Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR
Differential expression analysis of RNA-seq is one of the most commonly performed bioinformatics analyses. Transcript-level quantifications are inherently more uncertain than gene-level read counts..
Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods
In the past 5 years, RNA-Seq approaches, based on high-throughput sequencing technologies, are becoming an essential tool in transcriptomics studies. It is now commonly accepted that a normalization preprocessing step can significantly improve the quality of the analysis, in particular, for the differential gene expression analysis. Nevertheless, a gold standard normalization method has not yet been found. Université de Toulouse researchers compared two widely used and very important normalization methods and a third method related to these. The first method is the “Trimmed Mean of M-values” normalization (TMM) described in and implemented in the edgeR package. The second method is the “Relative Log Expression” normalization (RLE) implemented in the DESeq2 package. The third method is the “Median Ratio Normalization” (MRN). It has been shown that TMM and RLE give similar results both with real and simulated data sets. These two methods, as does MRN, deal efficiently with the intrinsic bias ...
RNA-seq analysis is easy as 1-2-3 with limma, Glimma and edgeR
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, researchers from the University of Melbourne 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 ...
SARTools – A DESeq2- and EdgeR-Based R Pipeline for Comprehensive Differential Analysis of RNA-Seq Data
Several R packages exist for the detection of differentially expressed genes from RNA-Seq data. The analysis process includes three main steps, namely normalization, dispersion estimation and test for differential expression. Quality control steps along this process are recommended but not mandatory, and failing to check the characteristics of the dataset may lead to spurious results. In addition, normalization methods and statistical models are not exchangeable across the packages without adequate transformations the users are often not aware of. Thus, dedicated analysis pipelines are needed to include systematic quality control steps and prevent errors from misusing the proposed methods. SARTools is an R pipeline for differential analysis of RNA-Seq count data. It can handle designs involving two or more conditions of a single biological factor with or without a blocking factor (such as a batch effect or a sample pairing). It is based on DESeq2 and edgeR and is composed of ...
Analysis example for RNA-Seq experiments using the bioconductor package edgeR
from RPubs by Alex Chitsazan Overview This is a script that will do differential gene expression (DGE) analysis for RNA-seq experiments using the bioconductor package edgeR. Project Details To give background on this project, this is a RNA-seq project that is being done in planarian, Schmidtea mediterranea, a flatworm that has remarkable regeneration properties. This project is being done in the lab of Alejandro Sanchez Alvarado, an HHMI funded lab at Stowers Institute for Medical Research. More knowledge of the lab and of planaria can be found here. The primary biochemical research is being done by a postdoc, while all bioinformatics and NGS analysis was done by myself, Alex Chitsazan. This project is an RNA-seq experiment being done to look at the differential gene expression levels of an RNAi knockdown of the methyltransferace mll, compared to an RNAi control, unc, looking at the whole worm (WW). This was done at ...
edgeRun – an R package for sensitive, functionally relevant differential expression discovery using an unconditional exact test
Next-generation sequencing platforms for measuring digital expression such as RNA-Seq are displacing traditional microarray-based methods in biological experiments. The detection of differentially expressed genes between groups of biological conditions has led to the development of numerous bioinformatics tools, but so far few, exploit the expanded dynamic range afforded by the new technologies. Researchers at the Harvard School of Public Health have developed edgeRun, an R package that implements an unconditional exact test that is a more powerful version of the exact test in edgeR. This increase in power is especially pronounced for experiments with as few as 2 replicates per condition, for genes with low total expression and with large biological coefficient of variation. In comparison with a panel of other tools, edgeRun consistently captures functionally similar differentially expressed genes. Availability - The package is freely available under the MIT license from CRAN (http://cran.r-project.org/web/packages/edgeRun) Contact - edimont@mail.harvard.edu. [box type="shadow" align="alignleft" ...
shRNA-seq data analysis with edgeR
Pooled short hairpin RNA sequencing (shRNA-seq) screens are becoming increasingly popular in functional genomics research, and there is a need to establish optimal analysis tools to handle such data. This open-source shRNA processing pipeline in edgeR provides a complete analysis solution for shRNA-seq screen data, that begins with the raw sequence reads and ends with a ranked lists of candidate shRNAs for downstream biological validation. First, the raw data contained in a fastq file is summarized into a matrix of counts (samples in the columns, hairpins in the rows) with options for allowing mismatches and small shifts in hairpin position. Diagnostic plots, normalization and differential representation analysis can then be performed using established methods to prioritize results in a statistically rigorous way, with the choice of either the classic exact testing methodology or a generalized linear modelling that can handle complex experimental designs. A detailed users' guide that demonstrates how ...
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