0          \n42 Snrnp27  2      0          \n43 Slc26a3  2      0          \n50 Usp32    2      0          \n51 Myg1     2      0          \n56 Icosl    2      0          \n57 Fgf6     2      0          \n61 Cd52     2      0          \n62 Dnajc5   2      0          \n65 Gabrg1   2      0          \n66 Slc22a18 2      0          \n67 Vps9d1   2      0          \n70 Uxt      2      0          \n73 S100a3   2      0          \n74 Tom1l2   2      0          \n75 Ift20    2      0          \n76 Hnrnpd   2      0          \n77 Car4     2      0          \n78 Dnmt3l   2      0          \n79 Mmp14    2      0          \n80 Mx1      2      0          "},"metadata":{}}]},{"metadata":{"trusted":false},"cell_type":"code","source":"# Plotting the network of the top 50 hub DEGs\nDATA<-networking[1:50,1]\nnetwork<-Networking(DATA,FileName,plot_width = 25, plot_height = 25,species = \"10090\")","execution_count":24,"outputs":[{"output_type":"stream","text":"Examine response components =200\t(200 means successful)\n\n\nYou can see the network with high resolution by clicking on the following link:\nhttps://string-db.org/api/highres_image/network?identifiers=Itgb2%0dTimp1%0dPparg%0dSdhd%0dItga5%0dGnai3%0dNgfr%0dS100a4%0dSec24b%0dItgb7%0dRab5b%0dSmarcb1%0dTspan32%0dSpg7%0dOprm1%0dAtp5f1%0dMxd1%0dNhp2%0dCdc45%0dCav2%0dGna12%0dMnt%0dDbt%0dTrappc10%0dRaf1%0dPdgfb%0dGabra2%0dGpcr2%0dHk2%0dKat2b%0dRpa1%0dTubgcp3%0dHip1r%0dCcl3%0dChtop%0dS100a6%0dIcam2%0dAraf%0dCfp%0dPcnt%0dUhrf1%0dNdufa9%0dTrim25%0dFmr1%0dCalm1%0dDlat%0dLman2l%0dOcrl%0dWdr77%0dMcm3ap&species=10090\n\n","name":"stderr"},{"output_type":"display_data","data":{"text/plain":"plot without title","image/png":"iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nI+PRRx89+gVoPLRkwymj8orHjx+fnZ09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Yy49i4iEEAXvfu2xpY897xzZwaNRq6ZpiqL0Lup9+E2Vps2XrqouNG5kk8nk7Ons0LPNqnc3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\naDSyLKOjsKOoqqooit/vr6mpef+2adGO+uzlzHZeO0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U1ooYknXzgi4iprGWP3DSfz+dyuYp//KJjx44NtWyEkKTXtOyaPCM3q/CY/w3J\nXNMvdaHd2O2Yb+ETe/nascFgMBgM3YZkJHR0ZPZOaTNYa0mmlfur+PXpXVL1ej2/fYNE21AY\nY9lVfoSQLMvvVTuxJBmNxhczL31v0ZvTNzy3O/Lr+ysKTSYTf4z8nV5z9xqNRjNmTJ5e/wUA\nzJkT7ccgnCSEAKBuni8YjDYGwBjNzu575EglpQQALrpoHFBKCEMI6psWCWc8kdgJQhNxZjsT\nlnhSE5pXD97GExcXN3LkyJ0rPv31h49ramrcbrff7w8EAsFgsMeALoFAwDLU/93Wj10uV8mu\nLUVL5gwbNqxNmzZRDLhX8gCtVqvX641Go9lsNplMNput6lDVzvW7zSbzgcMH07M7m0wms9ls\nNBp5Yte5VY8oBvxnWq02JSUlvHCx1+vtgeXiQEkkElFV9eUJU14aNHVIXP9OXUBRlEgkEgwG\nfT7fygXLuz/aU+vSziybOW7cuEsvvXTJkmsAYM4cCIcbMjC+ZEgJoZTwP9Vd+buGvL+zRv2k\nkIDffd99DyCEvv9uUWysg6d9koRZ3cSQhu2VKDRn4lSsIDQFZ7az89q2YaXSpG8f7ViaDqV0\n69atixYtKikpkbT6rsOu1Gg09YMcVFUtXl9Qc3hvfHz8iBEjevbsKctR2xzCGFu9enWpxulw\nOIxG45Zft2d2TzVbzDWVrj2/Ffca1ouf5KCUEkIUReGjMuJD6QMGDGhWm9NdLtcHH3yw5sk1\nu1/rOrxlr6OH1EHd0x4Oh/1+/8GPHjLZPE/835qLN7lf6t22X0tLUU5RVmHWCy+8sGzZsvz8\n/DlzoEcPyMw89aAYY0xVFUoJQljCEh+ry2qH6BLKkCRJCDXwBI4zwC9bNjy94B29Xv/Bf5/W\nyBoAqPLU3PrG9GAwuHD6O1A3AYVSlVHG+M46yif8AgPACCEkS7IsntuzikjsBKHRObOdmYWZ\nOw89m5Y0JdqxREEwGNyxY8evv/564MABr9fLN9LJsmw0GpOSknr27JmZmWkymaKeHm3atOn9\n99+/4KbeFovFYDDIsixJUn1KxEf38sSIz2z47q21kyZN6tmz2TUKWdNjTfF/ixcsvq+laax3\nxDnxOgd/IHxaRiQSiSxaotFo0tLSLrroIpPtyK/7b8zNKuzzjbOjRf/xgBRntjNrbdaRqiOT\nJ0/Oz8+fNw8Qgssv//fxUEoZVVVV0RvMgYCHt31GgBllFOiMGTNvuOH6hPhESZZF8nG0S564\n9fkbHuQD4WpbWkN9nRMem/ea2+v+7MFXARglFGqHS/PMjiGEEEiAQMIYYYyQ2GB3FhGJnSA0\nLj40bM2uced2+iLasUQTb1YXCoUikQhjjC966nS6qOdz9Xw+3zvvvLNr166Rt/TlAzN4rYtn\nG5RSSqmiKOFw2OfzffvmL6ph0XNTi6xWa7QD/wP+elu8KTvV9sW6deu2b9/ucrkURaGU8r13\nVqu1ffv2ffr0SUlJqW+FzRhZWtQnN6tw4o/F29zB9XlZzmxnuzfbmXPMeXl5+fn5K1ZASck/\nPl1Rt+RKiKqGwiFZQowBIASMpXRKL9617YIRo0wm45dffiFLMpY0GIv8o9aE5+6xWq2X9bsQ\nS/iYliV8XVtVyac/fuvzeefe9zx/G69b0WaMQV2jHswY4+cn4M/dg4QzlEjsBKER8XfZZUXn\nDslcE+1YhBM7dOjQ3Llz9+7dO/jabiaTqT63AwBKaf0i5rL/bcwP6tc8/t/trouGdWlGbWv4\n661+nAljjPdS9vv9qqry6cN8Fshf5dP8e1eUee4tPFCYl+XMdpqyTe3fas/Tu+pq+Pbbf5be\nUUopVSklBoN5T/HOdu1T/D4XAJjMdr/PZbXFDh+Wu2DBfEnWYCzzsmLzyfWj5eoX7nM4HAaD\nISu5kyRJCNAfsjIGlNEdpXuDoZDP56upqfnk/peiFqvQ/IjEThAaS927bE5u1pnf3OSM4fV6\nV61atWbNmpqaGkrpgKu6aLQyAKgRdeWcLRhju93eoajDpR9cOvTHA4WjuhwzFC6KjsnqTkWB\nM3tw+ipJMmbnOwvzsrblbiMuklWYlZeXN2fOHLvdPmfO7+ndgw/kGY26jindLr/ikfqfMPOp\ny8Lh8LTHvq5P7BhhrHZaLt8MBgCAMGOAJYwlrMGydBpVlSil9UujCPEVZF43AwaAEGKMIoQQ\nIMprZnXdSU7o7tcfVw3YYrHwzt58M4As1T05jClE5cuufK+n2+1++z/TG+lhCqcjkdgJQqNo\nwHdZoYkxxvx+/4EDB4qLi8vKygKBAAAYjcaEhIQOHTq0bdvWZDJ5V3k1iZqBO4vXjWwWf8uN\n8XorcGanxN/ersU1PL07MvvIkdeO8PRu+PDhd9xxx0cfsZIDl02+/unabWB1pTZWtyw496Op\nEYXce98HALS+FPeHNx2e7SCEkHQabbCjlFBKgDHKWN3DZgwQAGCEGCBglDKGEWIAvBJ58mXI\n/7w9LTY21mQ2aDX6o3d5Ho0ndryE7PV63ZU1b932WMM+RuH0JRI7QWh4Iqs7Y9R34kB1W9jr\nET+pmFVxbWfX98M6R/fvulFfb8u29tNKjv5p+dn5zjUjMiUv3TZ0W1Zh1sUXX3xeP7PFYjm3\n3ziEePqBtAwTYIQSSdZEiEJUddXKT1wu14MPf97ggUVLbeMWouh0RgB4/bVXJ0++BhDKyOiy\nrWgzBQwACOgtt95us9lmzHhKkmRJ4udCTiq3u+/j5x0OR2ysnbAgBpN/32xb+ysZNgAA+Iuw\nsaOiKj6vS6O3akLbw9pMokYOHDz0yqQHG/NBC6eT0+YTkiCcLpw5Iqs7cyDeKOJ4VRPJJCXc\nnvDik+z/fj04JLOwwJkdlQgb+1PEkIzV/dPyf9t7+8x2k7QY9flx2675iQAw8Dyrw+GwWq0u\nV4nHc8jlKnHVlITKD+nKjri8pZUle3HEs33bMpvNFhsb+9QT4xsjtiiilAGA3+detmwZZYwS\nsm/ffkIpo8RkslBKX3v15ZdffpVSSohKKTnJEsrWQ0/xLtkAkkEXo9fribmfzug4sGeDXq/T\nxfTUGuxanaFlQkebLU4X11enoZSh5tYoW4guUbEThIa0+/LdKZ+kiKzurOLMdqqL20sIHSkd\nMjTzlya+66b8FOEP7f1596W5WYXPP3tVbGysxWLRarWyLEsRCgYNDbN0t2lFze4L+48AxH4r\n20Ap5c2Qa2pqbrnt3SaIsAkwxijv3qI3BfxuAPjqq4Vjx462WGNuvvmmp2c+ZTLbA343MDb+\n8gmffTLP7XbHtWyBsea4a82bD0w54lkGAIMzfpSwgTH2eP67cXG1M4tlWUZ16u+9LobaM9oe\nj6dfYkZ2SpcmfA6EZk0kdoLQYKo+r4q9NFZkdWchZ7YzfX3mYxsP5RpuOq/zN012p9GqDb//\n7m0Oh4MfsJUwBoSAQeBwsGtml2JXMQCUFh1oldGmPvlwu93de4zLzDqnieP8d+Z89/X89Sv1\nev17dz5i1OkB4MeiDa9+/UkwGMx//FUA4EU4QghGAAgxyhijjDEGDAEYTbaA3wMIajceYixJ\nvEc0BoC1u6/yhrYDwKCMH2VsAIDsfCcAtDFqFwxJZYzNfPrpuNxMq9XKmynyanF9Ulg/qOPo\nRtnD4rqkpqb+1cMRzjYisROEhsEUxhS2bE8fcQb27MTTrH7fbX2VwY4sAAAgAElEQVSv66K0\nxLsx0jbB3UXrU8S8uVP4iA6tVrtv4/Z2XdIkCkeWF8WP6FX9w6bY87skbwnty9Ju+e7ntKG9\n+Ab/qir35OtfbPpQ/6lxT973/I33HHscpK5B3IOzXwsGAp8//CxvJocAIYwYo3zwA6UUGAUA\njCUEAAgDQpsO3BUI741Q15DMnxBgAMjJdzKAtibd/MGdAGDOnDmffPIJALz++uvJycmLFi0q\nawUxMTFGo1Gn08l1fZuPPnrCszr+xG56duUQ17nD5w+P0hMmNDsisROEU8IYJapKGQ3vD5eb\nvkyOuwIYYAkxhhBQAARIqm9yK5zZeLKVne/86fyWwfBBh7lXo95RFGvDX305tT6xQwh5PN6y\ndbslA04d0HP//v3tkpJ3rfq16lBF9pXDVVUNhUJerzcQxBdd3Nwnr1z1/CNWq/Wyfrm1h0H+\nXCdT1Y9X/eDzeOc9MKPuIGzttFZKKQCjlGzYd6tCPBhLfVI+BoC93tAVPxYTBoMSLM9ltwWA\n11577fvvvweADz/8MCYm5pgYtm/f/s4772RMGGKz2QwGg06nkySpfjX26POwfr/fVVW97tM7\nX3zMeWj4oTbPtbEOaF7tsoWoEImdIJwSSomqRFontt2x5ye9JoGvwiD0e6N3LMlHL8QIZ7b6\n3K4wL2vDvjt6tHulke4iuiv+3+Y/GRsbazab+fDfsh0HTbLNnupQiSpL8s+ffGeMtXUZek79\nuBG3233OuTfr9fpoBXwyJr441W63G43GrOQOEpa0WklRaP1X+Wrr1pK9oVDI5/N5XO55D8wA\nAMrUZUXnAIBZl3JOp08A4LsS1yMbSwDgnsyEK9rHAcCDDz64efNmAOBdAP8+DFVVv/vuu8WL\nF6dfOtASYzMajfxJ5t2bedPpcDgcDAbXvbvIbDRNmDAhMzNt+bZ+uVmFzmxnekG6ZBcDPM5q\nURu5LQhnAEopY6xqbhWSIcaeFvC7/X43/+ju8/vNtfNP2Um3JhVOe1mFWc5sZ2FtbvfKsqK+\nQzJ/bsCf3xyyOkJIKNJSo8F8LpxGozlcXtm1dytXcWXAV53ar+feitLrJ1/CUxBJkhhjsixX\nV1cnJiZGK+YTuuq5RxwxDrPZHOOwH3BV8m1tGqnuLZKxCFEppfX73iilhXsnZ7efhZFchPI/\n2lsJAB+3DKXa9Be2tn/8f7fX1NTMBbh4/nydTvfUU0+dfCSyLF9wwQUOh+P777+vqqrqcF73\nmM6t+QQUXq6LRCIbP/gBMWjfrt2YMWNSUlIQQrlZhf7QvrLZ12bZC/mLpDGeJeG0ICp2gnBS\n1q1f9smCZwFgSP8JeSN+n6m0a+LOmNd8GZ0u2Lt3t8lk4+OSEEL+QMBoMGAsISzJskaSsOgu\ndPZw5jiz1tfW7RowCWsOWR0AEELmzp2bmR7hq7E6nS4SiZjN5mLnro5ZnQCg/JdtLXqnEUJU\nVQ0Gg16v938fbLzrrruSkpKiGPbfu+XdZ2NiYurmrTG9Xquq4PEctFrb1N+m/iwqX1zeHImf\nf9VFZhkDQF5eHgDo9fovvmiYkdCMMY/H43Q6N27cWFpaGggECCF82q/FYmnfvn2vXr1SUlL+\nXAQtOjjtsDufV+9Eend2EomdIPyd+6depNPJj9w3V5Y1R18/dcb4YDA4aekz7ZfHINAj0NVu\naz7qHxRCiAHIEhbTzc9CW8/dmrEmowFzu2aS1XGFhYUffvjhpGu688ObWq32mH1gPKsLh8M+\nn2/RN1uNxoTrrrvOaDRGO/C/NGXem3a73WQy8dqYryQ/7DtiiB+wYd3y8y68CQCqSjaEq36J\nz7qREMJPLRw5ciSwumj27NmNGhgv0YVCoUgkghDiXe40Gs3fj7JYvXMsRpq+nT4X6d1ZSCR2\ngvCXps4Yf/3Ex2u3UR/1a5TvdKGU/m/u9JtvuiXG3JcxihAw+od/TQjzXdUYY0nMNT8L7bx4\nZ+qXqf86t2OM8e35jMGusTvTvk5bvm3Q4IyVJ/y+Jpi4Gg6Hv/rqq1WrVl09IctisRgMBr4P\njL/Oj97d//a76+Lj46+99tr27ds3dlSn4pEvZ/EGLjyxwxi7PSW7CxdmD711zdLZVKnRGlpk\nn3dl/RY3r9frqap56sr/RDvwv1PgzEl0XJSR9KBI784qIrEThON76oWrrVZr35zRkoQRwqj2\n+BtAbWJHKaVr1i30er3/d/eHUY1UaL6qv662D7f3Wbp1/T/P7QghjBHG2JH3j7SY3HJr6fQu\nbaYzhjACWjd3tRYDQIARRhg1QVbHRSKRH3/8cenSpTU1NSMu7JzYylS/D4wQEolEZs3eoNFo\nMjIyxowZ06pVq2b+2eaJb+by4yBHVx95zB5XWcG3H1x0xRS+p1ZRlGAw6PF4bj7ngmZ+HIQr\ncOYMSi+QsY0PxYl2OEKjE4mdIBzHUy9cXd/NISG+IwJgrXRSeQQAGAOzLHkUtay8mDcIdblc\nD9z1QbRDFpopFmE1+TUX6g6vHZn5T3K72oGkWq0RAGRZ9nprEMMM8RIeAqg7kcOAAUMIYSxL\nktSUi/6MMbfbvXnz5i1bthw+fDgQCBys/Fq3uG/ixES73d6pU6eePXsmJydrNJoT/6yoopTO\nXjSfto7h1cf6tsA8sSsrLY5v1eHotsB+v9/lco1t261169bRjv1kFTizh2aurfnaXfpkadZ6\nkd6dycSpWEE41q7iLXa73WKx8L0s75R+fKVtqL8kuERadlEgT/1mnnTpJK8vYDQaFUXhJYpP\nv3jtsnG3RTtwoTlCWhRzccyHoytuitn7dt/Ck87tGGNQtbAaADye8gkTrqOUIEaGnT9y8eJv\n+XgDflLHaov1umsMZmsoFJSkJj2ggxCy2+0DBgzo379/JBIJh8Ort++0bbm526Pd9Hp9/cps\n88cY0/kiQQBJkrRaLS/a1Q8I7pDShVdAVFVVVZVSijEu+XY1ufF0So/4q+63lOG56wv33bEv\nuCWYvjw92kEJjUIc0xOEY32z5DWr1Wo2m00mk8FguCFxlNFo+KZ0Zwcpcc+W/DhC9ZJsNBoN\nBoPJZLJYLFartby6KNpRC81a2sK02+/wF9UEz0tfV+DMPuarhFJCyR+vw3tv3xuTFwMAsmz4\n5JO5vDnizBlPrFyxsn6lBfEVW6AAgBBvuhaFRRiMsV6vt9lsDofBJtnMZjMfctr0kfw7kiSl\npqZ+88IEj8fj8/mCwWAkElEURVEUfgpEVVWeuQYCAZ/Pd2TPwRYxsXFxcdEO/B/j6d3uG8el\nL0939nbunrA72hEJDU9U7AThWCaTyWg08nXY+t02fXHpaneXwQlDA311BKsGA6GUUkplWaaU\nhkKhaEctNHdZ67Oc2U5Ym1Gs+UbaOvCjr7q6qX9kn2GTci/HCPGNMVc/f5vP71vw6OyiAUXp\nKzrvOvxqMOhHAIAZoxQw9OrVi2/4DAW9fMhVVVV53aIsBmi6PXbHhfBp+Z5S4Mw+r+vqvUUJ\nv773RddrxpjN5r86DhIIBH55c15MTMzEiRNNJlO0A/+XeHpXNuta3hWl9YzW9mEnaJssnEZO\ny3+EgtCo9Hq9Xq/nTRwwxv6wy2qIbdPmErrh154DDf4QCauSxainRCWEYIxVVdXpdNGOWjgN\n8PbFNxVmjZ+ROnPyvXyx70DlIajbLffExAcYY3e883DZ+WW37zzULzW/dk4pYxRRYMAHmNYN\nDgVGqdkoMwSRcAjVLR1GkYzN0Q3gn1KIb+W2QblZhXl5eWvXrl2xYsWy+UvdbnfXq0bpTcb6\nriL8OMhvs76UME5LSxs7dmy7du2iHfupys0qJDRUNvvarKxCZ7az85LOsqMBUgLGGEKID1hj\njB69IZQxCoy3C0B1k3gYpRQhhNCxzQeEf00cnhCEYy1ePlvS+vgeu6O3UasAfsasANsOuju3\ntvIP8ZFIxOv1VlRUjB89NdqBC6cBxtg1L91hsVgu6TtSwpJBq5MV5scq8qkQVqRYU1hVCCWf\n/7TI4/F8POVN/rbHWP1sq9/f/hgD/r7I30qbQ1edTfvvlS659nQ5esn3O+bl5eXn5/NrGGNV\nVVUbN27cvHlzeXl5KBTiH95kWbZare3atcvJyUlJSTnDPsgFI6Wrd47JzVrfUF1RKKWUqpQS\nSvnrFtXNQ6OIMYYQxhgAI8QoZQCAEGAsS9LptHzfnInEThCOtXbt2hBs4FMj+XJMac0BMFJD\nKOaZ1ZuezhvIGCOEAjB+KtbtdtNA2oABA8RvJeGErnnpDv7SSm3VIby/uk9m9rZICQDIpQFN\nGzsC7FMC20uLeRO4qqqqTx94J9oh/wNbSx6jYy9u/oldMFLyy+4rB2esOjqrOxohJBQKBYNB\nfkCKl/BP2Bb4tLbj8HMHKz/N7XKq6R3/pEGJotUZ/H433/N5771T3nzrbb/PNWhw7vr1hQCw\nbeuW9Iwufr8bIUBIkiUNbtoz3WcwsRQrCMfSaDRLfjg4cqyVD/CRZTkpNtmgNwLAy+MSAIAx\ndsfcpc+O668oCqV0325Px7bNvaGD0Bxc+ezNLVq0MJvNOp2uMlAdDtfMXfZ5HBgjgaCxb0fk\n9quEJIHt6IGk0Q75n9HItnC0YzghXqh7fop1cD4cN6sDAEmSTCbT6buL7l9Ia3VvWqt7f941\nHj4GgM/+dXqHMaK0tmSEeDcegLfeficpKQmh2lpSwO9iDGbNepev3AJQyihmuGnaa5/xRGIn\nCMdq3749osbCtXt79W5HCNHpdBqNxhvw6DUGqJsXOWPsOX6/3+/3V1a4inf4zh+acgZ/lBca\nisPhsFgsZrPZqNNTBH5CkntnAGWxu9yRLTVxGqOvs82z97Cxk11VVUmSGGNPf/ba/eNPm046\nzTyxcweKft134zHLr8LR+nb6DAAKnNltFl7GaGZR76LjpneLV3791U9v8JbUAMDb+73+0PcY\nYQDEGCCMggGvwWgBgIDfyxgLh8Ot27Tr2LEjAEDtZlGEETIYbQAQDvkZUKAYsPhFeqrEUqwg\nHIsxtmPHjjlz5lRWVk68McdoNOr1epWqBp0BACil9cs0H7y9zuFwXHHFFV26dBGJnXBCU+Y9\n6XA4Sjft6tA9vXTzrtQB2SChw99vaHV+j0hJVbC4gti0RMZQ7rOe2zFCVJ/PV1FR8erkJ6Id\n+MkqqZ7vGp7WPJdi/7yjTvh7Bc7sc1I+Majttw3aVv93+vz79x9ybZ960//QH5pkA++8OO2t\na++/5o2EFkmMMkIUAKjbRYcA/WX5mTGEMcZYwhiLit2pE4mdIBwHY+zw4cPffvut0+kMhUJ5\nl2Q6YgySJAEAIcTvj3z58WadTte5c+eRI0e2adNGZHXCyXjky+diYmJMJpNWq0UIYYzdOw+5\nt5Ykjcp2V1bHJrS0b/DVdDcRQkwuVqkLeTyeioqK5yY8Eu3AT1a5p6BiSEJzS+zK3Yv3HHm/\ne+vZl156qcjq/qkCZ/aQzNWufH/p9NJ3Lr3fZrNdNvwOCWOEEQJUf6CbAWOUEUrmff+yx+N5\n9YFvoxz3WUwkdoLwlwgh5eXlRUVFu3btqqysDIfDjDG9Xh8TE5OSkpKVlZWQkCDLYj+DcLKe\n/Pa12NhYntjx2QbbVqzPGNy7/gabftvQpXs3PpA0FAq53e7x6RcktmwVxZj/kWrfutJBxmaV\n2IlCXYMocGZ/832iw+Ewm83dOw3AEj/ZWpvZMWCHy8zlXk/3TuZ1zkU+ny/oizx996fRjvos\nJd6TBOEvSZKUmJiYmJiYm5tLCCGEAADvfSBKdMI/RSlNC7ZyyVSn0/FpdRjjnLzB/LVUs68M\n9FLf/v3cpRXGFnbe+Fqj0YR8QWgZ7dBPmka2ASjRjqLWgapPDtfk922/cuzYsSKrO0W5WYVr\n1l5jtVoNBsO+is2SJFsMdt6hDgAA2GI9GqTx7D0iWSwWSZIYc0U34LOZGCkmCCfGj8fqdDp+\nkEJkdcK/gwB4XzpZlrVarV6v57PpDAbD/iXrHHEWg8EQ2yZBp9Pxdf9t8xedXosqMrZGO4Ra\nBc7s5NjLH7/LZTKZvvrqq2iHc9p75K0rbTab1Wrlg3kMBj1BYQJhAiECIQLhC6RQJYHPQ9oV\nNr3FYrHb7U+8f320oz5LiYqdIAhCU8AYp6env/fee+lXnsMYI4To9fr6mXU9Wqcveesrh8kS\n3y89pnWCz+fb9/OO9om9iyovTWyTb9AmRjv8k6KRrABV0Y1he+kzCqnJiFl40UUXiUJdQ7Fa\nrRaLhe8iwBgzLaFm9Rv6oWcPJKV3CCNlaPXQpSjfVm27LGFkAPwAEA435xPSZzKR2AmCIDSR\nTp06DRky5IcPfki7JMcWZz96IKkcUftfP7ai5LAxxlZdXb32rcUtWrQYP3Fip073AECBM3to\n1jrU7NdYZCnKjd+O2lGXuGDBgugGcyYxGAwdO1r86EPkv6XG4Fz605rh2ZdcId8FbQB8AACg\ngTttk5mVEUIMBoOqqgaDIcpBn61EYicIgtBENBrNBRdcEBcX9913322tqIhJbtE2N1Oj0ZCQ\nogEfqaxUMFv+wldarbZr166jR49OSkri38intvOsJaqPoPlyHnxYwtZW6IOrrrpKFOoanE6n\nw8SnRAbZjUY1lHzdkH6IMcC/f9Lgewb4oEWMsaIoer0+evGe1cSpWEEQhCbFGPP7/du2bdu0\naVNJSYnf71dVFQBK3Z/0SL0zJSWlR48eycnJGs3xx5k08/SuoeaN/iPi6GtjW7bmm4Bht2Jy\nJpmG8LEoysEDOCZGY6ndVcn4LFhKFUUJBoNutzvdmpeamhrdsM9OomInCILQpBBCZrM5Jycn\nOztbUZRwOKyqKkLIFRzWusVwvjL7N98uqndH+3nnpa0cI+PU90ShrvEwxlSfRgrNV/a4D0cK\nO414DFPF1Dkd11XsWB3+SuYDdl0ucTA2Opr7jg1BEIQzFUJIq9VaLBaHw2G329u1GiFJcJJn\nruvTu0aOsVkrcGb3Tf38tklfdO/efc6cOdEO54yFEIqPj//+6x7WzFsTB95PCEEaPT/0g+tA\n3Tosr9it/LgkLi4u2oGfpURiJwiC0FwsLer7j26fm1WoEt9ZmN79vGt8ubsguHfqnXfeKQp1\nTSA1NTUrK+uLlzfU1NR4vV6/3x8MBsN1+IhFv9/v8XiqqqoWvLY5Ozu7TZs20Y76LCWWYgVB\nEJqPf7zpWZbMuVmFwUjp6p2jz5LF2aN31I0aNSra4ZwVDAbD+PHjZVle9NZvDMjo/3ThTT3r\na3WRSCQcDn/56ia9Xj9o0KCRI0f+1SZRobGJwxOCIAjNxSnunNteOrOk+ovopneNenhiqbNv\nr/bv/pBftHHjxhkzZjTSvQh/RVGUbdu2rVq1at++fYFAgFJa9WVV7MWxAHCo5sv0ttd26tRp\n4MCBHTt25B22hagQiZ0gCEJzoRC3RrKd4g9ZVzw5ENk7KH15g4T0TzVeYieOvjYTlFKPx1Ne\nXl5RUbFr2q6kKUllVKbkzovPW202mzEWW7yiTCzFCoIgNBcayVZS/UXrmHGn8kN6d5wFAAXO\nbIs+rU/K3AYKLZqWbe3XL3Xhik8ucMa9JLK6qMMY2+12u92elpbWv6A/AGTnO2e201mtzWWg\n3FlOJHaCIAjNyPbSmaeY2HF1x2Zz0hP/Lynm4lP/gdEiCnXNHFMZACAk0onmQpRMBUEQzli5\nWeuTYi4ucGa7A0XRjuUfW+rso6ie5fPO//TTT0VW12xVfVoFAO1aXBftQIRaIsUWBEFoRmzG\nrg3+M0/HnsaiUHe6KHuxDKZBx5Y3RDsQoZao2AmCIDQjOR1mNdJPPl16Ghc4sylT/zcjbd26\ndSKrOy0YZQwAjKnRDkQAEImdIAhCc1O496bG++HNPL3jhbrRo8bOnTu3d+/e0Q5HOCnP9GwD\nAOv3TI52IAKASOwEQRCaG5f/18a+i2aY3i119qY08uEzGU6nUxTqTi/ntLQAgCe4NdqBCABi\nj50gCKeIMQaMUkIoowwYAgQIEAOEMGDMKGVAebtMjBFCEsYnGHIvNJmG3XvHGEUIw79qTCt2\n1J2+5FgZAAIqjXYgQi1RsRME4ZQwxgilhKoqUYiqanUGNaIoRFGIoihhlSh79ux5+aWX1EhE\nVVRKCYim6CeSnvhwU95dblYhpZGGqN5RAEDyP8vaC5zZDNjb09sfPHhQZHWno06fdwKAIT9s\ni3YgQi2R2AmCcEoQAgBGKGWUMUYBgAHdt3efwWBmVC0qcj733PO33nozYMaAAmMMKKPiw/3f\nSYoZS5nSlPeIsTY3q9Ab2nEq6R1lBACwfOzbCqWUEJUQlRCFEEVVFUIIY5QxtnJb7tDMdVdf\ndfVnn32alJhIKaGUiHlIpxfJKgGAKv7Wmg2R2AmCcIoQAGCM4KhKTdu2ya1bt2YM0jun117F\nEAAQSillIJZiT2RZ0blNf6cWfVpuVuHBqs8KnDn/4tsZIwBwzFIsJaQunwsrSkRRIoSolCrb\nDj6jqpENP4z0+bz/+98sVYkQVVHVCCWEfzwQTiPKYQUAjNr20Q5EABCzYgVBOKFRVz2o1Rvj\nW8a88thNkiT5/ME7H33b5fa2cOjffu4+xiilfI8dYZQyOPpXCubLc/V/lCQsSTJCkhgo+fei\n3nOucM+N7sDmoVm/nPy3qNQnY/O24dvSF6fXX0kppUQhRKWUMv5iYCiolBp1Sbt3FXfqlAIA\nDBgw6JiSevhwWSgUkGWNJEkAIvs/bTiznddOg8K8RpkRLPxT4vCEIAh/6bdNW59+c+Ebz/wX\nIYQQKq90IwAG8PiUiYwxxuhl/5kx9/UpEpYAAANmjB31WfGYN2aGEAJACGFxeKL5y+7wDgCs\n2DbUpG2X0/H9k/kW9selWMYYf9kAwghjYBQoAFAArEEtHnn40ccem2YwWgAg4HMZzXa/z2Uy\n2xljjFKGxYvkNCM+qDUfIrETBOH4rr79aUnW/2di3r6SSoQx5m/SAADA+A4pSq+4aOh197za\nr2e7GyeOjW60Z5gBnZdEOwQAgEHpSwGgwJndNvbqTq3u/KubUUovfXSUJEkIIZbD8FTpk+kL\nEEL1uR0CjABFaKUGt/T7vGazBWNcn7kxgMsuG1+7ls8IMMwoRZJIFU4no5PtALC/Ym7bFhOi\nHcvZTizFCoJwfNfd+5rVajUYDFlpbSwaR4B6AIBX7HiTE0rplu0HAoGA1+ud9fztUQ73jHPY\n9U0r+8hoR/G7AmfOOSnzzPqUo68c90ieyWR6+78f1CZpda8Pytj1z1712u3vOmwxlNIN++7q\n0vqZxYsXD8vNRQCAgTEEjFJGjUZrIODBCENtQRcwljCWxWL9acSZ7cwqzIJmsIVAAJHYCYJw\nXNfe/XJMTIzJZDqwqSLtnGRXhSfT1NeYEArssFXZtsQkxVT5XOn29MLDhZFIxO/3V1VVffjy\n3dGO+ozSPN8jC5zZgzN+krAeAK6Zedm9lz2IJYwRL7/VFXSBL9TTVxY87/F4brykcmD6yttv\nv+3VV1+Fui2YjDIGwCgFxBAAwhLvgAiAEELAAInE7vSxtf/WjJ8yCGPLi3Ka4Yv2bCOWYgVB\nOA673W6xWAwGQ7dBjj0bStsmt9tc/Fsvb26RsiipRdJBZ2mFv8LU0RQJqGa7GWNMRQeTs0N9\nT+NPF6fHxcWV1ByQahM7VHvYmTEAoIxSymJiYnQ63ayv5QGd2euvv/GHH4QBGKOY8c2XCNAf\nzkqL/XWnlVZTWgHAA4UHzjdEOxRBJHaCIByXyWQymUw6rU6r02ad22nj0p09h6f56Lb26/tJ\nhvJY6BZ/zk4KNCmulaIoAMD/X2hAVkNGtEP4S/O+T4uJsZtMpo0HCuPtrYwW+6rior5J7RST\npyp2rY5KLXHG/g2qyWTiK6qUUenPIykQwuKExBnBMdoBAMvLvee3O96XGSOU8CouQggDAoz5\nYiHiq4aMMoYAAcYYYwmAidT+VIhatyAIx6HX6/V6vcFo2LJi977ifeeO7qbX6w0mg3mQZ/8S\n3+HQ3si6WIPBUHszg8FgEB/VG1jvjh9GO4S/ZLfbrVar2WzWGu1+1bvOVbkzg3jCrrk/JJb8\nhNeuKGKFYaPRaDKZrFar3W6/663/RDtkoXHREAUAhP6UVDBGKaFEVZXIvffep6qKoiqqEiFq\nhKgRVVFUJaKqyqNTH6FU5b2TRFZ3ikRiJwjCsRhjPGMzGo0DL+7dq09Pk8mkhll4v3bDgr29\nrmj3866lYFa0ss5stPCsTq/Xiw27DW7j/r88iBpF7y98i2d1NrvNapQXeAyZBpSh931kXTg+\n9rPYYs3Q1BsD8fE6C+W5ndlstlqt0Y5aaFw7L9oJAEMy1hxzPQPKKKGUUEZmzHicUpUQdfmK\nZW3bddDpjaoa0RtMhJJHHn6wuqqKUopA/Bo5VSKxEwThWH6/X5ZlnU7Hczuz2WwymSoOVSb3\ndlzwn3PMZvNND10d0x3ZY2w6g9ZgMGi1WkmSwuFwtAM/01R6V0c7hOPY695tNBoNBsPHGyJ6\nvT5QfsjaIqavv8c5tsRkrDq02+fMj9m6cLbmowX8ZgaDwWg0RjtqoXGpFSoAIHSc/V20rqkh\nMAAGCxfln9e/f1lZmd/vAoCA3wXAVELsdjuAGCXdAMQeO0EQjkUp9R5YFRt7kUaj0el0sizL\nstxnULYs126TYowZUgyfz/o674rh/NgEqSyk9JyoRi00kbweF687/JNer7/pPL1b1SendXmb\nbO9hPjJQGb3yvPV9yIQOP2zuf/5/KaXaN571XncHpVSn00U7aqGxrFz+5YolH2rztHdVdP7m\n61UlB7ZXVR4Oh8PTZy4E3pEcY5AkQkj/8wb/tHrlRWNGnX9h3ty5HyKErr/hpvfee+eG629E\nCI0ZM+aisaIdZgMQ7U4EQTiWqqrz5s37+eefzx15q9ls1uv1Go0GY1zfWoxSSghRFCUcDns8\nnp+/fSM3N3fs2LGS9KcN8sIp2F/5Udu4q6MdxbG8Xu/c397gW4AAACAASURBVN632+1Go1Gj\n0Uzfn/9YhzEVzh9aZuSCGgCdBSm+g75vq1zb05P+LxKJRIo2l1nsdwybMmrUKADIz8+P9iMQ\nGgal9OnHLv/PHS/XDZWpvZ4xAMYikfDbr//3kcfnU0oZo8AoowCIAQAD4D3Oa7tS83HTmCeB\nkhg6copEYicIwnEcPnx41qxZBw8ezBl+I8/tZFnmeRtjjFKqKEooFPL7/esXv5OSknLttdfG\nxcVFO+ozEGMEoeaVLpeUlHy798vY2Fiz2azVaudUr54UPwjVDSbh7ymMMUKIqqrBYNDj8fgL\nSbfE/ep93RL69erwbodNmzY99NBDXbp0mTFjRrQfjfAvvfDMjcmV+zMnPizJMkYI/XEyDf8t\nsXrlZ1VVFfc+NDe6oZ5tRGInCMLxlZeXf/XVV06nU1GUnkOv42uyfE6UqqqhUGjDslk6na5H\njx6jRo2KjY2NdrxnpmbYpriysvKll17KHNfRZrPxHZayLPN6C6ERCWt5QVdVVdehhUTfr6qi\n2lIaVyndYLGarh5Wumzrefpxd1iN6XxWQV5eHogy3umGUvr6C9fzyTRtO3THGKM/VuwYMEro\n3uLfgsGgy+W6+4Hme8T7zCMSO0EQ/pKiKPv27Vu3bt20adPy8vIUJcIYYIw1Go3dbk9NTe3T\np09ycrJYgW08zTCxI4QsXLhwyZIl/a7vxbtY89MzfKW+vqDLR5JUl9UULy658cYbO3ToAAA+\nn2/KtIEXXh7qlHh1SfUXmh96x867JfbS2Fb3t8rPz3/rrbdGjRp10003NXDEjFIGlBJglAEg\nwAjz0ResvmOahCUkFgFP2svPXOtwOEwmk0ajkWVZkmStVn9UmxIWCvkZY3y3hs/nq6qquu/h\nj6MZ8dlEJHaCIJwAY2zEiBH/eaQyp93XhBBJkoxGI99fJd4IG1szTOwAwO/3f/nll2vXrm3X\no02bnAS+C7N+pV5V1XA4HAwGf3q30OFwjBs3rmfPnse8VAoKCl566aW7ZpbxP3Zseat/RA4A\nNHgZj1IKwChRCaEtWsZ//dX8vueey+OMREJaWde+Y8qSJT+kpXWWJBljCY7eLCb8hQ/euctm\ns+n1eq1WiyVcPwyOf5XV4TtxA4FATU3N9be+HtWQzyIisRME4cSCweDq4vOaYYZxxgsr1TpN\nTLSjOI5IJLJp06alS5e+/fbb/Qak9hw2NKaTFSFEInTtRxsxxiaTqUuXLrm5uYmJiX+TKk2a\nNKmiouLZt/sfqPqYkkBu11+PPOWr+bomaVqSI8/xwgsvLFu2bMqUKQMGDPh3cTLGeO+0AQMG\nLVu2BIAhgB49++zdu7fs8AGdTg8AvbL7bNr4myRpEJawmFF7Iut/WXJgz0qr1cq33vJV+GP+\ninnhljEWiUT4auzl1zwdrYDPNiKxEwThBCZOnPjhhx9uPTg9o83UaMdyNqrwrGhhHRTtKI4v\nEAhUVlZ+tmRYS81DHo+HEKLX62NjY9u3b9++fXu73X7yeVJeXh7GeOHChYt+S1SI++IcPwA4\ns51wamU8xiilhKiqzR5TVVmG4PcGuJFIWKfVMQDER1lhjSSLIvSJbfh1+eEDaywWyzGJ3Vpp\nfR+Sw29Tf36CJ3Zut3vM+GlRjfosIvrYCYJwAtXV1RWeVSKri5ZNB+5ttrXS8ePHz/vi+Xuv\n21F/GPbPxZuTVJ+xvf1oz/T09ItzIH9D68ibNX06fQKQdfDRgzPLZrZ+sjUAzJgxY/Xq1Q8/\n/PA559S2Trxz2hidTpfTbXBuv0vdvqq35kxVFEWJkFce+wohjIAijF01VcuWLxs4cABGuLqm\n2mAwGHR6Bmhv8Z7ktsk6vR5LYnTtSenWY2B1+a983oxWq63tVAcwGAYCwD59ebtQfH1iJ8sy\nAIRCoSgHfTYRFTtBEE7ggQceyL2qoNnmFme8E22zY5QyhBgAZoxhjBgDhFBt3xFgUHdGAGNo\n2GlDPI1rvF2AeXl57777bqtWrQqc2d7QDos+jd/R0WW8/z5+8f03v8ybn/F9XsAggMJaJkVA\nvb34moG/9bvrhicZo0fPIUVQ/6wAQgAMY4zE4YmT5Ha7N62b7XA4jEZj/bmZY546vg7Ld1vy\nwxODzr+bJ3lCYxPPsiAIf6e4uHjmzJkFzpxoB3L2Mus7/dWXeLLCD30yBhghxiT+BQBEGQWg\njFEAhLHEGJakhkzsRo0aNf+rjxov4+c1vM8+++zDDxPy8wsBoMCZzSig2dCt7QsA8NBzE+Lj\n48sq9/HELqyCXoMAQLFoZU8ooiMPt5z+RKv7Dzy+7cVHFwCI09sNIxKJ/LDiyJWXxGo0mvoO\nl3V5Naqv3aqqqigKpRQhtG2P/jxCRGLXNETFThCEv5OXlyd6jDVXjBBCiNqxY6fx48e98MJL\nwYAXEEIMAa59e3355VcfeOBBAAiH/RLWYElu2KJUUx7aHT16NKU0Pz/fG9q9dvflKxen8+kX\nqe26Y4wRxgFq36I52CYxKfGgBxijjBYfcAaDAa/X26Zl1sRxdzZNnGe8YDD43nvvpXdkrZNa\nmEymY3basdpdjeTeR3813Oi4RTK88f7PF1xwwahRo0RBtGmI4z+CIJxAgTM72iGc7TYduO/P\nV1LKGGMAVFEiu3bu8vtclBLG1ITERL/Pz/6fvfsMjKLqGgB87p2Z7dnddAIh1BAgSzX0KlUk\nCoggTcGG7QW7LzbEDlZe/bCioCIqKEUiooKgSA99qUIgCYGElM32MjP3fj9uskZARAQ25T76\nI5lsZs4um92zt5xDFb0+aup/7vV7y/0+NwIB0KV8wc/MzKRUvZIT9N9++y37jDH2xgfWrEy1\nWq0mk0mv1xeW5ZwqPVrsyVMDRw/v3qDbd8AnO2avXVNYmqPTaY1Go9lsPn5q9xWLs9bT6/WD\nBg26Zd6e/733q8PhcLlcHo/H5/P5K/l8Pq/X+/j9je8k4py5G5s0adKjRw+e1V0xfMSO47i/\nUT1LqdUp323roi2aozWIvfq1Dx8kpKLFw6uvvvbQg1P9fr/BYKCUfvTx/I4d2nXs2NFgtHg9\n5UaTNeD3CqKIsXhpa3lE8Inxyof3sgK5Go2GdTFmecPxkmRtiiPJ54XKZV6s953T6Vy77Kgq\n02+//TYiAdcmXb6zbxjSOufIkW+++SY/P79Th/rt29RnDUigSiHDDz7ZptFobDbbDTfcEB8f\nH+mo6xCe2HEc95f4PGxkjb/meY2km/7GzY2bJ4YPThk3x+X0fPLdfxFCqqpSoqpEJUQFylop\nIIQoBUAUDCaL3+titdkEUWLVdy+JiD8x3vniMTYPG96ViRASBEFV1Xe+Ip1uwBliRY1clmS4\nXK6ygvIHJ74pRAmjR4/2+XyLFy/W6/URvAs1VEaWPTvTxr72+Xy7d+/eunXriRMn/H6/qqoA\ngBASRdFkMjVr1qxbt26pqamSJEU05DqHr2TkOO58Vts7D7BtjXQUdQ6l9M4Rb77w9p0IYYRQ\nfk4JsJksSh99YTQl9L93fFx8uvSj5Y9gQcQIEUEgKqFAESUAmCKKKA34vYIgAMIYoSrtni6B\n5cuXR3YcV6fT6fV6vV4vSRIbsdObQpJA5xctgECv3uaW4eYHsiwjhEKhkNZKhSgBAKbnTAcA\nltXxTrUXbn2h+6vcsnBWBwAGg6Fbt26dO3f2+XxOp9Pj8SiKotFozGYzayPLp18jgo/YcRz3\nl1RVXXugC5+HvcIKT5a88NAik8k0dFQnNs0If/RXZ6VfiaKqK77c4nQ6P1z6IFQU+mfjJVBR\n5oQCqwCCzipF8S9FfLgOAD5f+WJMTIzJZAondk7pY6tye5FacHRbTPeueqjStdbv97tcrmu7\n3avT6X49OCiklPVptVoSrAcGHFDL1ZY/txTN4pgxYzwez5dffmkymSJ716qnqgN1XDXHEzuO\n485txIgRS5cuVYhXxMZIx1K3TBn7rtVq1ev1jVMT9IKRSLJeMjpPerXxAABGEuOCkpyDRaxC\nWFlZ2dxlD13J8Hw+38ac3hFM9wkhi396PS4+JioqSqvVSpKkEL9GNKqh46GQxRAVA5VZHetV\n6vV6HQ5H59TR9erVC5/EHzq54fD1ek1yjxbLQidCh4cf1iRrWixrcfLkycmTJ9evX/+DDz6I\n1B2sbnhWV7PwqViO485NluVfDgzs0+qnSAdSt9wz6u24uDiTyaTVasuKAnmbT9ZrHmuID6Y4\n2h4o2GyM14r7ooMtvQaDgQ1WXeEP52y4rn/6tit50TMQQnwn41ACFUWRJXZG0QiIYmP6b99M\n6Tny7XAHelVVKaUY4/1bHB0ay1VPotfUD+emv5Z3h/nAvi27vmwmzLRlXXwTs0uFEMJac7FV\nkxgDpaiiJRqltKJANCBg0/WXpcZF95X71g1uxbO6moWXO+E47tw++OADWXVEOoo6x2q1RkVF\nRUVFGY1Gg8GQ0N7YUu2Zn13228FfoktSJVlq2bTh3p+PGAwGVsjDYrF88ObyKxbe3LlzV9sz\nIrt2ShCEZs2aLfloh7e01Ofz5Tz9iBwMEpUSQrqP+B8bqFNVVVEUv9/v9XrLSh0CNURHR//V\nCQfYsllWF5ALC+dPci58EgCC+cGZhTPf1L0JAHl5eZmZmXffffcVu4+UEgCqKrKiBBUlqCiy\nIsuEKIoqE1VWVFlRQqoaUhRZVRVKCCUELnWKn5Fl33htuuaSFrXmrgA+Fctx3DlMnTr1rbfe\n2ps3rU3KzEjHUre88MAS1qwpvHrsxG+u+hkWmqvzFwv5vsPU6m/XumVAH1BVNRgMut3u4uLi\nlz+8+QrExobrCJExjvA+R6/Xu2DBgl27do284yoUDDiXzo1NjjnV29ahwdAg8YtUoyhKKBTy\n+XyP3f1Wn579U7t+d/vwvf8oH2XlG89uYnZlhvEIIURVFFWmRDUYLX6fi1BAABiBSmnluB2w\nTmiiKAqCdBH7Y9h6TdZfDWGhsmkEKfTL/3ew6MWOyQAAgCr7//KdEDUDn4rlOO4ccnJySj2b\neVZ35Wm1Wq1WW7W9utFIDq4+mZRuCEb7O/ZvRikApnqiVxQFYyzLsk6nuzKxzZo1q5oUNTQa\njePGjbNYLN8t2OL3+2+4/V7QahuKYllZGaVUVd2hUGjbury8I8XDMkeuWrVq5sy9a/Z1+keR\nh2/sC+YXzp9k0DYG+Dp4LDizcKamiQYAjh8//p///KdJkyZvv/325biPLJFiyRYhBCF49NHH\nX3nl5R9/+HH0mPHO8hIAqjdY/H4vpWyvzD87OyGEUpWoKquSA0hBCIBA1omyIQ2sz7aNV2SZ\nAiAEGGFBEAFd4l043GXCR+w4jjuHRx555JpJ66rDW3hd89b01bGxsSaTKdxefcvqXSmBDg0y\n8N7f99h620o2qTFdECvkwfZ7jronPSoq6nIHxobrFNUrCtVlMw0h5MSJE1u2bDlw4IDD4WCd\nSQFAFEWDwZCcnNypU6c2bdoYDAYW/L/PStfYO1Mg/dO3IYT2dd5HCU3flo4QurhhPLfb+dKs\ncaIosk5csiy/9PwKVm6QUqKqiqoqhKgGg9nrKQ8E/N179NmyeaOkEdvYOuzdu1tRFYslJhj0\ni4KIsfBPMzs2ZU1VWSWkXfur9uzeDhRO+IIfvDLz2Wems8wAYWQwmAMBvyiKwj+/BBcRPLHj\nOO5Mubm5jRo1qiZjM3UKIWTRl0uQI42VAZMkiQ3a5S6WkS4Uf7W4e1FBq+vjo2KMrMO6z+cr\nKyvre2NCo0aNLndsy5cvNzZ7vho+JVhK5PF4PB5PMBgUBMFgMERFRen1+qqdNl599dU77rhj\ne8HgS1KX8VT5yn0npidZr0tPfsbxnaPgmYLo66MbTG/w448/vvXWW4MGDZo6dep5fv3xp4aa\nTKZHHpxf8X3F3CoAwCuv36LXxz/ywByVqJSohBI2YTpgwOANGzb6fW6EMLs9qujOigVBvIjN\nE5SqbIkeIWr79hmKou7du9Nksj766MMznnnaaLJ6veXffL101KgbBVESReky7c/gLjme2HEc\nd6bqUKisbiKELF68mBa3YsvstFqtKIqC8KeOEUQlKqnYGeByudYfeL/DwB/HD95zWQNjT4mA\nXKSTEv/+1tWVqqqTJ09+8uXOSdahek3ypTrtP12N9/xLo5o3a9Ot63B8VolBVqVl6fLZPp/v\nif8uZDOxlBJCKFACABgLbB8sVHQZQVCxAO4v/eetKcdOHwMADHjFC390VFNVlaiKSmRCaJu2\nHfpf3Xf27NeNJuujjz787LNPGwxWn9cZG1evvLxUECSMBT4PW1PwxI7juDNlZmY+MLOwGo7N\n1AUfLrUdWD+oc9NJFotFr9drtVo2Ict+Gi66y4rYrdvzYZNmyXfccYfBYFht7yRiU9/Way9H\nVLNnz7YNWFA7nhIsSV2zr2v/9M2X9sx5JQsPF77RKO6W1HpTi+cVF80pir81PvG+xIULFy5c\nuHD8+PFjx46d+eo4q9Xatk0fndaIMJYkUVVVjAVBxHJIJoQSoh44uMHtdpWXlz/+2EKo2CFb\n8WaNEVRWnv4bY58dp0jKl9O+OOP4PXPuPV12etkzSymlhCghRf6tyNU7wbR9x44OHToajWaf\n1wWI1VEBACQIwsWNCHKRwhM7juPOITwPyxYtUaqyglmVrxgEACEkIATh3k0AtLLTAeZvAxdh\njb1bh8Zvb1lf/Morr7Rp06ZlgwFJMWl6vZ4ttmPv5YQQ1vzU6/X+vOf/kpKSJk2aVHUe9lR5\n1r4TMy5tBsYyIZffbtbXknpml2rJ3Xmccxjv5VfGsoYZWq1WkjQaakpJbl7iLdQKgt8rawS9\nIgYc5adY4u52u0NBdP+Udy7i6qNeGv2f6+9LjE5k07WVG1op61xCCHlp0ctmHJVnm7K8Xypl\nhyip+icMAJQC+wLxbRM1Ct8Vy3Hcn7D3vH7pW6By35yqKoRQhChCmBLCii0gjDFSARAFQikB\nCrSiLgJGSIQLG1TgwliSwR78gQMH7tixY9WqVfadqzSisWuLm9liOwBgVTx+O/iBTqfr1KlT\nZmZmQkJC1fMkWTOTrJnshL1b/qARY/99bC1atKhlCy6zsrIyMzOzsrIv3/0Knzbn9NzC+e81\nTbgbwGa1Ws1ms8FgMBktVKEEy8d+O9YgudFO++4WPRrkbyvUxSJdik6j0bBiNw7HxRSSHP/a\nhLi4OK/izS3Lw3+eqKUAlBBCaXR0tNfrTdz9Oh7wISFEkARCKKKUIp7G1Xh8xI7juD/JzMx8\naFZZv/SNAECIQlRl5fc/DBx4NaVQr16DwlMFbrf71/W/Dh06NDs7u3OnzkCJ3mjx+cqBAgDG\nWBBFCWGh6rp17jzCKd3ChQvNZnP4uNfrPXz48O7du/Pz81l7dYSQVquNiYlJS0tr165dUlKS\nKP7Nh/Of93UnNPRvcheWa54qX5VkveaiT1I9jR49etGiRavtnQbYrlAjjQVfPMZm2CVJ0ov6\nU78SCi5KDUSA6GTJeTyoz5ADPm90jIXtjHE6nUlt/5hbb5fyZmxUV4zOV0Rw3KvjY2JijEaj\nVqttHN8IADXWG38vPa01RSFCCEKU0rzSPEVR2Pk/+s/cy363uSuLJ3Ycx/1JMBhc/3uPAbZs\nNj9DSEijMXi9TlbXPiGxwemigu++Wznt8af27tnBfsVosvq8TgC6c+fuq67KEESRL8q5EEeK\n3itxrbuq8WcjRoz4q90qlFK2AZYldpIkSZJ0xnaKv6WonnUH+l5cejd+/PhbHz9Um4brqsrP\nz9+6dWu9Np91b7EEXf5WTEuWzzCbzTqdTpIkrTe6ninmyIncsmAZLcayJhBPm4j1/LheEGOB\n1bJxOp0zpdEAMCDJMqpxzFWxZxaacfn3HS/+5LTrFwC14hKburDcUaPRiKLo/HxB6tDMg6fc\nMWkNipeviB8xjP1ds6Ffj8dTWlo6//55l/uOc1cSn4rluLru6ZuGCQhYrsCapj+x8BuomFcF\nBILf52naLHXEiGGvvTrL6/WeKCgAQDk5Obt27e7QoT07CQV4+ukZb731f16PkxepvxBsoO6B\n27OysqTz7EEOJ3P/5lqiYGKZ2Wp7RpP4O5sl3nWBvzhs2LDly5f/XvjWv7l6ddawYcOGDRtm\nZs5butx7oOCFtimzLuvlWOlpNmLnCZVsz8n1y8ETpfl9evUg5fjA4QPpKc3cAWI1mthYbCAQ\n+OxNwdTdVP/B+oL1T9m8Qui2Es+7h7T7nbcA3MIONj+1qLFZNplMOp1OFEWNLOtuvyOIMd35\nHTq0WWnWkhw9GtWggWw2E0LYk0qW5bPj5Go0PmLHcXXXc+NGTJn9rsEUdUYeRih5/a6JTy34\nhlYsq1ZZLS0AAEQQxYQShDEARQgIQQiFX0YQxhhjEWOe2/0lNvc3ffp0h8Nx2ZoWnM+m30d7\ng8cuZP6x7uyPvgLbKYLB4PqNc8L94o4eyWnRIhULGABO5Z5OapQAAEFfSNKKKlFDoZDX63U4\nHCLq2K9fvzNORSktmV9SNKfojOMf3PNhXEJcVFSUVqtFfv+J335rNWLkwSVfN7322u0fvpcg\nacvS0tv06EoIoZSylmvl5eUvj37pMt1lLiL4iB3H1UXzXphelpsz4b9PO4uK3MWnoXKxNGsV\nSSkd99hTb0y+2eV2T/98iSCIFNPKIqoIAARKEUYs0xNF9nuIUlpRXgt4VnduKgms3d9rgG1b\nZCsFdktdxL5Ybc/on74FoXNP7N54441ZWVk7j0+5gqFFzBXYTuH3++UQEgRBo9HodLr2HdqF\nQkG93gAAglZwOz1+T6BxagorZwMAgUDg+FFhwoSuZ5zn1KlTH3zwgd1u99fzVz0uCEJbXTs2\nIqjVasWoqNY33rh14Vddxo/ZvmhRgkw7KX73rh2nU1KiU5sSQgRBoJT6/X7gahee2HFcnbPj\n13WeUydiY2MdBfkCxlqNgYQIxoKMAqwzJaVEVUlsbKxGo3nlronTPvzsnJvkzjhY+S1P6c6N\nZQyzpyX2XaZUk/rP4flZsz69c7NPzvhpIBCoZZthz+9y53Yajca+R4mJDRoMBkIIQshoNMmy\nrNfr1RBJadWQEKKqKisexIraqKru7P0xSUlJzzzzzDkv4XK55u2aH57txRj3m3w7UNrwVOGB\npo33h0LO2Dj5eE5y+zaKogiCQAjRarWX/J5ykcUTO46rc3778hOr1WowGE7s3WmUG8ku1WSj\nRrB6oRwqS92Xny40mUwYY3A6Ix1vjbf+0DVWw1VXt9pSPVt6sCSmyPXz3rzHwgnNxIkTs7Ky\nfjt0XURDu9KysrI+/PDDSZM2XY7cTq/Xd+vWbeFni8dOSDebzaqqajSaoCeEMW7eqnEwGGRj\ndbIse71el8v19VeHJ06cqNFoLvwSc+eWaboIkiRptVq2eYI1HWvz0ottKF385puDW7TUpLcS\ndDpFUQAgEAgkFzdQVfWfbsfhqjO+bY3j6pYlc2ZbrdaoqCij0Wg0Gk1YE9VY9m+iTvcpxesu\n+rUssEEne90Gg8FgMJjNZqvVOvvuiZGOugZbbc/olbbq8Xt2C4JQDbO6sERzv/AAXkAuLC0t\nXW3v1DNtRaTjutLuvPNOSZLemZ5S4Mhy+w9dwjMjhLp06XLNNdd8+fn+awdPKykpKSsrkyHk\nquR0OsvLy0tKSh596LORszYPHz7cZrvQitALFgAA2O3PvXPnHFVVAUAQBFEU2bSvVqvV6nQT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A6vIdAHYJGwgEPD5fO9/vzUhIeGWUcNMJlOko65hWrVqdcMNNyxbsszlcnWafEfV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\nU2wAAAAASUVORK5CYII="},"metadata":{"image/png":{"width":420,"height":420}}}]},{"metadata":{},"cell_type":"markdown","source":"### 4.4 Gene Enrichment Analysis"},{"metadata":{"trusted":false},"cell_type":"code","source":"dbs <- listEnrichrDbs()\nprint(dbs)","execution_count":25,"outputs":[{"output_type":"stream","text":"    geneCoverage genesPerTerm                                       libraryName\n1          13362          275                               Genome_Browser_PWMs\n2          27884         1284                          TRANSFAC_and_JASPAR_PWMs\n3           6002           77                         Transcription_Factor_PPIs\n4          47172         1370                                         ChEA_2013\n5          47107          509                  Drug_Perturbations_from_GEO_2014\n6          21493         3713                           ENCODE_TF_ChIP-seq_2014\n7           1295           18                                     BioCarta_2013\n8           3185           73                                     Reactome_2013\n9           2854           34                                 WikiPathways_2013\n10         15057          300               Disease_Signatures_from_GEO_up_2014\n11          4128           48                                         KEGG_2013\n12         34061          641                        TF-LOF_Expression_from_GEO\n13          7504          155                               TargetScan_microRNA\n14         16399          247                                  PPI_Hub_Proteins\n15         12753           57                        GO_Molecular_Function_2015\n16         23726          127                                         GeneSigDB\n17         32740           85                               Chromosome_Location\n18         13373          258                                  Human_Gene_Atlas\n19         19270          388                                  Mouse_Gene_Atlas\n20         13236           82                        GO_Cellular_Component_2015\n21         14264           58                        GO_Biological_Process_2015\n22          3096           31                          Human_Phenotype_Ontology\n23         22288         4368                   Epigenomics_Roadmap_HM_ChIP-seq\n24          4533           37                                          KEA_2013\n25         10231          158                 NURSA_Human_Endogenous_Complexome\n26          2741            5                                             CORUM\n27          5655          342                           SILAC_Phosphoproteomics\n28         10406          715                   MGI_Mammalian_Phenotype_Level_3\n29         10493          200                   MGI_Mammalian_Phenotype_Level_4\n30         11251          100                                       Old_CMAP_up\n31          8695          100                                     Old_CMAP_down\n32          1759           25                                      OMIM_Disease\n33          2178           89                                     OMIM_Expanded\n34           851           15                                         VirusMINT\n35         10061          106                              MSigDB_Computational\n36         11250          166                       MSigDB_Oncogenic_Signatures\n37         15406          300             Disease_Signatures_from_GEO_down_2014\n38         17711          300                   Virus_Perturbations_from_GEO_up\n39         17576          300                 Virus_Perturbations_from_GEO_down\n40         15797          176                     Cancer_Cell_Line_Encyclopedia\n41         12232          343                          NCI-60_Cancer_Cell_Lines\n42         13572          301       Tissue_Protein_Expression_from_ProteomicsDB\n43          6454          301 Tissue_Protein_Expression_from_Human_Proteome_Map\n44          3723           47                                  HMDB_Metabolites\n45          7588           35                             Pfam_InterPro_Domains\n46          7682           78                        GO_Biological_Process_2013\n47          7324          172                        GO_Cellular_Component_2013\n48          8469          122                        GO_Molecular_Function_2013\n49         13121          305                              Allen_Brain_Atlas_up\n50         26382         1811                           ENCODE_TF_ChIP-seq_2015\n51         29065         2123                 ENCODE_Histone_Modifications_2015\n52           280            9                 Phosphatase_Substrates_from_DEPOD\n53         13877          304                            Allen_Brain_Atlas_down\n54         15852          912                 ENCODE_Histone_Modifications_2013\n55          4320          129                         Achilles_fitness_increase\n56          4271          128                         Achilles_fitness_decrease\n57         10496          201                      MGI_Mammalian_Phenotype_2013\n58          1678           21                                     BioCarta_2015\n59           756           12                                     HumanCyc_2015\n60          3800           48                                         KEGG_2015\n61          2541           39                                   NCI-Nature_2015\n62          1918           39                                      Panther_2015\n63          5863           51                                 WikiPathways_2015\n64          6768           47                                     Reactome_2015\n65         25651          807                                            ESCAPE\n66         19129         1594                                        HomoloGene\n67         23939          293               Disease_Perturbations_from_GEO_down\n68         23561          307                 Disease_Perturbations_from_GEO_up\n69         23877          302                  Drug_Perturbations_from_GEO_down\n70         15886            9                  Genes_Associated_with_NIH_Grants\n71         24350          299                    Drug_Perturbations_from_GEO_up\n72          3102           25                                          KEA_2015\n73         31132          298                    Gene_Perturbations_from_GEO_up\n74         30832          302                  Gene_Perturbations_from_GEO_down\n75         48230         1429                                         ChEA_2015\n76          5613           36                                             dbGaP\n77          9559           73                          LINCS_L1000_Chem_Pert_up\n78          9448           63                        LINCS_L1000_Chem_Pert_down\n79         16725         1443  GTEx_Tissue_Sample_Gene_Expression_Profiles_down\n80         19249         1443    GTEx_Tissue_Sample_Gene_Expression_Profiles_up\n81         15090          282                Ligand_Perturbations_from_GEO_down\n82         16129          292                 Aging_Perturbations_from_GEO_down\n83         15309          308                   Aging_Perturbations_from_GEO_up\n84         15103          318                  Ligand_Perturbations_from_GEO_up\n85         15022          290                  MCF7_Perturbations_from_GEO_down\n86         15676          310                    MCF7_Perturbations_from_GEO_up\n87         15854          279               Microbe_Perturbations_from_GEO_down\n88         15015          321                 Microbe_Perturbations_from_GEO_up\n89          3788          159             LINCS_L1000_Ligand_Perturbations_down\n90          3357          153               LINCS_L1000_Ligand_Perturbations_up\n91         12668          300          L1000_Kinase_and_GPCR_Perturbations_down\n92         12638          300            L1000_Kinase_and_GPCR_Perturbations_up\n93          8973           64                                     Reactome_2016\n94          7010           87                                         KEGG_2016\n95          5966           51                                 WikiPathways_2016\n96         15562          887         ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X\n97         17850          300                Kinase_Perturbations_from_GEO_down\n98         17660          300                  Kinase_Perturbations_from_GEO_up\n99          1348           19                                     BioCarta_2016\n100          934           13                                     HumanCyc_2016\n101         2541           39                                   NCI-Nature_2016\n102         2041           42                                      Panther_2016\n103         5209          300                                        DrugMatrix\n104        49238         1550                                         ChEA_2016\n105         2243           19                                             huMAP\n106        19586          545                                    Jensen_TISSUES\n107        22440          505 RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO\n108         8184           24                      MGI_Mammalian_Phenotype_2017\n109        18329          161                               Jensen_COMPARTMENTS\n110        15755           28                                   Jensen_DISEASES\n111        10271           22                                      BioPlex_2017\n112        10427           38                        GO_Cellular_Component_2017\n113        10601           25                        GO_Molecular_Function_2017\n114        13822           21                        GO_Biological_Process_2017\n115         8002          143                       GO_Cellular_Component_2017b\n116        10089           45                       GO_Molecular_Function_2017b\n117        13247           49                       GO_Biological_Process_2017b\n118        21809         2316                                    