---
title: 'MetaVolcanoR: Differential expression meta-analysis tool'
date: June 5, 2019
output:
html_document:
toc: yes
pdf_document:
toc: yes
prettydoc::html_pretty:
highlight: github
theme: lumen
toc: yes
vignette: >
%\VignetteIndexEntry{MetaVolcanoR: Differential expression meta-analysis tool}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r style, echo=FALSE, results="asis", message=FALSE}
knitr::opts_chunk$set(tidy = FALSE,
warning = FALSE,
message = FALSE,
cache=TRUE)
```
# Introduction
The measurement of how gene expression changes under different biological
conditions is necessary to reveal the gene regulatory programs that determine
the cellular phenotype.
Comparing the expresion of genes under a given condition against a reference
biological state is usually applied to identify sets of differentially
expressed genes (DEG). These DEG point out the genomic regions functionally
relevant under the biological condition of interest.
Athough individual genome-wide expression studies have small signal/noise ratio,
today's genomic data availability usually allows to combine differential gene
expression results from independent studies to overcome this limitation.
Databases such as GEO (https://www.ncbi.nlm.nih.gov/geo/), SRA
(https://www.ncbi.nlm.nih.gov/sra), ArrayExpress,
(https://www.ebi.ac.uk/arrayexpress/), and ENA (https://www.ebi.ac.uk/ena) offer
systematic access to vast amounts of transcriptome data. There exists more than
one gene expression study for many biological conditions. This redundancy could
be exploit by meta-analysis approaches to reveal genes that are consistently
and differentially expressed under given conditions.
MetaVolcanoR was designed to identify the genes whose expression is consistently
perturbed across several studies.
# Usage
## 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 p-value combining-approach. MetaVolcano exploits the
Volcano plot reasoning to visualize the gene expression meta-analysis results.
## Installation
```{r}
BiocManager::install("MetaVolcanoR", eval = FALSE)
```
## Load library
```{r}
library(MetaVolcanoR)
```
## Input Data
Users should provide a named list of \code{data.table/data.frame} objects
containing differential gene expression results. Each object of the list
must contain a *gene name*, a *fold change*, and a *p-value* variable. It
is highly recommended to also include the *variance* or the confidence
intervals of the *fold change* variable.
Take a look at the demo data. It includes differential gene expression results
from five studies.
```{r}
data(diffexplist)
class(diffexplist)
head(diffexplist[[1]])
length(diffexplist)
```
# Implemented meta-analysis approaches
## 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 stands
for the probability of the *summary fold-change* is not different than zero.
Users can set the *metathr* parameter to highlight the top percentage of the
most consistently perturbed genes. This perturbation ranking is defined
following the *topconfects* approach.
```{r}
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=1)
head(meta_degs_rem@metaresult, 3)
meta_degs_rem@MetaVolcano
draw_forest(remres=meta_degs_rem,
gene="MMP9",
genecol="Symbol",
foldchangecol="Log2FC",
llcol="CI.L",
rlcol="CI.R",
jobname="MetaVolcano",
outputfolder=".",
draw="HTML")
draw_forest(remres=meta_degs_rem,
gene="COL6A6",
genecol="Symbol",
foldchangecol="Log2FC",
llcol="CI.L",
rlcol="CI.R",
jobname="MetaVolcano",
outputfolder=".",
draw="HTML")
```
The *REM* MetaVolcano also allows users to explore the forest plot of a given
gene based on the REM results.
## Vote-counting approach
The *vote-counting* 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.
```{r}
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')
head(meta_degs_vote@metaresult, 3)
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 user preference, MetaVolcano can
highlight the top *metathr* percentage of consistently perturbed genes.
```{r}
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 highlight
the top *metathr* percentage of consistently perturbed genes.
```{r}
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')
head(meta_degs_comb@metaresult, 3)
meta_degs_comb@MetaVolcano
```