RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR

Abstract

The ability to easily and efficiently analyse RNA-sequencing data is a key strength of the Bioconductor project. Starting with counts summarised at the gene-level, a typical analysis involves pre-processing, exploratory data analysis, differential expression testing and pathway analysis with the results obtained informing future experiments and validation studies. In this workflow article, we analyse RNA-sequencing data from the mouse mammary gland, demonstrating use of the popular edgeR package to import, organise, filter and normalise the data, followed by the limma package with its voom method, linear modelling and empirical Bayes moderation to assess differential expression and perform gene set testing. This pipeline is further enhanced by the Glimma package which enables interactive exploration of the results so that individual samples and genes can be examined by the user. The complete analysis offered by these three packages highlights the ease with which researchers can turn the raw counts from an RNA-sequencing experiment into biological insights using Bioconductor.

The histone methyltransferases Set5 and Set1 have overlapping functions in gene silencing and telomere maintenance

“…Reads were mapped to the S. cereviaise reference genome using Rsubread, the data were normalized within the limma package using voom and differential gene expression was determined using EdgeR. 46,47 Significantly differentially expressed (SDE) genes with a log 2 fold-change (FC) >1.2, P < 0.05 are listed in Table S2. The SDE genes of each mutant were used to calculate Spearman's rank correlation coefficients relative to the 700 responsive regulatory mutants identified by Kemmeren et al 48 in the R statistical environment.…”

Machine learning applied to whole‐blood RNA‐sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

“…Read counts were normalised by the upper-quartile method, to correct for differences in sequencing depth between samples, using edgeR. 53,54 Counts were log 2transformed with an offset of 1, and samples in each data set were computed as the log 2 fold-change (log 2 fc) against the matching healthy control group mean. These processing steps were useful to reduce the distracting effects of extreme values and skewness typically found in RNA-seq data.…”

“…The limma/edgeR workflow was used for differential expression analysis, considering each data set as a batch. 54 The EGSEA (v1.10.1) R package was used to statistically test for enrichment of gene expression sets, using a consensus of several gene set enrichment analysis tools. 59 EGSEA uses count data transformed with voom (a function of the limma package).…”

Bioinformatics analysis in menopause transition promotes distinct modulation in calvaria and bone marrow osteoblastic cells

“…If Student's t-test was used for differentially expressed genes analysis in microarray, setting P-value <0.01 as cutoff is the correct method. We recommend using limma (Linear Models for Microarray Analysis) (Law et al, 2016), a commonly used statistical test to analyze differential expression package by using linear models, and choosing more than 1.5-fold expression changes and false discovery rate (FDR) <0.05 as cutoff is an appropriate and conservative approach to obtain DEGs. Moreover, Significant Analysis of Microarray (SAM) (Tusher et al, 2001) is also a considerable non-parametric statistical algorithm, and twofold expression change and q < 0.1 is rational cutoff to obtain DEGs.…”