ARCHS4_Tissues\n119        23601         2395                                 ARCHS4_Cell-lines\n120        20883          299                                  ARCHS4_IDG_Coexp\n121        19612          299                              ARCHS4_Kinases_Coexp\n122        25983          299                                  ARCHS4_TFs_Coexp\n123        19500          137                           SysMyo_Muscle_Gene_Sets\n124        14893          128                                   miRTarBase_2017\n125        17598         1208                          TargetScan_microRNA_2017\n126         5902          109              Enrichr_Libraries_Most_Popular_Genes\n127        12486          299           Enrichr_Submissions_TF-Gene_Coocurrence\n128         1073          100        Data_Acquisition_Method_Most_Popular_Genes\n129        19513          117                                            DSigDB\n130        14433           36                        GO_Biological_Process_2018\n131         8655           61                        GO_Cellular_Component_2018\n132        11459           39                        GO_Molecular_Function_2018\n133        19741          270           TF_Perturbations_Followed_by_Expression\n134        27360          802                          Chromosome_Location_hg19\n135        13072           26                 NIH_Funded_PIs_2017_Human_GeneRIF\n136        13464           45                 NIH_Funded_PIs_2017_Human_AutoRIF\n137        13787          200          Rare_Diseases_AutoRIF_ARCHS4_Predictions\n138        13929          200          Rare_Diseases_GeneRIF_ARCHS4_Predictions\n139        16964          200    NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions\n140        17258          200    NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions\n141        10352           58                  Rare_Diseases_GeneRIF_Gene_Lists\n142        10471           76                  Rare_Diseases_AutoRIF_Gene_Lists\n143        12419          491                                   SubCell_BarCode\n144        19378           37                                 GWAS_Catalog_2019\n145         6201           45                           WikiPathways_2019_Human\n146         4558           54                           WikiPathways_2019_Mouse\n147         3264           22                 TRRUST_Transcription_Factors_2019\n148         7802           92                                   KEGG_2019_Human\n149         8551           98                                   KEGG_2019_Mouse\n150        12444           23                             InterPro_Domains_2019\n151         9000           20                                 Pfam_Domains_2019\n152         7744          363     DepMap_WG_CRISPR_Screens_Broad_CellLines_2019\n153         6204          387    DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019\n154        13420           32              MGI_Mammalian_Phenotype_Level_4_2019\n155        14148          122                                UK_Biobank_GWAS_v1\n156         9813           49                                    BioPlanet_2019\n157         1397           13                                      ClinVar_2019\n158         9116           22                                       PheWeb_2019\n159        17464           63                                          DisGeNET\n160          394           73                              HMS_LINCS_KinomeScan\n161        11851          586                              CCLE_Proteomics_2020\n162         8189          421                                 ProteomicsDB_2020\n163        18704          100                       lncHUB_lncRNA_Co-Expression\n164         5605           39                     Virus-Host_PPI_P-HIPSTer_2020\n165         5718           31                       Elsevier_Pathway_Collection\n166        14156           40                    Table_Mining_of_CRISPR_Studies\n167        16979          295                        COVID-19_Related_Gene_Sets\n168         4383          146                              MSigDB_Hallmark_2020\n                                                                         link\n1                    http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/\n2                                    http://jaspar.genereg.net/html/DOWNLOAD/\n3                                                                            \n4                              http://amp.pharm.mssm.edu/lib/cheadownload.jsp\n5                                            http://www.ncbi.nlm.nih.gov/geo/\n6                                http://genome.ucsc.edu/ENCODE/downloads.html\n7                         https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways\n8                                 http://www.reactome.org/download/index.html\n9                     http://www.wikipathways.org/index.php/Download_Pathways\n10                                           http://www.ncbi.nlm.nih.gov/geo/\n11                                          http://www.kegg.jp/kegg/download/\n12                                           http://www.ncbi.nlm.nih.gov/geo/\n13  http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61\n14                                              http://amp.pharm.mssm.edu/X2K\n15                 http://www.geneontology.org/GO.downloads.annotations.shtml\n16                             http://genesigdb.org/genesigdb/downloadall.jsp\n17                   http://software.broadinstitute.org/gsea/msigdb/index.jsp\n18                                               http://biogps.org/downloads/\n19                                               http://biogps.org/downloads/\n20                 http://www.geneontology.org/GO.downloads.annotations.shtml\n21                 http://www.geneontology.org/GO.downloads.annotations.shtml\n22                                   http://www.human-phenotype-ontology.org/\n23                                         http://www.roadmapepigenomics.org/\n24                           http://amp.pharm.mssm.edu/lib/keacommandline.jsp\n25                                      https://www.nursa.org/nursa/index.jsf\n26                        http://mips.helmholtz-muenchen.de/genre/proj/corum/\n27                           http://amp.pharm.mssm.edu/lib/keacommandline.jsp\n28                                            http://www.informatics.jax.org/\n29                                            http://www.informatics.jax.org/\n30                                        http://www.broadinstitute.org/cmap/\n31                                        http://www.broadinstitute.org/cmap/\n32                                              http://www.omim.org/downloads\n33                                              http://www.omim.org/downloads\n34                                  http://mint.bio.uniroma2.it/download.html\n35                  http://www.broadinstitute.org/gsea/msigdb/collections.jsp\n36                  http://www.broadinstitute.org/gsea/msigdb/collections.jsp\n37                                           http://www.ncbi.nlm.nih.gov/geo/\n38                                           http://www.ncbi.nlm.nih.gov/geo/\n39                                           http://www.ncbi.nlm.nih.gov/geo/\n40                             https://portals.broadinstitute.org/ccle/home\\n\n41                                               http://biogps.org/downloads/\n42                                              https://www.proteomicsdb.org/\n43                                  http://www.humanproteomemap.org/index.php\n44                                               http://www.hmdb.ca/downloads\n45                                ftp://ftp.ebi.ac.uk/pub/databases/interpro/\n46                 http://www.geneontology.org/GO.downloads.annotations.shtml\n47                 http://www.geneontology.org/GO.downloads.annotations.shtml\n48                 http://www.geneontology.org/GO.downloads.annotations.shtml\n49                                                  http://www.brain-map.org/\n50                               http://genome.ucsc.edu/ENCODE/downloads.html\n51                               http://genome.ucsc.edu/ENCODE/downloads.html\n52                                            http://www.koehn.embl.de/depod/\n53                                                  http://www.brain-map.org/\n54                               http://genome.ucsc.edu/ENCODE/downloads.html\n55                                     http://www.broadinstitute.org/achilles\n56                                     http://www.broadinstitute.org/achilles\n57                                            http://www.informatics.jax.org/\n58                        https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways\n59                                                       http://humancyc.org/\n60                                          http://www.kegg.jp/kegg/download/\n61                                                    http://pid.nci.nih.gov/\n62                                                  http://www.pantherdb.org/\n63                    http://www.wikipathways.org/index.php/Download_Pathways\n64                                http://www.reactome.org/download/index.html\n65                                           http://www.maayanlab.net/ESCAPE/\n66                                     http://www.ncbi.nlm.nih.gov/homologene\n67                                           http://www.ncbi.nlm.nih.gov/geo/\n68                                           http://www.ncbi.nlm.nih.gov/geo/\n69                                           http://www.ncbi.nlm.nih.gov/geo/\n70                                    https://grants.nih.gov/grants/oer.htm\\n\n71                                           http://www.ncbi.nlm.nih.gov/geo/\n72                                          http://amp.pharm.mssm.edu/Enrichr\n73                                           http://www.ncbi.nlm.nih.gov/geo/\n74                                           http://www.ncbi.nlm.nih.gov/geo/\n75                                          http://amp.pharm.mssm.edu/Enrichr\n76                                            http://www.ncbi.nlm.nih.gov/gap\n77                                                           https://clue.io/\n78                                                           https://clue.io/\n79                                                 http://www.gtexportal.org/\n80                                                 http://www.gtexportal.org/\n81                                           http://www.ncbi.nlm.nih.gov/geo/\n82                                           http://www.ncbi.nlm.nih.gov/geo/\n83                                           http://www.ncbi.nlm.nih.gov/geo/\n84                                           http://www.ncbi.nlm.nih.gov/geo/\n85                                           http://www.ncbi.nlm.nih.gov/geo/\n86                                           http://www.ncbi.nlm.nih.gov/geo/\n87                                           http://www.ncbi.nlm.nih.gov/geo/\n88                                           http://www.ncbi.nlm.nih.gov/geo/\n89                                                           https://clue.io/\n90                                                           https://clue.io/\n91                                                           https://clue.io/\n92                                                           https://clue.io/\n93                                http://www.reactome.org/download/index.html\n94                                          http://www.kegg.jp/kegg/download/\n95                    http://www.wikipathways.org/index.php/Download_Pathways\n96                                                                           \n97                                           http://www.ncbi.nlm.nih.gov/geo/\n98                                           http://www.ncbi.nlm.nih.gov/geo/\n99                         http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways\n100                                                      http://humancyc.org/\n101                                                   http://pid.nci.nih.gov/\n102                                         http://www.pantherdb.org/pathway/\n103                                     https://ntp.niehs.nih.gov/drugmatrix/\n104                                         http://amp.pharm.mssm.edu/Enrichr\n105                                              http://proteincomplexes.org/\n106                                             http://tissues.jensenlab.org/\n107                                          http://www.ncbi.nlm.nih.gov/geo/\n108                                           http://www.informatics.jax.org/\n109                                        http://compartments.jensenlab.org/\n110                                            http://diseases.jensenlab.org/\n111                                           http://bioplex.hms.harvard.edu/\n112                                              http://www.geneontology.org/\n113                                              http://www.geneontology.org/\n114                                              http://www.geneontology.org/\n115                                              http://www.geneontology.org/\n116                                              http://www.geneontology.org/\n117                                              http://www.geneontology.org/\n118                                          http://amp.pharm.mssm.edu/archs4\n119                                          http://amp.pharm.mssm.edu/archs4\n120                                          http://amp.pharm.mssm.edu/archs4\n121                                          http://amp.pharm.mssm.edu/archs4\n122                                          http://amp.pharm.mssm.edu/archs4\n123                                               http://sys-myo.rhcloud.com/\n124                                        http://mirtarbase.mbc.nctu.edu.tw/\n125                                                http://www.targetscan.org/\n126                                         http://amp.pharm.mssm.edu/Enrichr\n127                                         http://amp.pharm.mssm.edu/Enrichr\n128                                         http://amp.pharm.mssm.edu/Enrichr\n129                             http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/\n130                                              http://www.geneontology.org/\n131                                              http://www.geneontology.org/\n132                                              http://www.geneontology.org/\n133                                          http://www.ncbi.nlm.nih.gov/geo/\n134                             http://hgdownload.cse.ucsc.edu/downloads.html\n135                                      https://www.ncbi.nlm.nih.gov/pubmed/\n136                                      https://www.ncbi.nlm.nih.gov/pubmed/\n137                                      https://amp.pharm.mssm.edu/geneshot/\n138                           https://www.ncbi.nlm.nih.gov/gene/about-generif\n139                                      https://www.ncbi.nlm.nih.gov/pubmed/\n140                                      https://www.ncbi.nlm.nih.gov/pubmed/\n141                           https://www.ncbi.nlm.nih.gov/gene/about-generif\n142                                      https://amp.pharm.mssm.edu/geneshot/\n143                                            http://www.subcellbarcode.org/\n144                                                https://www.ebi.ac.uk/gwas\n145                                             https://www.wikipathways.org/\n146                                             https://www.wikipathways.org/\n147                                          https://www.grnpedia.org/trrust/\n148                                                      https://www.kegg.jp/\n149                                                      https://www.kegg.jp/\n150                                           https://www.ebi.ac.uk/interpro/\n151                                                    https://pfam.xfam.org/\n152                                                       https://depmap.org/\n153                                                       https://depmap.org/\n154                                           http://www.informatics.jax.org/\n155                                     https://www.ukbiobank.ac.uk/tag/gwas/\n156                                         https://tripod.nih.gov/bioplanet/\n157                                     https://www.ncbi.nlm.nih.gov/clinvar/\n158                                              http://pheweb.sph.umich.edu/\n159                                                  https://www.disgenet.org\n160                                  http://lincs.hms.harvard.edu/kinomescan/\n161                                   https://portals.broadinstitute.org/ccle\n162                                             https://www.proteomicsdb.org/\n163                                        https://amp.pharm.mssm.edu/lnchub/\n164                                                      http://phipster.org/\n165                                 http://www.transgene.ru/disease-pathways/\n166                                                                          \n167                                        https://amp.pharm.mssm.edu/covid19\n168                  https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp \n    numTerms\n1        615\n2        326\n3        290\n4        353\n5        701\n6        498\n7        249\n8         78\n9        199\n10       142\n11       200\n12       269\n13       222\n14       385\n15      1136\n16      2139\n17       386\n18        84\n19        96\n20       641\n21      5192\n22      1779\n23       383\n24       474\n25      1796\n26      1658\n27        84\n28        71\n29       476\n30      6100\n31      6100\n32        90\n33       187\n34        85\n35       858\n36       189\n37       142\n38       323\n39       323\n40       967\n41        93\n42       207\n43        30\n44      3906\n45       311\n46       941\n47       205\n48       402\n49      2192\n50       816\n51       412\n52        59\n53      2192\n54       109\n55       216\n56       216\n57       476\n58       239\n59       125\n60       179\n61       209\n62       104\n63       404\n64      1389\n65       315\n66        12\n67       839\n68       839\n69       906\n70     32876\n71       906\n72       428\n73      2460\n74      2460\n75       395\n76       345\n77     33132\n78     33132\n79      2918\n80      2918\n81       261\n82       286\n83       286\n84       261\n85       401\n86       401\n87       312\n88       312\n89        96\n90        96\n91      3644\n92      3644\n93      1530\n94       293\n95       437\n96       104\n97       285\n98       285\n99       237\n100      152\n101      209\n102      112\n103     7876\n104      645\n105      995\n106     1842\n107     1302\n108     5231\n109     2283\n110     1811\n111     3915\n112      636\n113      972\n114     3166\n115      816\n116     3271\n117    10125\n118      108\n119      125\n120      352\n121      498\n122     1724\n123     1135\n124     3240\n125      683\n126      121\n127     1722\n128       12\n129     4026\n130     5103\n131      446\n132     1151\n133     1958\n134       36\n135     5687\n136    12558\n137     3725\n138     2244\n139    12558\n140     5684\n141     2244\n142     3725\n143      104\n144     1737\n145      472\n146      176\n147      571\n148      308\n149      303\n150     1071\n151      608\n152      558\n153      325\n154     5261\n155      857\n156     1510\n157      182\n158     1161\n159     9828\n160      148\n161      378\n162      913\n163     3729\n164     6715\n165     1721\n166      802\n167      205\n168       50\n","name":"stdout"}]},{"metadata":{"trusted":false},"cell_type":"code","source":"# Plotting the network of the top 100 hub DEGs\nDATA<-networking[1:100,1]\n#data<-as.character(data[,3])\n\ndbs <- c(\"Mouse_Gene_Atlas\", \"WikiPathways_2019_Mouse\",\"KEGG_2019_Mouse\")\nenriched <- enrichr(DATA, dbs)\nMouse_Gene_Atlas<-enriched[[1]][,c(1,2,3,9)]\nMouse_Gene_Atlas\nWikiPathways_2019_Mouse<-enriched[[2]][,c(1,2,3,9)]\nWikiPathways_2019_Mouse\nKEGG_2019_Mouse<-enriched[[3]][,c(1,2,3,9)]\nKEGG_2019_Mouse","execution_count":26,"outputs":[{"output_type":"stream","text":"Uploading data to Enrichr... Done.