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+<!-- README.md is generated from README.Rmd. Please edit that file -->
+
+# INDEED
+
+## Overview
+
+This R package implements INDEED algorithm from Zuo *et. al.*’s Methods
+paper, INDEED: Integrated differential expression and differential
+network analysis of omic data for biomarker discovery ([PMID:
+27592383](https://www.ncbi.nlm.nih.gov/pubmed/?term=27592383%5Buid%5D)).
+
+This R package will generate a list of dataframes containing information
+such as p-value, node degree and activity score for each biomolecule. A
+higher activity score indicates that the corresponding biomolecule has
+more neighbors connected in the differential network and their p-values
+are more statistically significant. It will also generate a network
+display to aid users’ biomarker selection.
+
+## Comparison with competing methods
+
+A comparison between INDEED and two competing methods ([DNAPATH](https://cran.r-project.org/web/packages/dnapath/index.html) and [JDINAC](https://github.com/jijiadong/JDINAC)) has been performed in a [simulation study](https://github.com/Hurricaner1989/INDEED-simulation). Metrics such as precision recall AUC, precision, recall and run time are computed under $n < p$, $n = p$, and $n > p$ conditions. We encourage users who are interested in a more thorough comparison to build on top of our simulation study and try to perform the comparison by themselves. 
+
+## Installation
+
+You can install INDEED from github with:
+
+``` r
+# install.packages("devtools")
+devtools::install_github("ressomlab/INDEED")
+```
+
+## Load package
+
+Load the package.
+
+``` r
+# load INDEED
+library(INDEED)
+#> Loading required package: glasso
+```
+
+## Examples
+
+A testing dataset has been provided to the users to get familiar with
+INDEED R package. It contains the expression levels of 39 metabolites
+from 120 subjects (CIRR: 60; HCC: 60) with CIRR group named as group 0
+and HCC group named as group 1.
+
+``` r
+# Data matrix contains the expression levels of 39 metabolites from 120 subjects 
+# (6 metabolites and 10 subjects are shown)
+head(Met_GU[, 1:10])
+#>            X1         X2         X3        X4          X5         X6
+#> 1 -1.17784288 -0.6524507  0.1130101 0.3273883 -0.81597223 0.91690985
+#> 2 -0.74465547 -0.8403552  1.2275791 1.4884276  0.95811649 0.22175791
+#> 3  1.02005243  1.6526556  0.4660893 1.4657142  1.15495800 0.66656520
+#> 4  0.40435337  0.4216086  0.3728297 0.4413724  0.41055731 0.39239917
+#> 5  1.27026847  1.5406950 -0.1213972 1.0226981 -1.41568157 0.02338627
+#> 6  0.04855234  0.6102747  1.0018852 0.8012087  0.03375084 0.29277059
+#>            X7          X8         X9        X10
+#> 1 -0.10606357 -0.14868927 -0.7536426  1.9331369
+#> 2  0.57873922 -0.04059911 -0.3448051 -0.3943420
+#> 3 -0.02235966 -0.25240024  0.6314481 -0.2927764
+#> 4  0.34483591  0.64974659  0.3820917  0.3832617
+#> 5  2.19089662 -0.80789325  0.1743634  1.2832645
+#> 6  0.20963886  0.25854132  0.8692107 -0.5259235
+# Group label for each subject (40 subjects are shown)
+Met_Group_GU[1:40]
+#>   X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21
+#> 1  0  0  0  0  0  0  0  0  0   0   0   0   0   0   0   0   0   0   0   1   1
+#>   X22 X23 X24 X25 X26 X27 X28 X29 X30 X31 X32 X33 X34 X35 X36 X37 X38 X39 X40
+#> 1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1
+# Metabolite KEGG IDs (10 metabolites are shown)
+Met_name_GU[1:10]
+#>  [1] "C00009" "C00022" "C00025" "C00049" "C00064" "C00065" "C00086" "C00097"
+#>  [9] "C00124" "C00148"
+```
+
+An example to obtain the differential network using partial correlation
+analysis.
