DGE.Tools2: RNA-Seq Analysis Workflow Package (deprecated: See DGEobj, DGEobj.utils and DGEobj.plots)

DGE.Tools2 is a suite of functions to facilitate and standardize RNA-Seq DGE analysis. DGE.Tools relies on the DGEobj data structure to store DGE data and analysis results.

DGE workflow:

DGE.Tools2 has modular functions to conduct DGE analysis from counts to contrasts with facility to select detected genes, normalize data (EdgeR TMM), linear modeling (limma voom and lmFit), and contrast analysis (topTable, topTreat). This process is broken logically into steps so that it is easy to, for example, substitute in a new or customized normalization step and still be able to take advantage of the other pieces of the pipeline. The DGE.Tools workflow include support for qualityWeights, duplicateCorrelation and SVA analysis. The most important reason to use DGE.Tools2 is that it produces a standardized data object, the DGEobj, that captures and annotates your workstream making your data better documented and easier to incorporate into downstream integrative analyses.

Standard data structures for DGE:

DGE.Tools2 uses an S3 data class called DGEobj to capture results in a customizable and reusable data object.
See the DGEobj package for more details.

QC Plots:

Several QC plots are availble to monitor the quality of your results. These include:

EdgeR dispersion plot
voom Mean-Variance plot
plotPvalHist: Faceted plot of pvalue distributions for each contrast to evaluate quality of your Fit.
cdfPlot: Faceted plot of pvalue distributions for each contrast to evaluate quality of your Fit. QCplots: Plot alignment metrics from Omicsoft or Xpress

Data Exploration Plots:

profilePlot: Plot LogIntensity vs. LogRatio from topTable dataframes with highlighting of significantly regulated genes.
volcanoPlot: Plot LogRatio vs. NegLogPvalue from topTable dataframes with highlighting of significantly regulated genes.
comparePlot: Compare LogRatios for two samples showing common or uniquely regulated genes
ggplotMDS: Run Multi-Dimentional Scaling analysis and plot the results

Other Documentation

• DGE_Tools_Training_Mar2019.pptx
• vignettes/DGE.Tools2_Workflow.pdf: Workflow example
• vignettes/DGE.ToolsPlotGallery.pdf: code examples for data exploration plots

Installation

It is best to run the install from a fresh R session before loading any packages because loaded packages cannot be updated.

    require(devtools)
    devtools::install_git("https://github.com/jrthompson54/DGE.Tools2")