\n  Querying Mouse_Gene_Atlas... Done.\n  Querying WikiPathways_2019_Mouse... Done.\n  Querying KEGG_2019_Mouse... Done.\nParsing results... Done.\n","name":"stdout"},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A data.frame: 64 × 4</caption>\n<thead>\n\t<tr><th scope=col>Term</th><th scope=col>Overlap</th><th scope=col>P.value</th><th scope=col>Genes</th></tr>\n\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>macrophage bone marrow 2hr LPS</td><td>5/365</td><td>0.01630428</td><td>KLF6;PDGFB;CCL3;OPRM1;ITGA5          </td></tr>\n\t<tr><td>osteoblast day5               </td><td>3/175</td><td>0.03419017</td><td>CCND2;SERPINF1;TIMP1                 </td></tr>\n\t<tr><td>follicular B-cells            </td><td>6/603</td><td>0.03550204</td><td>CD52;OXA1L;RAB5B;HIP1R;ICOSL;TSPAN32 </td></tr>\n\t<tr><td>adipose brown                 </td><td>5/456</td><td>0.03766717</td><td>NDUFA9;DBT;PPARG;DLAT;SDHD           </td></tr>\n\t<tr><td>macrophage peri LPS thio 7hrs </td><td>6/707</td><td>0.06685348</td><td>MMP14;SEC24B;MX1;CCL3;TRIM25;ITGA5   </td></tr>\n\t<tr><td>embryonic stem line V26 2 p16 </td><td>6/728</td><td>0.07468785</td><td>DNMT3L;MCM3AP;UHRF1;POLR3D;NHP2;WDR77</td></tr>\n\t<tr><td>B-cells GL7 positive Alum     </td><td>2/142</td><td>0.11299413</td><td>MYG1;HIP1R                           </td></tr>\n\t<tr><td>osteoblast day14              </td><td>3/301</td><td>0.12300399</td><td>MMP14;SERPINF1;TIMP1                 </td></tr>\n\t<tr><td>lung                          </td><td>3/354</td><td>0.17315800</td><td>CAV2;ICAM2;PDGFB                     </td></tr>\n\t<tr><td>macrophage bone marrow 24h LPS</td><td>4/551</td><td>0.18439789</td><td>RAB5B;RAF1;CFP;MXD1                  </td></tr>\n\t<tr><td>adipose white                 </td><td>2/199</td><td>0.19284347</td><td>CAV2;ARAF                            </td></tr>\n\t<tr><td>intestine large               </td><td>3/374</td><td>0.19342111</td><td>MXD1;SLC26A3;CAR4                    </td></tr>\n\t<tr><td>heart                         </td><td>4/568</td><td>0.19834977</td><td>NDUFA9;DLAT;SDHD;ATP5F1              </td></tr>\n\t<tr><td>macrophage peri LPS thio 1hrs </td><td>4/598</td><td>0.22372674</td><td>KLF6;ITGB2;CCL3;ITGA5                </td></tr>\n\t<tr><td>bone marrow                   </td><td>3/413</td><td>0.23447371</td><td>USP32;UHRF1;HNRNPD                   </td></tr>\n\t<tr><td>osteoblast day21              </td><td>2/264</td><td>0.28988388</td><td>MMP14;SERPINF1                       </td></tr>\n\t<tr><td>microglia                     </td><td>1/90 </td><td>0.30653611</td><td>GNA12                                </td></tr>\n\t<tr><td>hypothalamus                  </td><td>2/296</td><td>0.33760609</td><td>ARAF;GABRG1                          </td></tr>\n\t<tr><td>macrophage bone marrow 6hr LPS</td><td>4/730</td><td>0.34277594</td><td>GABRA2;CCL3;TRIM25;CFP               </td></tr>\n\t<tr><td>MEF                           </td><td>2/300</td><td>0.34351593</td><td>CCND2;ITGA5                          </td></tr>\n\t<tr><td>amygdala                      </td><td>2/308</td><td>0.35528651</td><td>GABRA2;GABRG1                        </td></tr>\n\t<tr><td>prostate                      </td><td>2/329</td><td>0.38582271</td><td>IFT20;ARAF                           </td></tr>\n\t<tr><td>granulocytes mac1+gr1+        </td><td>3/557</td><td>0.39329908</td><td>CDC45;OPRM1;RAF1                     </td></tr>\n\t<tr><td>granulo mono progenitor       </td><td>1/124</td><td>0.39635904</td><td>CDC45                                </td></tr>\n\t<tr><td>cerebral cortex               </td><td>2/349</td><td>0.41432952</td><td>TOM1L2;CALM1                         </td></tr>\n\t<tr><td>mast cells IgE                </td><td>3/598</td><td>0.43787322</td><td>MNT;CCND2;GNA12                      </td></tr>\n\t<tr><td>nucleus accumbens             </td><td>2/383</td><td>0.46125546</td><td>GABRA2;OPRM1                         </td></tr>\n\t<tr><td>C3H 10T1 2                    </td><td>1/155</td><td>0.46818663</td><td>FMR1                                 </td></tr>\n\t<tr><td>bone                          </td><td>1/157</td><td>0.47251920</td><td>USP32                                </td></tr>\n\t<tr><td>embryonic stem line Bruce4 p13</td><td>4/876</td><td>0.47649276</td><td>MCM3AP;NHP2;RPA1;WDR77               </td></tr>\n\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n\t<tr><td>3T3-L1                       </td><td>1/181 </td><td>0.5218684</td><td>FMR1                   </td></tr>\n\t<tr><td>stem cells  HSC              </td><td>1/186 </td><td>0.5315604</td><td>SMARCB1                </td></tr>\n\t<tr><td>Baf3                         </td><td>2/440 </td><td>0.5348624</td><td>C1D;TSPAN32            </td></tr>\n\t<tr><td>T-cells CD4+                 </td><td>1/191 </td><td>0.5410583</td><td>ITGB7                  </td></tr>\n\t<tr><td>thymocyte SP CD4+            </td><td>1/203 </td><td>0.5630842</td><td>OXA1L                  </td></tr>\n\t<tr><td>intestine small              </td><td>2/466 </td><td>0.5661282</td><td>MXD1;CALM1             </td></tr>\n\t<tr><td>B-cells GL7 positive KLH     </td><td>1/207 </td><td>0.5701916</td><td>HIP1R                  </td></tr>\n\t<tr><td>B-cells GL7 negative KLH     </td><td>1/212 </td><td>0.5789154</td><td>OPRM1                  </td></tr>\n\t<tr><td>dendritic plasmacytoid B220+ </td><td>1/214 </td><td>0.5823557</td><td>LMAN2L                 </td></tr>\n\t<tr><td>adrenal gland                </td><td>1/245 </td><td>0.6322640</td><td>OCRL                   </td></tr>\n\t<tr><td>stomach                      </td><td>1/249 </td><td>0.6382586</td><td>SLC26A3                </td></tr>\n\t<tr><td>nih 3T3                      </td><td>1/252 </td><td>0.6426912</td><td>CAV2                   </td></tr>\n\t<tr><td>dorsal root ganglia          </td><td>2/541 </td><td>0.6478253</td><td>TOM1L2;NGFR            </td></tr>\n\t<tr><td>mega erythrocyte progenitor  </td><td>2/562 </td><td>0.6684413</td><td>SMARCB1;GNA12          </td></tr>\n\t<tr><td>pituitary                    </td><td>1/294 </td><td>0.6993952</td><td>TOM1L2                 </td></tr>\n\t<tr><td>mIMCD-3                      </td><td>1/300 </td><td>0.7067340</td><td>WDR77                  </td></tr>\n\t<tr><td>liver                        </td><td>3/928 </td><td>0.7317621</td><td>GABRA2;HAAO;OPRM1      </td></tr>\n\t<tr><td>macrophage peri LPS thio 0hrs</td><td>1/353 </td><td>0.7643320</td><td>ITGB2                  </td></tr>\n\t<tr><td>spinal cord                  </td><td>1/353 </td><td>0.7643320</td><td>GNA12                  </td></tr>\n\t<tr><td>skeletal muscle              </td><td>2/710 </td><td>0.7875443</td><td>FGF6;MKRN2             </td></tr>\n\t<tr><td>B-cells marginal zone        </td><td>1/380 </td><td>0.7892219</td><td>SPG7                   </td></tr>\n\t<tr><td>olfactory bulb               </td><td>1/414 </td><td>0.8169003</td><td>DNAJC5                 </td></tr>\n\t<tr><td>cornea                       </td><td>1/427 </td><td>0.8265064</td><td>S100A3                 </td></tr>\n\t<tr><td>retinal pigment epithelium   </td><td>1/473 </td><td>0.8566681</td><td>USP32                  </td></tr>\n\t<tr><td>dorsal striatum              </td><td>1/477 </td><td>0.8590317</td><td>HNRNPD                 </td></tr>\n\t<tr><td>salivary gland               </td><td>1/480 </td><td>0.8607791</td><td>SMARCB1                </td></tr>\n\t<tr><td>kidney                       </td><td>1/554 </td><td>0.8977133</td><td>SLC22A18               </td></tr>\n\t<tr><td>retina                       </td><td>1/664 </td><td>0.9354559</td><td>HNRNPD                 </td></tr>\n\t<tr><td>lacrimal gland               </td><td>1/749 </td><td>0.9548602</td><td>IFT20                  </td></tr>\n\t<tr><td>testis                       </td><td>4/3059</td><td>0.9991093</td><td>SEC24B;CDC45;MKRN2;PCNT</td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 64 × 4\n\n| Term &lt;chr&gt; | Overlap &lt;chr&gt; | P.value &lt;dbl&gt; | Genes &lt;chr&gt; |\n|---|---|---|---|\n| macrophage bone marrow 2hr LPS | 5/365 | 0.01630428 | KLF6;PDGFB;CCL3;OPRM1;ITGA5           |\n| osteoblast day5                | 3/175 | 0.03419017 | CCND2;SERPINF1;TIMP1                  |\n| follicular B-cells             | 6/603 | 0.03550204 | CD52;OXA1L;RAB5B;HIP1R;ICOSL;TSPAN32  |\n| adipose brown                  | 5/456 | 0.03766717 | NDUFA9;DBT;PPARG;DLAT;SDHD            |\n| macrophage peri LPS thio 7hrs  | 6/707 | 0.06685348 | MMP14;SEC24B;MX1;CCL3;TRIM25;ITGA5    |\n| embryonic stem line V26 2 p16  | 6/728 | 0.07468785 | DNMT3L;MCM3AP;UHRF1;POLR3D;NHP2;WDR77 |\n| B-cells GL7 positive Alum      | 2/142 | 0.11299413 | MYG1;HIP1R                            |\n| osteoblast day14               | 3/301 | 0.12300399 | MMP14;SERPINF1;TIMP1                  |\n| lung                           | 3/354 | 0.17315800 | CAV2;ICAM2;PDGFB                      |\n| macrophage bone marrow 24h LPS | 4/551 | 0.18439789 | RAB5B;RAF1;CFP;MXD1                   |\n| adipose white                  | 2/199 | 0.19284347 | CAV2;ARAF                             |\n| intestine large                | 3/374 | 0.19342111 | MXD1;SLC26A3;CAR4                     |\n| heart                          | 4/568 | 0.19834977 | NDUFA9;DLAT;SDHD;ATP5F1               |\n| macrophage peri LPS thio 1hrs  | 4/598 | 0.22372674 | KLF6;ITGB2;CCL3;ITGA5                 |\n| bone marrow                    | 3/413 | 0.23447371 | USP32;UHRF1;HNRNPD                    |\n| osteoblast day21               | 2/264 | 0.28988388 | MMP14;SERPINF1                        |\n| microglia                      | 1/90  | 0.30653611 | GNA12                                 |\n| hypothalamus                   | 2/296 | 0.33760609 | ARAF;GABRG1                           |\n| macrophage bone marrow 6hr LPS | 4/730 | 0.34277594 | GABRA2;CCL3;TRIM25;CFP                |\n| MEF                            | 2/300 | 0.34351593 | CCND2;ITGA5                           |\n| amygdala                       | 2/308 | 0.35528651 | GABRA2;GABRG1                         |\n| prostate                       | 2/329 | 0.38582271 | IFT20;ARAF                            |\n| granulocytes mac1+gr1+         | 3/557 | 0.39329908 | CDC45;OPRM1;RAF1                      |\n| granulo mono progenitor        | 1/124 | 0.39635904 | CDC45                                 |\n| cerebral cortex                | 2/349 | 0.41432952 | TOM1L2;CALM1                          |\n| mast cells IgE                 | 3/598 | 0.43787322 | MNT;CCND2;GNA12                       |\n| nucleus accumbens              | 2/383 | 0.46125546 | GABRA2;OPRM1                          |\n| C3H 10T1 2                     | 1/155 | 0.46818663 | FMR1                                  |\n| bone                           | 1/157 | 0.47251920 | USP32                                 |\n| embryonic stem line Bruce4 p13 | 4/876 | 0.47649276 | MCM3AP;NHP2;RPA1;WDR77                |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| 3T3-L1                        | 1/181  | 0.5218684 | FMR1                    |\n| stem cells  HSC               | 1/186  | 0.5315604 | SMARCB1                 |\n| Baf3                          | 2/440  | 0.5348624 | C1D;TSPAN32             |\n| T-cells CD4+                  | 1/191  | 0.5410583 | ITGB7                   |\n| thymocyte SP CD4+             | 1/203  | 0.5630842 | OXA1L                   |\n| intestine small               | 2/466  | 0.5661282 | MXD1;CALM1              |\n| B-cells GL7 positive KLH      | 1/207  | 0.5701916 | HIP1R                   |\n| B-cells GL7 negative KLH      | 1/212  | 0.5789154 | OPRM1                   |\n| dendritic plasmacytoid B220+  | 1/214  | 0.5823557 | LMAN2L                  |\n| adrenal gland                 | 1/245  | 0.6322640 | OCRL                    |\n| stomach                       | 1/249  | 0.6382586 | SLC26A3                 |\n| nih 3T3                       | 1/252  | 0.6426912 | CAV2                    |\n| dorsal root ganglia           | 2/541  | 0.6478253 | TOM1L2;NGFR             |\n| mega erythrocyte progenitor   | 2/562  | 0.6684413 | SMARCB1;GNA12           |\n| pituitary                     | 1/294  | 0.6993952 | TOM1L2                  |\n| mIMCD-3                       | 1/300  | 0.7067340 | WDR77                   |\n| liver                         | 3/928  | 0.7317621 | GABRA2;HAAO;OPRM1       |\n| macrophage peri LPS thio 0hrs | 1/353  | 0.7643320 | ITGB2                   |\n| spinal cord                   | 1/353  | 0.7643320 | GNA12                   |\n| skeletal muscle               | 2/710  | 0.7875443 | FGF6;MKRN2              |\n| B-cells marginal zone         | 1/380  | 0.7892219 | SPG7                    |\n| olfactory bulb                | 1/414  | 0.8169003 | DNAJC5                  |\n| cornea                        | 1/427  | 0.8265064 | S100A3                  |\n| retinal pigment epithelium    | 1/473  | 0.8566681 | USP32                   |\n| dorsal striatum               | 1/477  | 0.8590317 | HNRNPD                  |\n| salivary gland                | 1/480  | 0.8607791 | SMARCB1                 |\n| kidney                        | 1/554  | 0.8977133 | SLC22A18                |\n| retina                        | 1/664  | 0.9354559 | HNRNPD                  |\n| lacrimal gland                | 1/749  | 0.9548602 | IFT20                   |\n| testis                        | 4/3059 | 0.9991093 | SEC24B;CDC45;MKRN2;PCNT |\n\n","text/latex":"A data.frame: 64 × 4\n\\begin{tabular}{llll}\n Term & Overlap & P.value & Genes\\\\\n <chr> & <chr> & <dbl> & <chr>\\\\\n\\hline\n\t macrophage bone marrow 2hr LPS & 5/365 & 0.01630428 & KLF6;PDGFB;CCL3;OPRM1;ITGA5          \\\\\n\t osteoblast day5                & 3/175 & 0.03419017 & CCND2;SERPINF1;TIMP1                 \\\\\n\t follicular B-cells             & 6/603 & 0.03550204 & CD52;OXA1L;RAB5B;HIP1R;ICOSL;TSPAN32 \\\\\n\t adipose brown                  & 5/456 & 0.03766717 & NDUFA9;DBT;PPARG;DLAT;SDHD           \\\\\n\t macrophage peri LPS thio 7hrs  & 6/707 & 0.06685348 & MMP14;SEC24B;MX1;CCL3;TRIM25;ITGA5   \\\\\n\t embryonic stem line V26 2 p16  & 6/728 & 0.07468785 & DNMT3L;MCM3AP;UHRF1;POLR3D;NHP2;WDR77\\\\\n\t B-cells GL7 positive Alum      & 2/142 & 0.11299413 & MYG1;HIP1R                           \\\\\n\t osteoblast day14               & 3/301 & 0.12300399 & MMP14;SERPINF1;TIMP1                 \\\\\n\t lung                           & 3/354 & 0.17315800 & CAV2;ICAM2;PDGFB                     \\\\\n\t macrophage bone marrow 24h LPS & 4/551 & 0.18439789 & RAB5B;RAF1;CFP;MXD1                  \\\\\n\t adipose white                  & 2/199 & 0.19284347 & CAV2;ARAF                            \\\\\n\t intestine large                & 3/374 & 0.19342111 & MXD1;SLC26A3;CAR4                    \\\\\n\t heart                          & 4/568 & 0.19834977 & NDUFA9;DLAT;SDHD;ATP5F1              \\\\\n\t macrophage peri LPS thio 1hrs  & 4/598 & 0.22372674 & KLF6;ITGB2;CCL3;ITGA5                \\\\\n\t bone marrow                    & 3/413 & 0.23447371 & USP32;UHRF1;HNRNPD                   \\\\\n\t osteoblast day21               & 2/264 & 0.28988388 & MMP14;SERPINF1                       \\\\\n\t microglia                      & 1/90  & 0.30653611 & GNA12                                \\\\\n\t hypothalamus                   & 2/296 & 0.33760609 & ARAF;GABRG1                          \\\\\n\t macrophage bone marrow 6hr LPS & 4/730 & 0.34277594 & GABRA2;CCL3;TRIM25;CFP               \\\\\n\t MEF                            & 2/300 & 0.34351593 & CCND2;ITGA5                          \\\\\n\t amygdala                       & 2/308 & 0.35528651 & GABRA2;GABRG1                        \\\\\n\t prostate                       & 2/329 & 0.38582271 & IFT20;ARAF                           \\\\\n\t granulocytes mac1+gr1+         & 3/557 & 0.39329908 & CDC45;OPRM1;RAF1                     \\\\\n\t granulo mono progenitor        & 1/124 & 0.39635904 & CDC45                                \\\\\n\t cerebral cortex                & 2/349 & 0.41432952 & TOM1L2;CALM1                         \\\\\n\t mast cells IgE                 & 3/598 & 0.43787322 & MNT;CCND2;GNA12                      \\\\\n\t nucleus accumbens              & 2/383 & 0.46125546 & GABRA2;OPRM1                         \\\\\n\t C3H 10T1 2                     & 1/155 & 0.46818663 & FMR1                                 \\\\\n\t bone                           & 1/157 & 0.47251920 & USP32                                \\\\\n\t embryonic stem line Bruce4 p13 & 4/876 & 0.47649276 & MCM3AP;NHP2;RPA1;WDR77               \\\\\n\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t 3T3-L1                        & 1/181  & 0.5218684 & FMR1                   \\\\\n\t stem cells  HSC               & 1/186  & 0.5315604 & SMARCB1                \\\\\n\t Baf3                          & 2/440  & 0.5348624 & C1D;TSPAN32            \\\\\n\t T-cells CD4+                  & 1/191  & 0.5410583 & ITGB7                  \\\\\n\t thymocyte SP CD4+             & 1/203  & 0.5630842 & OXA1L                  \\\\\n\t intestine small               & 2/466  & 0.5661282 & MXD1;CALM1             \\\\\n\t B-cells GL7 positive KLH      & 1/207  & 0.5701916 & HIP1R                  \\\\\n\t B-cells GL7 negative KLH      & 1/212  & 0.5789154 & OPRM1                  \\\\\n\t dendritic plasmacytoid B220+  & 1/214  & 0.5823557 & LMAN2L                 \\\\\n\t adrenal gland                 & 1/245  & 0.6322640 & OCRL                   \\\\\n\t stomach                       & 1/249  & 0.6382586 & SLC26A3                \\\\\n\t nih 3T3                       & 1/252  & 0.6426912 & CAV2                   \\\\\n\t dorsal root ganglia           & 2/541  & 0.6478253 & TOM1L2;NGFR            \\\\\n\t mega erythrocyte progenitor   & 2/562  & 0.6684413 & SMARCB1;GNA12          \\\\\n\t pituitary                     & 1/294  & 0.6993952 & TOM1L2                 \\\\\n\t mIMCD-3                       & 1/300  & 0.7067340 & WDR77                  \\\\\n\t liver                         & 3/928  & 0.7317621 & GABRA2;HAAO;OPRM1      \\\\\n\t macrophage peri LPS thio 0hrs & 1/353  & 0.7643320 & ITGB2                  \\\\\n\t spinal cord                   & 1/353  & 0.7643320 & GNA12                  \\\\\n\t skeletal muscle               & 2/710  & 0.7875443 & FGF6;MKRN2             \\\\\n\t B-cells marginal zone         & 1/380  & 0.7892219 & SPG7                   \\\\\n\t olfactory bulb                & 1/414  & 0.8169003 & DNAJC5                 \\\\\n\t cornea                        & 1/427  & 0.8265064 & S100A3                 \\\\\n\t retinal pigment epithelium    & 1/473  & 0.8566681 & USP32                  \\\\\n\t dorsal striatum               & 1/477  & 0.8590317 & HNRNPD                 \\\\\n\t salivary gland                & 1/480  & 0.8607791 & SMARCB1                \\\\\n\t kidney                        & 1/554  & 0.8977133 & SLC22A18               \\\\\n\t retina                        & 1/664  & 0.9354559 & HNRNPD                 \\\\\n\t lacrimal gland                & 1/749  & 0.9548602 & IFT20                  \\\\\n\t testis                        & 4/3059 & 0.9991093 & SEC24B;CDC45;MKRN2;PCNT\\\\\n\\end{tabular}\n","text/plain":"   Term                           Overlap P.value   \n1  macrophage bone marrow 2hr LPS 5/365   0.01630428\n2  osteoblast day5                3/175   0.03419017\n3  follicular B-cells             6/603   0.03550204\n4  adipose brown                  5/456   0.03766717\n5  macrophage peri LPS thio 7hrs  6/707   0.06685348\n6  embryonic stem line V26 2 p16  6/728   0.07468785\n7  B-cells GL7 positive Alum      2/142   0.11299413\n8  osteoblast day14               3/301   0.12300399\n9  lung                           3/354   0.17315800\n10 macrophage bone marrow 24h LPS 4/551   0.18439789\n11 adipose white                  2/199   0.19284347\n12 intestine large                3/374   0.19342111\n13 heart                          4/568   0.19834977\n14 macrophage peri LPS thio 1hrs  4/598   0.22372674\n15 bone marrow                    3/413   0.23447371\n16 osteoblast day21               2/264   0.28988388\n17 microglia                      1/90    0.30653611\n18 hypothalamus                   2/296   0.33760609\n19 macrophage bone marrow 6hr LPS 4/730   0.34277594\n20 MEF                            2/300   0.34351593\n21 amygdala                       2/308   0.35528651\n22 prostate                       2/329   0.38582271\n23 granulocytes mac1+gr1+         3/557   0.39329908\n24 granulo mono progenitor        1/124   0.39635904\n25 cerebral cortex                2/349   0.41432952\n26 mast cells IgE                 3/598   0.43787322\n27 nucleus accumbens              2/383   0.46125546\n28 C3H 10T1 2                     1/155   0.46818663\n29 bone                           1/157   0.47251920\n30 embryonic stem line Bruce4 p13 4/876   0.47649276\n⋮  ⋮                              ⋮       ⋮         \n35 3T3-L1                         1/181   0.5218684 \n36 stem cells  HSC                1/186   0.5315604 \n37 Baf3                           2/440   0.5348624 \n38 T-cells CD4+                   1/191   0.5410583 \n39 thymocyte SP CD4+              1/203   0.5630842 \n40 intestine small                2/466   0.5661282 \n41 B-cells GL7 positive KLH       1/207   0.5701916 \n42 B-cells GL7 negative KLH       1/212   0.5789154 \n43 dendritic plasmacytoid B220+   1/214   0.5823557 \n44 adrenal gland                  1/245   0.6322640 \n45 stomach                        1/249   0.6382586 \n46 nih 3T3                        1/252   0.6426912 \n47 dorsal root ganglia            2/541   0.6478253 \n48 mega erythrocyte progenitor    2/562   0.6684413 \n49 pituitary                      1/294   0.6993952 \n50 mIMCD-3                        1/300   0.7067340 \n51 liver                          3/928   0.7317621 \n52 macrophage peri LPS thio 0hrs  1/353   0.7643320 \n53 spinal cord                    1/353   0.7643320 \n54 skeletal muscle                2/710   0.7875443 \n55 B-cells marginal zone          1/380   0.7892219 \n56 olfactory bulb                 1/414   0.8169003 \n57 cornea                         1/427   0.8265064 \n58 retinal pigment epithelium     1/473   0.8566681 \n59 dorsal striatum                1/477   0.8590317 \n60 salivary gland                 1/480   0.8607791 \n61 kidney                         1/554   0.8977133 \n62 retina                         1/664   0.9354559 \n63 lacrimal gland                 1/749   0.9548602 \n64 testis                         4/3059  0.9991093 \n   Genes                                \n1  KLF6;PDGFB;CCL3;OPRM1;ITGA5          \n2  CCND2;SERPINF1;TIMP1                 \n3  CD52;OXA1L;RAB5B;HIP1R;ICOSL;TSPAN32 \n4  NDUFA9;DBT;PPARG;DLAT;SDHD           \n5  MMP14;SEC24B;MX1;CCL3;TRIM25;ITGA5   \n6  DNMT3L;MCM3AP;UHRF1;POLR3D;NHP2;WDR77\n7  MYG1;HIP1R                           \n8  MMP14;SERPINF1;TIMP1                 \n9  CAV2;ICAM2;PDGFB                     \n10 RAB5B;RAF1;CFP;MXD1                  \n11 CAV2;ARAF                            \n12 MXD1;SLC26A3;CAR4                    \n13 NDUFA9;DLAT;SDHD;ATP5F1              \n14 KLF6;ITGB2;CCL3;ITGA5                \n15 USP32;UHRF1;HNRNPD                   \n16 MMP14;SERPINF1                       \n17 GNA12                                \n18 ARAF;GABRG1                          \n19 GABRA2;CCL3;TRIM25;CFP               \n20 CCND2;ITGA5                          \n21 GABRA2;GABRG1                        \n22 IFT20;ARAF                           \n23 CDC45;OPRM1;RAF1                     \n24 CDC45                                \n25 TOM1L2;CALM1                         \n26 MNT;CCND2;GNA12                      \n27 GABRA2;OPRM1                         \n28 FMR1                                 \n29 USP32                                \n30 MCM3AP;NHP2;RPA1;WDR77               \n⋮  ⋮                                    \n35 FMR1                                 \n36 SMARCB1                              \n37 C1D;TSPAN32                          \n38 ITGB7                                \n39 OXA1L                                \n40 MXD1;CALM1                           \n41 HIP1R                                \n42 OPRM1                                \n43 LMAN2L                               \n44 OCRL                                 \n45 SLC26A3                              \n46 CAV2                                 \n47 TOM1L2;NGFR                          \n48 SMARCB1;GNA12                        \n49 TOM1L2                               \n50 WDR77                                \n51 GABRA2;HAAO;OPRM1                    \n52 ITGB2                                \n53 GNA12                                \n54 FGF6;MKRN2                           \n55 SPG7                                 \n56 DNAJC5                               \n57 S100A3                               \n58 USP32                                \n59 HNRNPD                               \n60 SMARCB1                              \n61 SLC22A18                             \n62 HNRNPD                               \n63 IFT20                                \n64 SEC24B;CDC45;MKRN2;PCNT              "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A data.frame: 71 × 4</caption>\n<thead>\n\t<tr><th scope=col>Term</th><th scope=col>Overlap</th><th scope=col>P.value</th><th scope=col>Genes</th></tr>\n\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>Focal Adhesion WP85                                              </td><td>8/185</td><td>8.326651e-07</td><td>CCND2;CAV2;ITGB2;ARAF;PDGFB;ITGB7;ITGA5;RAF1</td></tr>\n\t<tr><td>Integrin-mediated Cell Adhesion WP6                              </td><td>6/100</td><td>3.218782e-06</td><td>CAV2;ITGB2;ARAF;ITGB7;ITGA5;RAF1            </td></tr>\n\t<tr><td>Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP2841            </td><td>7/324</td><td>3.420955e-04</td><td>FGF6;NGFR;ITGB2;PDGFB;ITGB7;ITGA5;RAF1      </td></tr>\n\t<tr><td>ESC Pluripotency Pathways WP339                                  </td><td>4/118</td><td>1.350028e-03</td><td>FGF6;ARAF;PDGFB;RAF1                        </td></tr>\n\t<tr><td>G1 to S cell cycle control WP413                                 </td><td>3/61 </td><td>1.944856e-03</td><td>CCND2;CDC45;RPA1                            </td></tr>\n\t<tr><td>Lung fibrosis WP3632                                             </td><td>3/61 </td><td>1.944856e-03</td><td>PDGFB;CCL3;TIMP1                            </td></tr>\n\t<tr><td>Regulation of Actin Cytoskeleton WP523                           </td><td>4/152</td><td>3.390183e-03</td><td>FGF6;GNA12;PDGFB;RAF1                       </td></tr>\n\t<tr><td>Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling WP3855</td><td>2/26 </td><td>4.943342e-03</td><td>CCND2;RAF1                                  </td></tr>\n\t<tr><td>Novel Jun-Dmp1 Pathway WP3654                                    </td><td>2/26 </td><td>4.943342e-03</td><td>ARAF;RAF1                                   </td></tr>\n\t<tr><td>MAPK Cascade WP251                                               </td><td>2/28 </td><td>5.719465e-03</td><td>ARAF;RAF1                                   </td></tr>\n\t<tr><td>Matrix Metalloproteinases WP441                                  </td><td>2/29 </td><td>6.127077e-03</td><td>MMP14;TIMP1                                 </td></tr>\n\t<tr><td>G Protein Signaling Pathways WP232                               </td><td>3/92 </td><td>6.203711e-03</td><td>GNA12;GNAI3;CALM1                           </td></tr>\n\t<tr><td>TCA Cycle WP434                                                  </td><td>2/31 </td><td>6.980855e-03</td><td>DLAT;SDHD                                   </td></tr>\n\t<tr><td>Electron Transport Chain WP295                                   </td><td>3/103</td><td>8.463663e-03</td><td>NDUFA9;SDHD;ATP5F1                          </td></tr>\n\t<tr><td>DNA Replication WP150                                            </td><td>2/41 </td><td>1.199311e-02</td><td>CDC45;RPA1                                  </td></tr>\n\t<tr><td>IL-7 Signaling Pathway WP297                                     </td><td>2/44 </td><td>1.372822e-02</td><td>CCND2;RAF1                                  </td></tr>\n\t<tr><td>Glycolysis and Gluconeogenesis WP157                             </td><td>2/51 </td><td>1.816873e-02</td><td>DLAT;HK2                                    </td></tr>\n\t<tr><td>mir-193a and MVP in colon cancer metastasis WP3979               </td><td>1/6  </td><td>2.405810e-02</td><td>CCND2                                       </td></tr>\n\t<tr><td>Oxidative phosphorylation WP1248                                 </td><td>2/62 </td><td>2.618858e-02</td><td>NDUFA9;ATP5F1                               </td></tr>\n\t<tr><td>MAPK signaling pathway WP493                                     </td><td>3/159</td><td>2.679264e-02</td><td>GNA12;PDGFB;RAF1                            </td></tr>\n\t<tr><td>IL-5 Signaling Pathway WP151                                     </td><td>2/69 </td><td>3.190681e-02</td><td>ITGB2;RAF1                                  </td></tr>\n\t<tr><td>EGFR1 Signaling Pathway WP572                                    </td><td>3/178</td><td>3.568364e-02</td><td>CAV2;ARAF;RAF1                              </td></tr>\n\t<tr><td>Mismatch repair WP1257                                           </td><td>1/9  </td><td>3.587191e-02</td><td>RPA1                                        </td></tr>\n\t<tr><td>Macrophage markers WP2271                                        </td><td>1/10 </td><td>3.977838e-02</td><td>CD52                                        </td></tr>\n\t<tr><td>Chemokine signaling pathway WP2292                               </td><td>3/190</td><td>4.198933e-02</td><td>GNAI3;CCL3;RAF1                             </td></tr>\n\t<tr><td>Ptf1a related regulatory pathway WP201                           </td><td>1/11 </td><td>4.366921e-02</td><td>KAT2B                                       </td></tr>\n\t<tr><td>MicroRNAs in Cardiomyocyte Hypertrophy WP1560                    </td><td>2/82 </td><td>4.367808e-02</td><td>RAF1;CALM1                                  </td></tr>\n\t<tr><td>Homologous recombination WP1258                                  </td><td>1/13 </td><td>5.140422e-02</td><td>RPA1                                        </td></tr>\n\t<tr><td>Osteoclast WP454                                                 </td><td>1/14 </td><td>5.524852e-02</td><td>PDGFB                                       </td></tr>\n\t<tr><td>Serotonin and anxiety WP2141                                     </td><td>1/18 </td><td>7.047247e-02</td><td>FMR1                                        </td></tr>\n\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n\t<tr><td>G13 Signaling Pathway WP298                            </td><td>1/39 </td><td>0.1465097</td><td>CALM1            </td></tr>\n\t<tr><td>Microglia Pathogen Phagocytosis Pathway WP3626         </td><td>1/41 </td><td>0.1534226</td><td>ITGB2            </td></tr>\n\t<tr><td>Splicing factor NOVA regulated synaptic proteins WP1983</td><td>1/42 </td><td>0.1568583</td><td>CAV2             </td></tr>\n\t<tr><td>Eukaryotic Transcription Initiation WP567              </td><td>1/43 </td><td>0.1602802</td><td>POLR3D           </td></tr>\n\t<tr><td>Tryptophan metabolism WP79                             </td><td>1/44 </td><td>0.1636884</td><td>HAAO             </td></tr>\n\t<tr><td>ErbB signaling pathway WP1261                          </td><td>1/46 </td><td>0.1704638</td><td>ARAF             </td></tr>\n\t<tr><td>Notch Signaling Pathway WP29                           </td><td>1/46 </td><td>0.1704638</td><td>KAT2B            </td></tr>\n\t<tr><td>TNF-alpha NF-kB Signaling Pathway WP246                </td><td>2/187</td><td>0.1753730</td><td>SMARCB1;DDX3X    </td></tr>\n\t<tr><td>TYROBP Causal Network WP3625                           </td><td>1/58 </td><td>0.2099927</td><td>ITGB2            </td></tr>\n\t<tr><td>Wnt Signaling Pathway WP403                            </td><td>1/60 </td><td>0.2163975</td><td>CCND2            </td></tr>\n\t<tr><td>Endochondral Ossification WP1270                       </td><td>1/62 </td><td>0.2227510</td><td>CALM1            </td></tr>\n\t<tr><td>p53 signaling WP2902                                   </td><td>1/67 </td><td>0.2384130</td><td>CCND2            </td></tr>\n\t<tr><td>Kit Receptor Signaling Pathway WP407                   </td><td>1/67 </td><td>0.2384130</td><td>RAF1             </td></tr>\n\t<tr><td>Peptide GPCRs WP234                                    </td><td>1/71 </td><td>0.2507179</td><td>OPRM1            </td></tr>\n\t<tr><td>IL-2 Signaling Pathway WP450                           </td><td>1/76 </td><td>0.2658231</td><td>RAF1             </td></tr>\n\t<tr><td>PPAR signaling pathway WP2316                          </td><td>1/81 </td><td>0.2806275</td><td>PPARG            </td></tr>\n\t<tr><td>mRNA processing WP310                                  </td><td>3/457</td><td>0.2824550</td><td>DDX3X;FMR1;HNRNPD</td></tr>\n\t<tr><td>Cytoplasmic Ribosomal Proteins WP163                   </td><td>1/92 </td><td>0.3121672</td><td>RPL13            </td></tr>\n\t<tr><td>Amino Acid metabolism WP662                            </td><td>1/95 </td><td>0.3205293</td><td>SDHD             </td></tr>\n\t<tr><td>Wnt Signaling Pathway and Pluripotency WP723           </td><td>1/97 </td><td>0.3260482</td><td>CCND2            </td></tr>\n\t<tr><td>Spinal Cord Injury WP2432                              </td><td>1/99 </td><td>0.3315227</td><td>NGFR             </td></tr>\n\t<tr><td>IL-6 signaling Pathway WP387                           </td><td>1/99 </td><td>0.3315227</td><td>RAF1             </td></tr>\n\t<tr><td>IL-3 Signaling Pathway WP373                           </td><td>1/100</td><td>0.3342435</td><td>RAF1             </td></tr>\n\t<tr><td>Wnt Signaling Pathway NetPath WP539                    </td><td>1/108</td><td>0.3556198</td><td>RAF1             </td></tr>\n\t<tr><td>Calcium Regulation in the Cardiac Cell WP553           </td><td>1/147</td><td>0.4505021</td><td>GNAI3            </td></tr>\n\t<tr><td>GPCRs, Other WP41                                      </td><td>1/155</td><td>0.4681866</td><td>OPRM1            </td></tr>\n\t<tr><td>Insulin Signaling WP65                                 </td><td>1/159</td><td>0.4768169</td><td>RAF1             </td></tr>\n\t<tr><td>Purine metabolism WP2185                               </td><td>1/171</td><td>0.5018864</td><td>POLR3D           </td></tr>\n\t<tr><td>GPCRs, Class A Rhodopsin-like WP189                    </td><td>1/228</td><td>0.6056730</td><td>OPRM1            </td></tr>\n\t<tr><td>Non-odorant GPCRs WP1396                               </td><td>1/267</td><td>0.6640618</td><td>OPRM1            </td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 71 × 4\n\n| Term &lt;chr&gt; | Overlap &lt;chr&gt; | P.value &lt;dbl&gt; | Genes &lt;chr&gt; |\n|---|---|---|---|\n| Focal Adhesion WP85                                               | 8/185 | 8.326651e-07 | CCND2;CAV2;ITGB2;ARAF;PDGFB;ITGB7;ITGA5;RAF1 |\n| Integrin-mediated Cell Adhesion WP6                               | 6/100 | 3.218782e-06 | CAV2;ITGB2;ARAF;ITGB7;ITGA5;RAF1             |\n| Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP2841             | 7/324 | 3.420955e-04 | FGF6;NGFR;ITGB2;PDGFB;ITGB7;ITGA5;RAF1       |\n| ESC Pluripotency Pathways WP339                                   | 4/118 | 1.350028e-03 | FGF6;ARAF;PDGFB;RAF1                         |\n| G1 to S cell cycle control WP413                                  | 3/61  | 1.944856e-03 | CCND2;CDC45;RPA1                             |\n| Lung fibrosis WP3632                                              | 3/61  | 1.944856e-03 | PDGFB;CCL3;TIMP1                             |\n| Regulation of Actin Cytoskeleton WP523                            | 4/152 | 3.390183e-03 | FGF6;GNA12;PDGFB;RAF1                        |\n| Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling WP3855 | 2/26  | 4.943342e-03 | CCND2;RAF1                                   |\n| Novel Jun-Dmp1 Pathway WP3654                                     | 2/26  | 4.943342e-03 | ARAF;RAF1                                    |\n| MAPK Cascade WP251                                                | 2/28  | 5.719465e-03 | ARAF;RAF1                                    |\n| Matrix Metalloproteinases WP441                                   | 2/29  | 6.127077e-03 | MMP14;TIMP1                                  |\n| G Protein Signaling Pathways WP232                                | 3/92  | 6.203711e-03 | GNA12;GNAI3;CALM1                            |\n| TCA Cycle WP434                                                   | 2/31  | 6.980855e-03 | DLAT;SDHD                                    |\n| Electron Transport Chain WP295                                    | 3/103 | 8.463663e-03 | NDUFA9;SDHD;ATP5F1                           |\n| DNA Replication WP150                                             | 2/41  | 1.199311e-02 | CDC45;RPA1                                   |\n| IL-7 Signaling Pathway WP297                                      | 2/44  | 1.372822e-02 | CCND2;RAF1                                   |\n| Glycolysis and Gluconeogenesis WP157                              | 2/51  | 1.816873e-02 | DLAT;HK2                                     |\n| mir-193a and MVP in colon cancer metastasis WP3979                | 1/6   | 2.405810e-02 | CCND2                                        |\n| Oxidative phosphorylation WP1248                                  | 2/62  | 2.618858e-02 | NDUFA9;ATP5F1                                |\n| MAPK signaling pathway WP493                                      | 3/159 | 2.679264e-02 | GNA12;PDGFB;RAF1                             |\n| IL-5 Signaling Pathway WP151                                      | 2/69  | 3.190681e-02 | ITGB2;RAF1                                   |\n| EGFR1 Signaling Pathway WP572                                     | 3/178 | 3.568364e-02 | CAV2;ARAF;RAF1                               |\n| Mismatch repair WP1257                                            | 1/9   | 3.587191e-02 | RPA1                                         |\n| Macrophage markers WP2271                                         | 1/10  | 3.977838e-02 | CD52                                         |\n| Chemokine signaling pathway WP2292                                | 3/190 | 4.198933e-02 | GNAI3;CCL3;RAF1                              |\n| Ptf1a related regulatory pathway WP201                            | 1/11  | 4.366921e-02 | KAT2B                                        |\n| MicroRNAs in Cardiomyocyte Hypertrophy WP1560                     | 2/82  | 4.367808e-02 | RAF1;CALM1                                   |\n| Homologous recombination WP1258                                   | 1/13  | 5.140422e-02 | RPA1                                         |\n| Osteoclast WP454                                                  | 1/14  | 5.524852e-02 | PDGFB                                        |\n| Serotonin and anxiety WP2141                                      | 1/18  | 7.047247e-02 | FMR1                                         |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| G13 Signaling Pathway WP298                             | 1/39  | 0.1465097 | CALM1             |\n| Microglia Pathogen Phagocytosis Pathway WP3626          | 1/41  | 0.1534226 | ITGB2             |\n| Splicing factor NOVA regulated synaptic proteins WP1983 | 1/42  | 0.1568583 | CAV2              |\n| Eukaryotic Transcription Initiation WP567               | 1/43  | 0.1602802 | POLR3D            |\n| Tryptophan metabolism WP79                              | 1/44  | 0.1636884 | HAAO              |\n| ErbB signaling pathway WP1261                           | 1/46  | 0.1704638 | ARAF              |\n| Notch Signaling Pathway WP29                            | 1/46  | 0.1704638 | KAT2B             |\n| TNF-alpha NF-kB Signaling Pathway WP246                 | 2/187 | 0.1753730 | SMARCB1;DDX3X     |\n| TYROBP Causal Network WP3625                            | 1/58  | 0.2099927 | ITGB2             |\n| Wnt Signaling Pathway WP403                             | 1/60  | 0.2163975 | CCND2             |\n| Endochondral Ossification WP1270                        | 1/62  | 0.2227510 | CALM1             |\n| p53 signaling WP2902                                    | 1/67  | 0.2384130 | CCND2             |\n| Kit Receptor Signaling Pathway WP407                    | 1/67  | 0.2384130 | RAF1              |\n| Peptide GPCRs WP234                                     | 1/71  | 0.2507179 | OPRM1             |\n| IL-2 Signaling Pathway WP450                            | 1/76  | 0.2658231 | RAF1              |\n| PPAR signaling pathway WP2316                           | 1/81  | 0.2806275 | PPARG             |\n| mRNA processing WP310                                   | 3/457 | 0.2824550 | DDX3X;FMR1;HNRNPD |\n| Cytoplasmic Ribosomal Proteins WP163                    | 1/92  | 0.3121672 | RPL13             |\n| Amino Acid metabolism WP662                             | 1/95  | 0.3205293 | SDHD              |\n| Wnt Signaling Pathway and Pluripotency WP723            | 1/97  | 0.3260482 | CCND2             |\n| Spinal Cord Injury WP2432                               | 1/99  | 0.3315227 | NGFR              |\n| IL-6 signaling Pathway WP387                            | 1/99  | 0.3315227 | RAF1              |\n| IL-3 Signaling Pathway WP373                            | 1/100 | 0.3342435 | RAF1              |\n| Wnt Signaling Pathway NetPath WP539                     | 1/108 | 0.3556198 | RAF1              |\n| Calcium Regulation in the Cardiac Cell WP553            | 1/147 | 0.4505021 | GNAI3             |\n| GPCRs, Other WP41                                       | 1/155 | 0.4681866 | OPRM1             |\n| Insulin Signaling WP65                                  | 1/159 | 0.4768169 | RAF1              |\n| Purine metabolism WP2185                                | 1/171 | 0.5018864 | POLR3D            |\n| GPCRs, Class A Rhodopsin-like WP189                     | 1/228 | 0.6056730 | OPRM1             |\n| Non-odorant GPCRs WP1396                                | 1/267 | 0.6640618 | OPRM1             |\n\n","text/latex":"A data.frame: 71 × 4\n\\begin{tabular}{llll}\n Term & Overlap & P.value & Genes\\\\\n <chr> & <chr> & <dbl> & <chr>\\\\\n\\hline\n\t Focal Adhesion WP85                                               & 8/185 & 8.326651e-07 & CCND2;CAV2;ITGB2;ARAF;PDGFB;ITGB7;ITGA5;RAF1\\\\\n\t Integrin-mediated Cell Adhesion WP6                               & 6/100 & 3.218782e-06 & CAV2;ITGB2;ARAF;ITGB7;ITGA5;RAF1            \\\\\n\t Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP2841             & 7/324 & 3.420955e-04 & FGF6;NGFR;ITGB2;PDGFB;ITGB7;ITGA5;RAF1      \\\\\n\t ESC Pluripotency Pathways WP339                                   & 4/118 & 1.350028e-03 & FGF6;ARAF;PDGFB;RAF1                        \\\\\n\t G1 to S cell cycle control WP413                                  & 3/61  & 1.944856e-03 & CCND2;CDC45;RPA1                            \\\\\n\t Lung fibrosis WP3632                                              & 3/61  & 1.944856e-03 & PDGFB;CCL3;TIMP1                            \\\\\n\t Regulation of Actin Cytoskeleton WP523                            & 4/152 & 3.390183e-03 & FGF6;GNA12;PDGFB;RAF1                       \\\\\n\t Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling WP3855 & 2/26  & 4.943342e-03 & CCND2;RAF1                                  \\\\\n\t Novel Jun-Dmp1 Pathway WP3654                                     & 2/26  & 4.943342e-03 & ARAF;RAF1                                   \\\\\n\t MAPK Cascade WP251                                                & 2/28  & 5.719465e-03 & ARAF;RAF1                                   \\\\\n\t Matrix Metalloproteinases WP441                                   & 2/29  & 6.127077e-03 & MMP14;TIMP1                                 \\\\\n\t G Protein Signaling Pathways WP232                                & 3/92  & 6.203711e-03 & GNA12;GNAI3;CALM1                           \\\\\n\t TCA Cycle WP434                                                   & 2/31  & 6.980855e-03 & DLAT;SDHD                                   \\\\\n\t Electron Transport Chain WP295                                    & 3/103 & 8.463663e-03 & NDUFA9;SDHD;ATP5F1                          \\\\\n\t DNA Replication WP150                                             & 2/41  & 1.199311e-02 & CDC45;RPA1                                  \\\\\n\t IL-7 Signaling Pathway WP297                                      & 2/44  & 1.372822e-02 & CCND2;RAF1                                  \\\\\n\t Glycolysis and Gluconeogenesis WP157                              & 2/51  & 1.816873e-02 & DLAT;HK2                                    \\\\\n\t mir-193a and MVP in colon cancer metastasis WP3979                & 1/6   & 2.405810e-02 & CCND2                                       \\\\\n\t Oxidative phosphorylation WP1248                                  & 2/62  & 2.618858e-02 & NDUFA9;ATP5F1                               \\\\\n\t MAPK signaling pathway WP493                                      & 3/159 & 2.679264e-02 & GNA12;PDGFB;RAF1                            \\\\\n\t IL-5 Signaling Pathway WP151                                      & 2/69  & 3.190681e-02 & ITGB2;RAF1                                  \\\\\n\t EGFR1 Signaling Pathway WP572                                     & 3/178 & 3.568364e-02 & CAV2;ARAF;RAF1                              \\\\\n\t Mismatch repair WP1257                                            & 1/9   & 3.587191e-02 & RPA1                                        \\\\\n\t Macrophage markers WP2271                                         & 1/10  & 3.977838e-02 & CD52                                        \\\\\n\t Chemokine signaling pathway WP2292                                & 3/190 & 4.198933e-02 & GNAI3;CCL3;RAF1                             \\\\\n\t Ptf1a related regulatory pathway WP201                            & 1/11  & 4.366921e-02 & KAT2B                                       \\\\\n\t MicroRNAs in Cardiomyocyte Hypertrophy WP1560                     & 2/82  & 4.367808e-02 & RAF1;CALM1                                  \\\\\n\t Homologous recombination WP1258                                   & 1/13  & 5.140422e-02 & RPA1                                        \\\\\n\t Osteoclast WP454                                                  & 1/14  & 5.524852e-02 & PDGFB                                       \\\\\n\t Serotonin and anxiety WP2141                                      & 1/18  & 7.047247e-02 & FMR1                                        \\\\\n\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t G13 Signaling Pathway WP298                             & 1/39  & 0.1465097 & CALM1            \\\\\n\t Microglia Pathogen Phagocytosis Pathway WP3626          & 1/41  & 0.1534226 & ITGB2            \\\\\n\t Splicing factor NOVA regulated synaptic proteins WP1983 & 1/42  & 0.1568583 & CAV2             \\\\\n\t Eukaryotic Transcription Initiation WP567               & 1/43  & 0.1602802 & POLR3D           \\\\\n\t Tryptophan metabolism WP79                              & 1/44  & 0.1636884 & HAAO             \\\\\n\t ErbB signaling pathway WP1261                           & 1/46  & 0.1704638 & ARAF             \\\\\n\t Notch Signaling Pathway WP29                            & 1/46  & 0.1704638 & KAT2B            \\\\\n\t TNF-alpha NF-kB Signaling Pathway WP246                 & 2/187 & 0.1753730 & SMARCB1;DDX3X    \\\\\n\t TYROBP Causal Network WP3625                            & 1/58  & 0.2099927 & ITGB2            \\\\\n\t Wnt Signaling Pathway WP403                             & 1/60  & 0.2163975 & CCND2            \\\\\n\t Endochondral Ossification WP1270                        & 1/62  & 0.2227510 & CALM1            \\\\\n\t p53 signaling WP2902                                    & 1/67  & 0.2384130 & CCND2            \\\\\n\t Kit Receptor Signaling Pathway WP407                    & 1/67  & 0.2384130 & RAF1             \\\\\n\t Peptide GPCRs WP234                                     & 1/71  & 0.2507179 & OPRM1            \\\\\n\t IL-2 Signaling Pathway WP450                            & 1/76  & 0.2658231 & RAF1             \\\\\n\t PPAR signaling pathway WP2316                           & 1/81  & 0.2806275 & PPARG            \\\\\n\t mRNA processing WP310                                   & 3/457 & 0.2824550 & DDX3X;FMR1;HNRNPD\\\\\n\t Cytoplasmic Ribosomal Proteins WP163                    & 1/92  & 0.3121672 & RPL13            \\\\\n\t Amino Acid metabolism WP662                             & 1/95  & 0.3205293 & SDHD             \\\\\n\t Wnt Signaling Pathway and Pluripotency WP723            & 1/97  & 0.3260482 & CCND2            \\\\\n\t Spinal Cord Injury WP2432                               & 1/99  & 0.3315227 & NGFR             \\\\\n\t IL-6 signaling Pathway WP387                            & 1/99  & 0.3315227 & RAF1             \\\\\n\t IL-3 Signaling Pathway WP373                            & 1/100 & 0.3342435 & RAF1             \\\\\n\t Wnt Signaling Pathway NetPath WP539                     & 1/108 & 0.3556198 & RAF1             \\\\\n\t Calcium Regulation in the Cardiac Cell WP553            & 1/147 & 0.4505021 & GNAI3            \\\\\n\t GPCRs, Other WP41                                       & 1/155 & 0.4681866 & OPRM1            \\\\\n\t Insulin Signaling WP65                                  & 1/159 & 0.4768169 & RAF1             \\\\\n\t Purine metabolism WP2185                                & 1/171 & 0.5018864 & POLR3D           \\\\\n\t GPCRs, Class A Rhodopsin-like WP189                     & 1/228 & 0.6056730 & OPRM1            \\\\\n\t Non-odorant GPCRs WP1396                                & 1/267 & 0.6640618 & OPRM1            \\\\\n\\end{tabular}\n","text/plain":"   Term                                                              Overlap\n1  Focal Adhesion WP85                                               8/185  \n2  Integrin-mediated Cell Adhesion WP6                               6/100  \n3  Focal Adhesion-PI3K-Akt-mTOR-signaling pathway WP2841             7/324  \n4  ESC Pluripotency Pathways WP339                                   4/118  \n5  G1 to S cell cycle control WP413                                  3/61   \n6  Lung fibrosis WP3632                                              3/61   \n7  Regulation of Actin Cytoskeleton WP523                            4/152  \n8  Dysregulated miRNA Targeting in Insulin/PI3K-AKT Signaling WP3855 2/26   \n9  Novel Jun-Dmp1 Pathway WP3654                                     2/26   \n10 MAPK Cascade WP251                                                2/28   \n11 Matrix Metalloproteinases WP441                                   2/29   \n12 G Protein Signaling Pathways WP232                                3/92   \n13 TCA Cycle WP434                                                   2/31   \n14 Electron Transport Chain WP295                                    3/103  \n15 DNA Replication WP150                                             2/41   \n16 IL-7 Signaling Pathway WP297                                      2/44   \n17 Glycolysis and Gluconeogenesis WP157                              2/51   \n18 mir-193a and MVP in colon cancer metastasis WP3979                1/6    \n19 Oxidative phosphorylation WP1248                                  2/62   \n20 MAPK signaling pathway WP493                                      3/159  \n21 IL-5 Signaling Pathway WP151                                      2/69   \n22 EGFR1 Signaling Pathway WP572                                     3/178  \n23 Mismatch repair WP1257                                            1/9    \n24 Macrophage markers WP2271                                         1/10   \n25 Chemokine signaling pathway WP2292                                3/190  \n26 Ptf1a related regulatory pathway WP201                            1/11   \n27 MicroRNAs in Cardiomyocyte Hypertrophy WP1560                     2/82   \n28 Homologous recombination WP1258                                   1/13   \n29 Osteoclast WP454                                                  1/14   \n30 Serotonin and anxiety WP2141                                      1/18   \n⋮  ⋮                                                                 ⋮      \n42 G13 Signaling Pathway WP298                                       1/39   \n43 Microglia Pathogen Phagocytosis Pathway WP3626                    1/41   \n44 Splicing factor NOVA regulated synaptic proteins WP1983           1/42   \n45 Eukaryotic Transcription Initiation WP567                         1/43   \n46 Tryptophan metabolism WP79                                        1/44   \n47 ErbB signaling pathway WP1261                                     1/46   \n48 Notch Signaling Pathway WP29                                      1/46   \n49 TNF-alpha NF-kB Signaling Pathway WP246                           2/187  \n50 TYROBP Causal Network WP3625                                      1/58   \n51 Wnt Signaling Pathway WP403                                       1/60   \n52 Endochondral Ossification WP1270                                  1/62   \n53 p53 signaling WP2902                                              1/67   \n54 Kit Receptor Signaling Pathway WP407                              1/67   \n55 Peptide GPCRs WP234                                               1/71   \n56 IL-2 Signaling Pathway WP450                                      1/76   \n57 PPAR signaling pathway WP2316                                     1/81   \n58 mRNA processing WP310                                             3/457  \n59 Cytoplasmic Ribosomal Proteins WP163                              1/92   \n60 Amino Acid metabolism