+
+``` r
+# set seed to avoid randomness
+set.seed(100)
+# Compute rho values to run graphical lasso
+pre_data <- select_rho_partial(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU, error_curve = TRUE)
+```
+
+![](figure/rho-selection-1.png)<!-- -->
+
+From the error curve figure, users can choose the rho value based on the
+minimum rule (red vertical line), the one standard error rule (blue
+horizontal line) or their preferred value. INDEED provides users the
+option to adjust multiple testing effect in edge detection (fdr = TRUE).
+This will lead to a more sparse network in general. In this example, the
+network is too sparse. We decide to set fdr = FALSE for demonstration.
+It’s a good idea to start by setting fdr = TRUE and later relax it to
+fdr = FALSE if the network is too sparse when working on a new dataset.
+
+``` r
+# Choose optimal rho values to compute activity scores and build the differential network
+result <- partial_cor(data_list = pre_data, rho_group1 = 'min', rho_group2 = "min", p_val = pvalue_M_GU, permutation = 1000, permutation_thres = 0.05, fdr = FALSE)
+```
+
+Show the network display and users can interact with it.
+
+``` r
+# Show result 
+head(result$activity_score)
+#>   Node     ID P_value Node_Degree Activity_Score
+#> 1   12 C00183   0.000           3            8.2
+#> 2   15 C00188   0.487           3            8.1
+#> 3    5 C00064   0.015           3            7.7
+#> 4   18 C00247   0.889           6            6.4
+#> 5    8 C00097   0.578           6            6.0
+#> 6   16 C00189   0.016           5            6.0
+head(result$diff_network)
+#>   Node1 Node2 Binary    Weight
+#> 1     1    19      1  2.290368
+#> 2     2     8     -1 -2.290368
+#> 3     2    31      1  2.226212
+#> 4     2    33     -1 -4.635348
+#> 5     3    22      1  2.033520
+#> 6     4    33     -1 -2.457263
+# Show network
+network_display(result = result, nodesize= 'Node_Degree', nodecolor= 'Activity_Score', edgewidth= FALSE, layout= 'nice')
+```
+
+<!-- Network display image was generated from somewhere else -->
+
+![](figure/network_display_partial.png)<!-- -->
+
+An example to obtain the differential network using correlation
+analysis. When the partial correlation analysis returns a too sparse
+network even when the multiple testing correction is turned off (fdr =
+FALSE). It’s better to try correlation analysis.
+
+``` r
+# set seed to avoid randomness
+set.seed(100)
+# Compute rho values to run graphical lasso
+result <- non_partial_cor(data = Met_GU, class_label = Met_Group_GU, id = Met_name_GU, method = "pearson", p_val = pvalue_M_GU, permutation = 1000, permutation_thres = 0.05, fdr = FALSE)
+```
+
+Show the network display and users can interact with it. Here, edgewidth
+is assigned to the significance level of the differential connection
+(z-score of edge connection with different colors for positive or
+negative changes).
+
+``` r
+# Show result 
+head(result$activity_score)
+#>   Node     ID P_value Node_Degree Activity_Score
+#> 1   22 C00581   0.074           5           11.9
+#> 2   15 C00188   0.487           5           10.0
+#> 3   31 C02497   0.537           5            8.6
+#> 4   21 C00383   0.001           5            8.5
+#> 5   13 C00186   0.388           5            7.8
+#> 6   16 C00189   0.016           3            7.2
+head(result$diff_network)
+#>   Node1 Node2 Binary    Weight
+#> 1     1    19      1  2.512144
+#> 2     4    18      1  2.326348
+#> 3     5    13     -1 -2.197286
+#> 4     5    22     -1 -2.033520
+#> 5     5    34      1  4.635348
+#> 6     6    31     -1 -2.257129
+# Show network
+network_display(result = result, nodesize= 'Node_Degree', nodecolor= 'Activity_Score', edgewidth= TRUE, layout= 'nice')
+```
+
+<!-- Network display image was generated from somewhere else -->
+
+![](figure/network_display_correlation.png)<!-- -->