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# 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 ``` &nbsp; 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 ```