README.md

In SCBIT-YYLab/DysRegSig: Gene dysregulation analysis and construction of mechanistic signature in cancer

DysRegSig

DysRegSig is capable of robustly exploring gene dysregulations from high-dimensional expression data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. DysRegSig also offers tools to rank dysregulated regulations and TFs, and construct mechanistic signature with gene dysregulations by using genetic algorithm.

Installation

DysRegSig dependents packages Boruta, RGBM, glmnet, expm, flare, limma, ggpubr, ggplot2, reshape2, igraph, survival, survcomp, survminer, ROCR, pROC, e1071, and so on. At first, Bioconductor packages need to be pre-installed.

```{r, eval = FALSE} if(!require(BiocManager)) install.packages("BiocManager") library(BiocManager) install(c('limma','survcomp'))

Then use devtools to install DysRegSig package from github.

```{r, eval = FALSE}
if(!require(devtools)) install.packages("devtools")
devtools::install_github('SCBIT-YYLab/DysRegSig')

Users could also download DysRegSig_2.2.3.tar.gz, and install DysRegSig package from local path.

```{r, eval = FALSE} install.packages('DysRegSig_2.2.3.tar.gz', type = 'source',repos = NULL)

```{r, eval = FALSE}
library(DysRegSig)

Quick start

In DysRegSig, the main function for gene dysregulation analysis is DysReg, which could idnetify gene dysregulations from high-dimensional data while considering the cooperativity and synergy between regulators to target. DysReg first build conditional GRNs with tree-based feature selection algorithm, where regulatory intensity and its confidential interval of each linkis estimated with a de-biased LASSO method. Gene dysregulations were then identified by integrating three properties including differential regulation, differential expression of target, and the consistency between differential regulation and differential expression.

```{r, eval = FALSE} data(ExpData) ExpData[1:5,1:5]

data(tf2tar) head(tf2tar)

data(ClinData) head(ClinData)

group.1 <- ClinData$sample[which(ClinData$binaryResponse == 'CR/PR')] exp.1 <- ExpData[,colnames(ExpData) %in% group.1]

group.2 <- ClinData$sample[which(ClinData$binaryResponse == 'SD/PD')] exp.2 <- ExpData[,colnames(ExpData) %in% group.2]

dysreg.out <- DysReg(exp.1 = exp.1, exp.2 = exp.2, tf2tar, de.genes = NULL, de.pval = 0.05, grn.method = 'Boruta', pValue = 0.01, ci = 0.90, verbose = T)

dysreg.res <- dysreg.out$dysreg head(dysreg.res)

The expression pattern of two genes in one gene dysregulaiton could be visulazied by function `plotDysRegExp` (**Figure 1**). In order to more clearly visualize the differences of gene regulation between conditions, `plotDysRegExp` adds the regression lines and confidence interval shadows calculated by single variable regression for each condtion. 

```{r, eval = FALSE}
plotDysregExp(tf = dysreg.res$TF[1], tar = dysreg.res$Target[1],
              exp.1 = exp.1,exp.2 = exp.2, 
                exp1.label = 'Response', exp2.label = 'No-response',
              dysreg = dysreg.res, method ='DysReg', conf.int.level = 0.95)

Usage

This package offers three methods for gene dysreualtion analysis, and follow-up analysis toos. The more details about uasge of DysRegSig can be found:

{r, eval = FALSE} vignette("DysRegSig")

SCBIT-YYLab/DysRegSig documentation built on July 19, 2021, 4:38 a.m.