MetaVolcanoR

Gene expression meta-analysis visualization tool.

Overview

The MetaVolcanoR R package combines differential gene expression results. It implements three strategies to summarize gene expression activities from different studies. i) Random Effects Model (REM) approach. ii) a vote-counting approach, and iii) a combining-approach. MetaVolcano exploits the Volcano plot reasoning to visualize the gene expression meta-analysis results.

Installation

BiocManager::install('MetaVolcanoR')

Usage

Load required libraries.

library(MetaVolcanoR) 

Input Data

Users should provide a named list of data.table/data.frame objects containing differential gene expression results. Each object of the list must contain gene name, fold change, and p-value variables. It is highly recomended to also include variance or the confidence interval of the fold change variables.

Take a look at the demo data. It includes differential gene expression results of five studies.

data(diffexplist)

Random Effect Model MetaVolcano

The REM MetaVolcano summarizes the gene fold change of several studies taking into account the variance. The REM estimates a summary p-value which stand for the probability of the summary fold-change is not different than zero. Users can set the metathr parameter to highligth the top percentage of the most consistently perturbed genes. This perturbation ranking is defined following the topconfects approach.

meta_degs_rem <- rem_mv(diffexp=diffexplist,
            pcriteria="pvalue",
            foldchangecol='Log2FC', 
            genenamecol='Symbol',
            geneidcol=NULL,
            collaps=FALSE,
            llcol='CI.L',
            rlcol='CI.R',
            vcol=NULL, 
            cvar=TRUE,
            metathr=0.01,
            jobname="MetaVolcano",
            outputfolder=".", 
            draw='HTML',
            ncores=4)

# REM results
head(meta_degs_rem@metaresult, 3)

# Plot MetaVolcano
meta_degs_rem@MetaVolcano

  The REM MetaVolcano also allow users to explore the forest plot of a given gene based on the REM results.

draw_forest(remres=meta_degs_rem,
        gene="MMP9",
        genecol="Symbol", 
        foldchangecol="Log2FC",
        llcol="CI.L", 
        rlcol="CI.R",
        studynames=names(diffexplist),
        jobname=jobname,
        outputfolder=outputfolder,
        draw="HTML")

draw_forest(remres=meta_degs_rem,
        gene="COL6A6",
        genecol="Symbol", 
        foldchangecol="Log2FC",
        llcol="CI.L", 
        rlcol="CI.R",
        studynames=names(diffexplist),
        jobname=jobname,
        outputfolder=outputfolder,
        draw="HTML")

Vote-counting approach

MetaVolcano identifies differential expressed genes (DEG) for each study based on the user-defined p-value and fold change thresholds. It displays the number of differentially expressed and unperturbed genes per study. In addition, it plots the inverse cumulative distribution of the consistently DEG, so the user can identify the number of genes whose expression is perturbed in at least 1 or n studies.

meta_degs_vote <- votecount_mv(diffexp=diffexplist,
                   pcriteria='pvalue',
                   foldchangecol='Log2FC',
                   genenamecol='Symbol',
                   geneidcol=NULL,
                   pvalue=0.05,
                   foldchange=0, 
                   metathr=0.01,
                   collaps=FALSE,
                   jobname="MetaVolcano", 
                   outputfolder=".",
                   draw='HTML')

# Vote-counting results
head(meta_degs_vote@metaresult, 3)

# Plot DEG by study and DEG inverse cummulative distribution
meta_degs_vote@degfreq

The vote-counting MetaVolcano visualizes genes based on the number of studies where genes were identified as differentially expressed and the gene fold change sign consistency. It means that a gene that was differentially expressed in five studies, from which three of them it was downregulated, will get a sign consistency score of 2 + (-3) = -1. Based on the user preference, MetaVolcano can highligths the top metathr percentage of consistently perturbed genes.

# Plot MetaVolcano
meta_degs_vote@MetaVolcano

Combining-approach

The combinig MetaVolcano summarizes the fold change of a gene in different studies by the mean or median depending on the user preference. In addition, the combinig MetaVolcano summarizes the gene differential expression p-values using the Fisher method. The combining MetaVolcano can highligths the top metathr percentage of consistently perturbed genes.

meta_degs_comb <- combining_mv(diffexp=diffexplist,
                   pcriteria='pvalue', 
                   foldchangecol='Log2FC',
                   genenamecol='Symbol',
                   geneidcol=NULL,
                   metafc='Mean',
                   metathr=0.01, 
                   collaps=TRUE,
                   jobname="MetaVolcano",
                   outputfolder=".",
                   draw='HTML')

# Combining results
head(meta_degs_comb@metaresult, 3)

# Plot MetaVolcano
meta_degs_comb@MetaVolcano