WP662                                       1/95   \n61 Wnt Signaling Pathway and Pluripotency WP723                      1/97   \n62 Spinal Cord Injury WP2432                                         1/99   \n63 IL-6 signaling Pathway WP387                                      1/99   \n64 IL-3 Signaling Pathway WP373                                      1/100  \n65 Wnt Signaling Pathway NetPath WP539                               1/108  \n66 Calcium Regulation in the Cardiac Cell WP553                      1/147  \n67 GPCRs, Other WP41                                                 1/155  \n68 Insulin Signaling WP65                                            1/159  \n69 Purine metabolism WP2185                                          1/171  \n70 GPCRs, Class A Rhodopsin-like WP189                               1/228  \n71 Non-odorant GPCRs WP1396                                          1/267  \n   P.value      Genes                                       \n1  8.326651e-07 CCND2;CAV2;ITGB2;ARAF;PDGFB;ITGB7;ITGA5;RAF1\n2  3.218782e-06 CAV2;ITGB2;ARAF;ITGB7;ITGA5;RAF1            \n3  3.420955e-04 FGF6;NGFR;ITGB2;PDGFB;ITGB7;ITGA5;RAF1      \n4  1.350028e-03 FGF6;ARAF;PDGFB;RAF1                        \n5  1.944856e-03 CCND2;CDC45;RPA1                            \n6  1.944856e-03 PDGFB;CCL3;TIMP1                            \n7  3.390183e-03 FGF6;GNA12;PDGFB;RAF1                       \n8  4.943342e-03 CCND2;RAF1                                  \n9  4.943342e-03 ARAF;RAF1                                   \n10 5.719465e-03 ARAF;RAF1                                   \n11 6.127077e-03 MMP14;TIMP1                                 \n12 6.203711e-03 GNA12;GNAI3;CALM1                           \n13 6.980855e-03 DLAT;SDHD                                   \n14 8.463663e-03 NDUFA9;SDHD;ATP5F1                          \n15 1.199311e-02 CDC45;RPA1                                  \n16 1.372822e-02 CCND2;RAF1                                  \n17 1.816873e-02 DLAT;HK2                                    \n18 2.405810e-02 CCND2                                       \n19 2.618858e-02 NDUFA9;ATP5F1                               \n20 2.679264e-02 GNA12;PDGFB;RAF1                            \n21 3.190681e-02 ITGB2;RAF1                                  \n22 3.568364e-02 CAV2;ARAF;RAF1                              \n23 3.587191e-02 RPA1                                        \n24 3.977838e-02 CD52                                        \n25 4.198933e-02 GNAI3;CCL3;RAF1                             \n26 4.366921e-02 KAT2B                                       \n27 4.367808e-02 RAF1;CALM1                                  \n28 5.140422e-02 RPA1                                        \n29 5.524852e-02 PDGFB                                       \n30 7.047247e-02 FMR1                                        \n⋮  ⋮            ⋮                                           \n42 0.1465097    CALM1                                       \n43 0.1534226    ITGB2                                       \n44 0.1568583    CAV2                                        \n45 0.1602802    POLR3D                                      \n46 0.1636884    HAAO                                        \n47 0.1704638    ARAF                                        \n48 0.1704638    KAT2B                                       \n49 0.1753730    SMARCB1;DDX3X                               \n50 0.2099927    ITGB2                                       \n51 0.2163975    CCND2                                       \n52 0.2227510    CALM1                                       \n53 0.2384130    CCND2                                       \n54 0.2384130    RAF1                                        \n55 0.2507179    OPRM1                                       \n56 0.2658231    RAF1                                        \n57 0.2806275    PPARG                                       \n58 0.2824550    DDX3X;FMR1;HNRNPD                           \n59 0.3121672    RPL13                                       \n60 0.3205293    SDHD                                        \n61 0.3260482    CCND2                                       \n62 0.3315227    NGFR                                        \n63 0.3315227    RAF1                                        \n64 0.3342435    RAF1                                        \n65 0.3556198    RAF1                                        \n66 0.4505021    GNAI3                                       \n67 0.4681866    OPRM1                                       \n68 0.4768169    RAF1                                        \n69 0.5018864    POLR3D                                      \n70 0.6056730    OPRM1                                       \n71 0.6640618    OPRM1                                       "},"metadata":{}},{"output_type":"display_data","data":{"text/html":"<table>\n<caption>A data.frame: 185 × 4</caption>\n<thead>\n\t<tr><th scope=col>Term</th><th scope=col>Overlap</th><th scope=col>P.value</th><th scope=col>Genes</th></tr>\n\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n</thead>\n<tbody>\n\t<tr><td>Regulation of actin cytoskeleton                   </td><td>8/217</td><td>2.751245e-06</td><td>FGF6;ITGB2;GNA12;ARAF;PDGFB;ITGB7;ITGA5;RAF1      </td></tr>\n\t<tr><td>Rap1 signaling pathway                             </td><td>7/209</td><td>2.223718e-05</td><td>FGF6;NGFR;ITGB2;GNAI3;PDGFB;RAF1;CALM1            </td></tr>\n\t<tr><td>Parathyroid hormone synthesis, secretion and action</td><td>5/107</td><td>7.399918e-05</td><td>MMP14;GNA12;ARAF;GNAI3;RAF1                       </td></tr>\n\t<tr><td>Long-term depression                               </td><td>4/61 </td><td>1.093080e-04</td><td>GNA12;ARAF;GNAI3;RAF1                             </td></tr>\n\t<tr><td>Focal adhesion                                     </td><td>6/199</td><td>1.571228e-04</td><td>CCND2;CAV2;PDGFB;ITGB7;ITGA5;RAF1                 </td></tr>\n\t<tr><td>Melanoma                                           </td><td>4/72 </td><td>2.083409e-04</td><td>FGF6;ARAF;PDGFB;RAF1                              </td></tr>\n\t<tr><td>Glioma                                             </td><td>4/75 </td><td>2.438882e-04</td><td>ARAF;PDGFB;RAF1;CALM1                             </td></tr>\n\t<tr><td>Pertussis                                          </td><td>4/76 </td><td>2.566497e-04</td><td>ITGB2;GNAI3;ITGA5;CALM1                           </td></tr>\n\t<tr><td>Pathways in cancer                                 </td><td>9/535</td><td>3.092998e-04</td><td>FGF6;CCND2;GNA12;ARAF;GNAI3;PDGFB;PPARG;RAF1;CALM1</td></tr>\n\t<tr><td>Ras signaling pathway                              </td><td>6/233</td><td>3.671621e-04</td><td>FGF6;NGFR;RAB5B;PDGFB;RAF1;CALM1                  </td></tr>\n\t<tr><td>Morphine addiction                                 </td><td>4/92 </td><td>5.322212e-04</td><td>GABRA2;GNAI3;OPRM1;GABRG1                         </td></tr>\n\t<tr><td>PI3K-Akt signaling pathway                         </td><td>7/357</td><td>6.100600e-04</td><td>FGF6;NGFR;CCND2;PDGFB;ITGB7;ITGA5;RAF1            </td></tr>\n\t<tr><td>MAPK signaling pathway                             </td><td>6/294</td><td>1.236093e-03</td><td>FGF6;NGFR;GNA12;ARAF;PDGFB;RAF1                   </td></tr>\n\t<tr><td>Natural killer cell mediated cytotoxicity          </td><td>4/118</td><td>1.350028e-03</td><td>ITGB2;ICAM2;ARAF;RAF1                             </td></tr>\n\t<tr><td>Estrogen signaling pathway                         </td><td>4/134</td><td>2.151432e-03</td><td>GNAI3;OPRM1;RAF1;CALM1                            </td></tr>\n\t<tr><td>Insulin signaling pathway                          </td><td>4/139</td><td>2.457330e-03</td><td>ARAF;RAF1;CALM1;HK2                               </td></tr>\n\t<tr><td>Thermogenesis                                      </td><td>5/231</td><td>2.473460e-03</td><td>NDUFA9;SMARCB1;PPARG;SDHD;ATP5F1                  </td></tr>\n\t<tr><td>Vascular smooth muscle contraction                 </td><td>4/140</td><td>2.521912e-03</td><td>GNA12;ARAF;RAF1;CALM1                             </td></tr>\n\t<tr><td>Long-term potentiation                             </td><td>3/67 </td><td>2.544059e-03</td><td>ARAF;RAF1;CALM1                                   </td></tr>\n\t<tr><td>Renal cell carcinoma                               </td><td>3/68 </td><td>2.653772e-03</td><td>ARAF;PDGFB;RAF1                                   </td></tr>\n\t<tr><td>Parkinson disease                                  </td><td>4/144</td><td>2.791925e-03</td><td>NDUFA9;GNAI3;SDHD;ATP5F1                          </td></tr>\n\t<tr><td>Retrograde endocannabinoid signaling               </td><td>4/150</td><td>3.233136e-03</td><td>GABRA2;NDUFA9;GNAI3;GABRG1                        </td></tr>\n\t<tr><td>Human cytomegalovirus infection                    </td><td>5/255</td><td>3.776783e-03</td><td>GNA12;GNAI3;CCL3;RAF1;CALM1                       </td></tr>\n\t<tr><td>Cell adhesion molecules (CAMs)                     </td><td>4/170</td><td>5.043483e-03</td><td>ITGB2;ICAM2;ITGB7;ICOSL                           </td></tr>\n\t<tr><td>Gap junction                                       </td><td>3/86 </td><td>5.144036e-03</td><td>GNAI3;PDGFB;RAF1                                  </td></tr>\n\t<tr><td>cGMP-PKG signaling pathway                         </td><td>4/172</td><td>5.255241e-03</td><td>GNA12;GNAI3;RAF1;CALM1                            </td></tr>\n\t<tr><td>Alzheimer disease                                  </td><td>4/175</td><td>5.583934e-03</td><td>NDUFA9;SDHD;ATP5F1;CALM1                          </td></tr>\n\t<tr><td>GABAergic synapse                                  </td><td>3/90 </td><td>5.837183e-03</td><td>GABRA2;GNAI3;GABRG1                               </td></tr>\n\t<tr><td>GnRH signaling pathway                             </td><td>3/90 </td><td>5.837183e-03</td><td>MMP14;RAF1;CALM1                                  </td></tr>\n\t<tr><td>Progesterone-mediated oocyte maturation            </td><td>3/90 </td><td>5.837183e-03</td><td>ARAF;GNAI3;RAF1                                   </td></tr>\n\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n\t<tr><td>Toll-like receptor signaling pathway                    </td><td>1/99  </td><td>0.3315227</td><td>CCL3     </td></tr>\n\t<tr><td>Cytokine-cytokine receptor interaction                  </td><td>2/292 </td><td>0.3316813</td><td>NGFR;CCL3</td></tr>\n\t<tr><td>T cell receptor signaling pathway                       </td><td>1/101 </td><td>0.3369534</td><td>RAF1     </td></tr>\n\t<tr><td>Aldosterone synthesis and secretion                     </td><td>1/102 </td><td>0.3396524</td><td>CALM1    </td></tr>\n\t<tr><td>Glucagon signaling pathway                              </td><td>1/102 </td><td>0.3396524</td><td>CALM1    </td></tr>\n\t<tr><td>Longevity regulating pathway                            </td><td>1/102 </td><td>0.3396524</td><td>PPARG    </td></tr>\n\t<tr><td>NF-kappa B signaling pathway                            </td><td>1/102 </td><td>0.3396524</td><td>TRIM25   </td></tr>\n\t<tr><td>Pancreatic secretion                                    </td><td>1/105 </td><td>0.3476843</td><td>SLC26A3  </td></tr>\n\t<tr><td>Toxoplasmosis                                           </td><td>1/108 </td><td>0.3556198</td><td>GNAI3    </td></tr>\n\t<tr><td>TNF signaling pathway                                   </td><td>1/110 </td><td>0.3608570</td><td>MMP14    </td></tr>\n\t<tr><td>Cholinergic synapse                                     </td><td>1/113 </td><td>0.3686342</td><td>GNAI3    </td></tr>\n\t<tr><td>Glutamatergic synapse                                   </td><td>1/114 </td><td>0.3712057</td><td>GNAI3    </td></tr>\n\t<tr><td>Ribosome biogenesis in eukaryotes                       </td><td>1/115 </td><td>0.3737669</td><td>NHP2     </td></tr>\n\t<tr><td>Oocyte meiosis                                          </td><td>1/116 </td><td>0.3763179</td><td>CALM1    </td></tr>\n\t<tr><td>Platelet activation                                     </td><td>1/125 </td><td>0.3988190</td><td>GNAI3    </td></tr>\n\t<tr><td>AMPK signaling pathway                                  </td><td>1/126 </td><td>0.4012691</td><td>PPARG    </td></tr>\n\t<tr><td>Inflammatory mediator regulation of TRP channels        </td><td>1/127 </td><td>0.4037094</td><td>CALM1    </td></tr>\n\t<tr><td>Osteoclast differentiation                              </td><td>1/128 </td><td>0.4061398</td><td>PPARG    </td></tr>\n\t<tr><td>Autophagy                                               </td><td>1/130 </td><td>0.4109713</td><td>RAF1     </td></tr>\n\t<tr><td>Spliceosome                                             </td><td>1/132 </td><td>0.4157639</td><td>SNRNP27  </td></tr>\n\t<tr><td>Signaling pathways regulating pluripotency of stem cells</td><td>1/137 </td><td>0.4275778</td><td>RAF1     </td></tr>\n\t<tr><td>Measles                                                 </td><td>1/144 </td><td>0.4437217</td><td>CCND2    </td></tr>\n\t<tr><td>mTOR signaling pathway                                  </td><td>1/154 </td><td>0.4660072</td><td>RAF1     </td></tr>\n\t<tr><td>Cushing syndrome                                        </td><td>1/159 </td><td>0.4768169</td><td>GNAI3    </td></tr>\n\t<tr><td>RNA transport                                           </td><td>1/167 </td><td>0.4936646</td><td>FMR1     </td></tr>\n\t<tr><td>Ribosome                                                </td><td>1/170 </td><td>0.4998434</td><td>RPL13    </td></tr>\n\t<tr><td>Herpes simplex virus 1 infection                        </td><td>2/433 </td><td>0.5261910</td><td>ITGA5;CFP</td></tr>\n\t<tr><td>Calcium signaling pathway                               </td><td>1/189 </td><td>0.5372822</td><td>CALM1    </td></tr>\n\t<tr><td>Epstein-Barr virus infection                            </td><td>1/229 </td><td>0.6072885</td><td>CCND2    </td></tr>\n\t<tr><td>Olfactory transduction                                  </td><td>1/1133</td><td>0.9912021</td><td>CALM1    </td></tr>\n</tbody>\n</table>\n","text/markdown":"\nA data.frame: 185 × 4\n\n| Term &lt;chr&gt; | Overlap &lt;chr&gt; | P.value &lt;dbl&gt; | Genes &lt;chr&gt; |\n|---|---|---|---|\n| Regulation of actin cytoskeleton                    | 8/217 | 2.751245e-06 | FGF6;ITGB2;GNA12;ARAF;PDGFB;ITGB7;ITGA5;RAF1       |\n| Rap1 signaling pathway                              | 7/209 | 2.223718e-05 | FGF6;NGFR;ITGB2;GNAI3;PDGFB;RAF1;CALM1             |\n| Parathyroid hormone synthesis, secretion and action | 5/107 | 7.399918e-05 | MMP14;GNA12;ARAF;GNAI3;RAF1                        |\n| Long-term depression                                | 4/61  | 1.093080e-04 | GNA12;ARAF;GNAI3;RAF1                              |\n| Focal adhesion                                      | 6/199 | 1.571228e-04 | CCND2;CAV2;PDGFB;ITGB7;ITGA5;RAF1                  |\n| Melanoma                                            | 4/72  | 2.083409e-04 | FGF6;ARAF;PDGFB;RAF1                               |\n| Glioma                                              | 4/75  | 2.438882e-04 | ARAF;PDGFB;RAF1;CALM1                              |\n| Pertussis                                           | 4/76  | 2.566497e-04 | ITGB2;GNAI3;ITGA5;CALM1                            |\n| Pathways in cancer                                  | 9/535 | 3.092998e-04 | FGF6;CCND2;GNA12;ARAF;GNAI3;PDGFB;PPARG;RAF1;CALM1 |\n| Ras signaling pathway                               | 6/233 | 3.671621e-04 | FGF6;NGFR;RAB5B;PDGFB;RAF1;CALM1                   |\n| Morphine addiction                                  | 4/92  | 5.322212e-04 | GABRA2;GNAI3;OPRM1;GABRG1                          |\n| PI3K-Akt signaling pathway                          | 7/357 | 6.100600e-04 | FGF6;NGFR;CCND2;PDGFB;ITGB7;ITGA5;RAF1             |\n| MAPK signaling pathway                              | 6/294 | 1.236093e-03 | FGF6;NGFR;GNA12;ARAF;PDGFB;RAF1                    |\n| Natural killer cell mediated cytotoxicity           | 4/118 | 1.350028e-03 | ITGB2;ICAM2;ARAF;RAF1                              |\n| Estrogen signaling pathway                          | 4/134 | 2.151432e-03 | GNAI3;OPRM1;RAF1;CALM1                             |\n| Insulin signaling pathway                           | 4/139 | 2.457330e-03 | ARAF;RAF1;CALM1;HK2                                |\n| Thermogenesis                                       | 5/231 | 2.473460e-03 | NDUFA9;SMARCB1;PPARG;SDHD;ATP5F1                   |\n| Vascular smooth muscle contraction                  | 4/140 | 2.521912e-03 | GNA12;ARAF;RAF1;CALM1                              |\n| Long-term potentiation                              | 3/67  | 2.544059e-03 | ARAF;RAF1;CALM1                                    |\n| Renal cell carcinoma                                | 3/68  | 2.653772e-03 | ARAF;PDGFB;RAF1                                    |\n| Parkinson disease                                   | 4/144 | 2.791925e-03 | NDUFA9;GNAI3;SDHD;ATP5F1                           |\n| Retrograde endocannabinoid signaling                | 4/150 | 3.233136e-03 | GABRA2;NDUFA9;GNAI3;GABRG1                         |\n| Human cytomegalovirus infection                     | 5/255 | 3.776783e-03 | GNA12;GNAI3;CCL3;RAF1;CALM1                        |\n| Cell adhesion molecules (CAMs)                      | 4/170 | 5.043483e-03 | ITGB2;ICAM2;ITGB7;ICOSL                            |\n| Gap junction                                        | 3/86  | 5.144036e-03 | GNAI3;PDGFB;RAF1                                   |\n| cGMP-PKG signaling pathway                          | 4/172 | 5.255241e-03 | GNA12;GNAI3;RAF1;CALM1                             |\n| Alzheimer disease                                   | 4/175 | 5.583934e-03 | NDUFA9;SDHD;ATP5F1;CALM1                           |\n| GABAergic synapse                                   | 3/90  | 5.837183e-03 | GABRA2;GNAI3;GABRG1                                |\n| GnRH signaling pathway                              | 3/90  | 5.837183e-03 | MMP14;RAF1;CALM1                                   |\n| Progesterone-mediated oocyte maturation             | 3/90  | 5.837183e-03 | ARAF;GNAI3;RAF1                                    |\n| ⋮ | ⋮ | ⋮ | ⋮ |\n| Toll-like receptor signaling pathway                     | 1/99   | 0.3315227 | CCL3      |\n| Cytokine-cytokine receptor interaction                   | 2/292  | 0.3316813 | NGFR;CCL3 |\n| T cell receptor signaling pathway                        | 1/101  | 0.3369534 | RAF1      |\n| Aldosterone synthesis and secretion                      | 1/102  | 0.3396524 | CALM1     |\n| Glucagon signaling pathway                               | 1/102  | 0.3396524 | CALM1     |\n| Longevity regulating pathway                             | 1/102  | 0.3396524 | PPARG     |\n| NF-kappa B signaling pathway                             | 1/102  | 0.3396524 | TRIM25    |\n| Pancreatic secretion                                     | 1/105  | 0.3476843 | SLC26A3   |\n| Toxoplasmosis                                            | 1/108  | 0.3556198 | GNAI3     |\n| TNF signaling pathway                                    | 1/110  | 0.3608570 | MMP14     |\n| Cholinergic synapse                                      | 1/113  | 0.3686342 | GNAI3     |\n| Glutamatergic synapse                                    | 1/114  | 0.3712057 | GNAI3     |\n| Ribosome biogenesis in eukaryotes                        | 1/115  | 0.3737669 | NHP2      |\n| Oocyte meiosis                                           | 1/116  | 0.3763179 | CALM1     |\n| Platelet activation                                      | 1/125  | 0.3988190 | GNAI3     |\n| AMPK signaling pathway                                   | 1/126  | 0.4012691 | PPARG     |\n| Inflammatory mediator regulation of TRP channels         | 1/127  | 0.4037094 | CALM1     |\n| Osteoclast differentiation                               | 1/128  | 0.4061398 | PPARG     |\n| Autophagy                                                | 1/130  | 0.4109713 | RAF1      |\n| Spliceosome                                              | 1/132  | 0.4157639 | SNRNP27   |\n| Signaling pathways regulating pluripotency of stem cells | 1/137  | 0.4275778 | RAF1      |\n| Measles                                                  | 1/144  | 0.4437217 | CCND2     |\n| mTOR signaling pathway                                   | 1/154  | 0.4660072 | RAF1      |\n| Cushing syndrome                                         | 1/159  | 0.4768169 | GNAI3     |\n| RNA transport                                            | 1/167  | 0.4936646 | FMR1      |\n| Ribosome                                                 | 1/170  | 0.4998434 | RPL13     |\n| Herpes simplex virus 1 infection                         | 2/433  | 0.5261910 | ITGA5;CFP |\n| Calcium signaling pathway                                | 1/189  | 0.5372822 | CALM1     |\n| Epstein-Barr virus infection                             | 1/229  | 0.6072885 | CCND2     |\n| Olfactory transduction                                   | 1/1133 | 0.9912021 | CALM1     |\n\n","text/latex":"A data.frame: 185 × 4\n\\begin{tabular}{llll}\n Term & Overlap & P.value & Genes\\\\\n <chr> & <chr> & <dbl> & <chr>\\\\\n\\hline\n\t Regulation of actin cytoskeleton                    & 8/217 & 2.751245e-06 & FGF6;ITGB2;GNA12;ARAF;PDGFB;ITGB7;ITGA5;RAF1      \\\\\n\t Rap1 signaling pathway                              & 7/209 & 2.223718e-05 & FGF6;NGFR;ITGB2;GNAI3;PDGFB;RAF1;CALM1            \\\\\n\t Parathyroid hormone synthesis, secretion and action & 5/107 & 7.399918e-05 & MMP14;GNA12;ARAF;GNAI3;RAF1                       \\\\\n\t Long-term depression                                & 4/61  & 1.093080e-04 & GNA12;ARAF;GNAI3;RAF1                             \\\\\n\t Focal adhesion                                      & 6/199 & 1.571228e-04 & CCND2;CAV2;PDGFB;ITGB7;ITGA5;RAF1                 \\\\\n\t Melanoma                                            & 4/72  & 2.083409e-04 & FGF6;ARAF;PDGFB;RAF1                              \\\\\n\t Glioma                                              & 4/75  & 2.438882e-04 & ARAF;PDGFB;RAF1;CALM1                             \\\\\n\t Pertussis                                           & 4/76  & 2.566497e-04 & ITGB2;GNAI3;ITGA5;CALM1                           \\\\\n\t Pathways in cancer                                  & 9/535 & 3.092998e-04 & FGF6;CCND2;GNA12;ARAF;GNAI3;PDGFB;PPARG;RAF1;CALM1\\\\\n\t Ras signaling pathway                               & 6/233 & 3.671621e-04 & FGF6;NGFR;RAB5B;PDGFB;RAF1;CALM1                  \\\\\n\t Morphine addiction                                  & 4/92  & 5.322212e-04 & GABRA2;GNAI3;OPRM1;GABRG1                         \\\\\n\t PI3K-Akt signaling pathway                          & 7/357 & 6.100600e-04 & FGF6;NGFR;CCND2;PDGFB;ITGB7;ITGA5;RAF1            \\\\\n\t MAPK signaling pathway                              & 6/294 & 1.236093e-03 & FGF6;NGFR;GNA12;ARAF;PDGFB;RAF1                   \\\\\n\t Natural killer cell mediated cytotoxicity           & 4/118 & 1.350028e-03 & ITGB2;ICAM2;ARAF;RAF1                             \\\\\n\t Estrogen signaling pathway                          & 4/134 & 2.151432e-03 & GNAI3;OPRM1;RAF1;CALM1                            \\\\\n\t Insulin signaling pathway                           & 4/139 & 2.457330e-03 & ARAF;RAF1;CALM1;HK2                               \\\\\n\t Thermogenesis                                       & 5/231 & 2.473460e-03 & NDUFA9;SMARCB1;PPARG;SDHD;ATP5F1                  \\\\\n\t Vascular smooth muscle contraction                  & 4/140 & 2.521912e-03 & GNA12;ARAF;RAF1;CALM1                             \\\\\n\t Long-term potentiation                              & 3/67  & 2.544059e-03 & ARAF;RAF1;CALM1                                   \\\\\n\t Renal cell carcinoma                                & 3/68  & 2.653772e-03 & ARAF;PDGFB;RAF1                                   \\\\\n\t Parkinson disease                                   & 4/144 & 2.791925e-03 & NDUFA9;GNAI3;SDHD;ATP5F1                          \\\\\n\t Retrograde endocannabinoid signaling                & 4/150 & 3.233136e-03 & GABRA2;NDUFA9;GNAI3;GABRG1                        \\\\\n\t Human cytomegalovirus infection                     & 5/255 & 3.776783e-03 & GNA12;GNAI3;CCL3;RAF1;CALM1                       \\\\\n\t Cell adhesion molecules (CAMs)                      & 4/170 & 5.043483e-03 & ITGB2;ICAM2;ITGB7;ICOSL                           \\\\\n\t Gap junction                                        & 3/86  & 5.144036e-03 & GNAI3;PDGFB;RAF1                                  \\\\\n\t cGMP-PKG signaling pathway                          & 4/172 & 5.255241e-03 & GNA12;GNAI3;RAF1;CALM1                            \\\\\n\t Alzheimer disease                                   & 4/175 & 5.583934e-03 & NDUFA9;SDHD;ATP5F1;CALM1                          \\\\\n\t GABAergic synapse                                   & 3/90  & 5.837183e-03 & GABRA2;GNAI3;GABRG1                               \\\\\n\t GnRH signaling pathway                              & 3/90  & 5.837183e-03 & MMP14;RAF1;CALM1                                  \\\\\n\t Progesterone-mediated oocyte maturation             & 3/90  & 5.837183e-03 & ARAF;GNAI3;RAF1                                   \\\\\n\t ⋮ & ⋮ & ⋮ & ⋮\\\\\n\t Toll-like receptor signaling pathway                     & 1/99   & 0.3315227 & CCL3     \\\\\n\t Cytokine-cytokine receptor interaction                   & 2/292  & 0.3316813 & NGFR;CCL3\\\\\n\t T cell receptor signaling pathway                        & 1/101  & 0.3369534 & RAF1     \\\\\n\t Aldosterone synthesis and secretion                      & 1/102  & 0.3396524 & CALM1    \\\\\n\t Glucagon signaling pathway                               & 1/102  & 0.3396524 & CALM1    \\\\\n\t Longevity regulating pathway                             & 1/102  & 0.3396524 & PPARG    \\\\\n\t NF-kappa B signaling pathway                             & 1/102  & 0.3396524 & TRIM25   \\\\\n\t Pancreatic secretion                                     & 1/105  & 0.3476843 & SLC26A3  \\\\\n\t Toxoplasmosis                                            & 1/108  & 0.3556198 & GNAI3    \\\\\n\t TNF signaling pathway                                    & 1/110  & 0.3608570 & MMP14    \\\\\n\t Cholinergic synapse                                      & 1/113  & 0.3686342 & GNAI3    \\\\\n\t Glutamatergic synapse                                    & 1/114  & 0.3712057 & GNAI3    \\\\\n\t Ribosome biogenesis in eukaryotes                        & 1/115  & 0.3737669 & NHP2     \\\\\n\t Oocyte meiosis                                           & 1/116  & 0.3763179 & CALM1    \\\\\n\t Platelet activation                                      & 1/125  & 0.3988190 & GNAI3    \\\\\n\t AMPK signaling pathway                                   & 1/126  & 0.4012691 & PPARG    \\\\\n\t Inflammatory mediator regulation of TRP channels         & 1/127  & 0.4037094 & CALM1    \\\\\n\t Osteoclast differentiation                               & 1/128  & 0.4061398 & PPARG    \\\\\n\t Autophagy                                                & 1/130  & 0.4109713 & RAF1     \\\\\n\t Spliceosome                                              & 1/132  & 0.4157639 & SNRNP27  \\\\\n\t Signaling pathways regulating pluripotency of stem cells & 1/137  & 0.4275778 & RAF1     \\\\\n\t Measles                                                  & 1/144  & 0.4437217 & CCND2    \\\\\n\t mTOR signaling pathway                                   & 1/154  & 0.4660072 & RAF1     \\\\\n\t Cushing syndrome                                         & 1/159  & 0.4768169 & GNAI3    \\\\\n\t RNA transport                                            & 1/167  & 0.4936646 & FMR1     \\\\\n\t Ribosome                                                 & 1/170  & 0.4998434 & RPL13    \\\\\n\t Herpes simplex virus 1 infection                         & 2/433  & 0.5261910 & ITGA5;CFP\\\\\n\t Calcium signaling pathway                                & 1/189  & 0.5372822 & CALM1    \\\\\n\t Epstein-Barr virus infection                             & 1/229  & 0.6072885 & CCND2    \\\\\n\t Olfactory transduction                                   & 1/1133 & 0.9912021 & CALM1    \\\\\n\\end{tabular}\n","text/plain":"    Term                                                     Overlap\n1   Regulation of actin cytoskeleton                         8/217  \n2   Rap1 signaling pathway                                   7/209  \n3   Parathyroid hormone synthesis, secretion and action      5/107  \n4   Long-term depression                                     4/61   \n5   Focal adhesion                                           6/199  \n6   Melanoma                                                 4/72   \n7   Glioma                                                   4/75   \n8   Pertussis                                                4/76   \n9   Pathways in cancer                                       9/535  \n10  Ras signaling pathway                                    6/233  \n11  Morphine addiction                                       4/92   \n12  PI3K-Akt signaling pathway                               7/357  \n13  MAPK signaling pathway                                   6/294  \n14  Natural killer cell mediated cytotoxicity                4/118  \n15  Estrogen signaling pathway                               4/134  \n16  Insulin signaling pathway                                4/139  \n17  Thermogenesis                                            5/231  \n18  Vascular smooth muscle contraction                       4/140  \n19  Long-term potentiation                                   3/67   \n20  Renal cell carcinoma                                     3/68   \n21  Parkinson disease                                        4/144  \n22  Retrograde endocannabinoid signaling                     4/150  \n23  Human cytomegalovirus infection                          5/255  \n24  Cell adhesion molecules (CAMs)                           4/170  \n25  Gap junction                                             3/86   \n26  cGMP-PKG signaling pathway                               4/172  \n27  Alzheimer disease                                        4/175  \n28  GABAergic synapse                                        3/90   \n29  GnRH signaling pathway                                   3/90   \n30  Progesterone-mediated oocyte maturation                  3/90   \n⋮   ⋮                                                        ⋮      \n156 Toll-like receptor signaling pathway                     1/99   \n157 Cytokine-cytokine receptor interaction                   2/292  \n158 T cell receptor signaling pathway                        1/101  \n159 Aldosterone synthesis and secretion                      1/102  \n160 Glucagon signaling pathway                               1/102  \n161 Longevity regulating pathway                             1/102  \n162 NF-kappa B signaling pathway                             1/102  \n163 Pancreatic secretion                                     1/105  \n164 Toxoplasmosis                                            1/108  \n165 TNF signaling pathway                                    1/110  \n166 Cholinergic synapse                                      1/113  \n167 Glutamatergic synapse                                    1/114  \n168 Ribosome biogenesis in eukaryotes                        1/115  \n169 Oocyte meiosis                                           1/116  \n170 Platelet activation                                      1/125  \n171 AMPK signaling pathway                                   1/126  \n172 Inflammatory mediator regulation of TRP channels         1/127  \n173 Osteoclast differentiation                               1/128  \n174 Autophagy                                                1/130  \n175 Spliceosome                                              1/132  \n176 Signaling pathways regulating pluripotency of stem cells 1/137  \n177 Measles                                                  1/144  \n178 mTOR signaling pathway                                   1/154  \n179 Cushing syndrome                                         1/159  \n180 RNA transport                                            1/167  \n181 Ribosome                                                 1/170  \n182 Herpes simplex virus 1 infection                         2/433  \n183 Calcium signaling pathway                                1/189  \n184 Epstein-Barr virus infection                             1/229  \n185 Olfactory transduction                                   1/1133 \n    P.value      Genes                                             \n1   2.751245e-06 FGF6;ITGB2;GNA12;ARAF;PDGFB;ITGB7;ITGA5;RAF1      \n2   2.223718e-05 FGF6;NGFR;ITGB2;GNAI3;PDGFB;RAF1;CALM1            \n3   7.399918e-05 MMP14;GNA12;ARAF;GNAI3;RAF1                       \n4   1.093080e-04 GNA12;ARAF;GNAI3;RAF1                             \n5   1.571228e-04 CCND2;CAV2;PDGFB;ITGB7;ITGA5;RAF1                 \n6   2.083409e-04 FGF6;ARAF;PDGFB;RAF1                              \n7   2.438882e-04 ARAF;PDGFB;RAF1;CALM1                             \n8   2.566497e-04 ITGB2;GNAI3;ITGA5;CALM1                           \n9   3.092998e-04 FGF6;CCND2;GNA12;ARAF;GNAI3;PDGFB;PPARG;RAF1;CALM1\n10  3.671621e-04 FGF6;NGFR;RAB5B;PDGFB;RAF1;CALM1                  \n11  5.322212e-04 GABRA2;GNAI3;OPRM1;GABRG1                         \n12  6.100600e-04 FGF6;NGFR;CCND2;PDGFB;ITGB7;ITGA5;RAF1            \n13  1.236093e-03 FGF6;NGFR;GNA12;ARAF;PDGFB;RAF1                   \n14  1.350028e-03 ITGB2;ICAM2;ARAF;RAF1                             \n15  2.151432e-03 GNAI3;OPRM1;RAF1;CALM1                            \n16  2.457330e-03 ARAF;RAF1;CALM1;HK2                               \n17  2.473460e-03 NDUFA9;SMARCB1;PPARG;SDHD;ATP5F1                  \n18  2.521912e-03 GNA12;ARAF;RAF1;CALM1                             \n19  2.544059e-03 ARAF;RAF1;CALM1                                   \n20  2.653772e-03 ARAF;PDGFB;RAF1                                   \n21  2.791925e-03 NDUFA9;GNAI3;SDHD;ATP5F1                          \n22  3.233136e-03 GABRA2;NDUFA9;GNAI3;GABRG1                        \n23  3.776783e-03 GNA12;GNAI3;CCL3;RAF1;CALM1                       \n24  5.043483e-03 ITGB2;ICAM2;ITGB7;ICOSL                           \n25  5.144036e-03 GNAI3;PDGFB;RAF1                                  \n26  5.255241e-03 GNA12;GNAI3;RAF1;CALM1                            \n27  5.583934e-03 NDUFA9;SDHD;ATP5F1;CALM1                          \n28  5.837183e-03 GABRA2;GNAI3;GABRG1                               \n29  5.837183e-03 MMP14;RAF1;CALM1                                  \n30  5.837183e-03 ARAF;GNAI3;RAF1                                   \n⋮   ⋮            ⋮                                                 \n156 0.3315227    CCL3                                              \n157 0.3316813    NGFR;CCL3                                         \n158 0.3369534    RAF1                                              \n159 0.3396524    CALM1                                             \n160 0.3396524    CALM1                                             \n161 0.3396524    PPARG                                             \n162 0.3396524    TRIM25                                            \n163 0.3476843    SLC26A3                                           \n164 0.3556198    GNAI3                                             \n165 0.3608570    MMP14                                             \n166 0.3686342    GNAI3                                             \n167 0.3712057    GNAI3                                             \n168 0.3737669    NHP2                                              \n169 0.3763179    CALM1                                             \n170 0.3988190    GNAI3                                             \n171 0.4012691    PPARG                                             \n172 0.4037094    CALM1                                             \n173 0.4061398    PPARG                                             \n174 0.4109713    RAF1                                              \n175 0.4157639    SNRNP27                                           \n176 0.4275778    RAF1                                              \n177 0.4437217    CCND2                                             \n178 0.4660072    RAF1                                              \n179 0.4768169    GNAI3                                             \n180 0.4936646    FMR1                                              \n181 0.4998434    RPL13                                             \n182 0.5261910    ITGA5;CFP                                         \n183 0.5372822    CALM1                                             \n184 0.6072885    CCND2                                             \n185 0.9912021    CALM1                                             "},"metadata":{}}]},{"metadata":{"trusted":false},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"name":"ir","display_name":"R","language":"R"},"language_info":{"name":"R","codemirror_mode":"r","pygments_lexer":"r","mimetype":"text/x-r-source","file_extension":".r","version":"4.0.3"}},"nbformat":4,"nbformat_minor":2}