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b/ATAC/AnalysisPipeline/2.cluster.R |
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#' @description: cluster and dimension reduction |
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library(Signac) |
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library(Seurat) |
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library(harmony) |
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library(SeuratWrappers) |
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library(GenomeInfoDb) |
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library(EnsDb.Hsapiens.v86) #---GRCh38 (hg38) |
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library(ggplot2) |
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library(patchwork) |
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library(clustree) |
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set.seed(101) |
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library(future) |
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plan("multiprocess", workers = 4) |
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options(future.globals.maxSize = 50000 * 1024^2) #50G |
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library(GenomicRanges) |
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library(ggpubr) |
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source("/home/longzhilin/Analysis_Code/SingleCell/scATAC.Integrate.multipleSample.R") |
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setwd("/data/active_data/lzl/RenalTumor-20200713/DataAnalysis-20210803/scATAC") |
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scATAC.merge.pro <- readRDS("scATAC.merge.pro.rds") |
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#Normalization: Signac performs term frequency-inverse document frequency (TF-IDF) normalization. |
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#This is a two-step normalization procedure, that both normalizes across cells to correct for differences in cellular sequencing depth, and across peaks to give higher values to more rare peaks. |
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scATAC.merge.pro <- RunTFIDF(scATAC.merge.pro) |
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#Feature selection：though we note that we see very similar results when using only a subset of features (try setting min.cutoff to ‘q75’ to use the top 25% all peaks) |
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scATAC.merge.pro <- FindTopFeatures(scATAC.merge.pro, min.cutoff = 'q1') |
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saveRDS(scATAC.merge.pro, file = "scATAC.merge.pro.rds") |
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#####################1.observe batch effect######################## |
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#Dimension reduction: We next run singular value decomposition (SVD) on the TD-IDF matrix, using the features (peaks) selected above. |
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scATAC.merge.pro <- RunSVD(scATAC.merge.pro) |
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scATAC.merge.pro <- RunUMAP(scATAC.merge.pro, dims = 2:30, reduction = 'lsi') |
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scATAC.merge.pro <- RunTSNE(scATAC.merge.pro, dims = 2:30, reduction = 'lsi') |
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pdf("2.Cluster/observe.patient.effect.pdf") |
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ElbowPlot(object = scATAC.merge.pro, ndims = 30, reduction = "lsi") |
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DepthCor(scATAC.merge.pro) |
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DimPlot(scATAC.merge.pro, group.by = 'dataset', pt.size = 0.1, reduction = 'umap') |
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DimPlot(scATAC.merge.pro, group.by = 'dataset', pt.size = 0.1, reduction = 'tsne') |
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dev.off() |
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#####################2.correct batch effect######################## |
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DefaultAssay(scATAC.merge.pro) <- "ATAC" |
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pdf("2.Cluster/Harmony.integration.PC15.pdf") |
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Harmony.scATAC.PC15 <- Harmony.integration.reduceDimension(scATAC.object = scATAC.merge.pro, set.resolutions = seq(0.2, 1.2, by = 0.1), groups = "dataset", assay = "ATAC", PC = 15, npcs = 30) |
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dev.off() |
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saveRDS(Harmony.scATAC.PC15, file = "Harmony.scATAC.PC15.rds") |
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#####################3.gene activity######################## |
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#PC 15 |
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gene.activities <- GeneActivity(Harmony.scATAC.PC15) |
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# add the gene activity matrix to the Seurat object as a new assay and normalize it |
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Harmony.scATAC.PC15[['ACTIVITY']] <- CreateAssayObject(counts = gene.activities) |
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Harmony.scATAC.PC15 <- NormalizeData( |
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object = Harmony.scATAC.PC15, |
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assay = 'ACTIVITY', |
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normalization.method = 'LogNormalize', |
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scale.factor = median(Harmony.scATAC.PC15$nCount_ACTIVITY) |
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) |
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saveRDS(Harmony.scATAC.PC15, file = "Harmony.scATAC.PC15.rds") |