DESeq2
- kkennedy
Summary
Determines differential expression analysis on count based expression data sets.
Introduction
DESeq2 estimates differentially expressed gene lists based on a negative binomial distribution model. Previous methods for identifying differentially expressed gene lists assumed a Poisson distribution; however, Poisson does not account for variation (or overdispersion) found in expression data. DESeq2 uses a negative binomial distribution (similar to edgeR), assuming variance in the case of few replicates.
The input is a tab-delimited file containing genes and their expression values. The results include files detailing the results of differential expression testing (one that includes all of the results, and one that only includes the results that exceed a minimum false-discovery rate). Also included for visualization purposes are plots of the estimated dispersions, the log fold changes against the mean normalized counts and a histogram of p-values. The plots are purely for visualization purposes and may not be necessary for all users.
Reference:
Anders S1, Huber W. Differential expression analysis for sequence count data. Genome Biol. 2010;11(10):R106. doi: 10.1186/gb-2010-11-10-r106. Epub 2010 Oct 27. http://genomebiology.com/2010/11/10/R106
Quick Start
- To use DESeq2, your input file must be tab-delimited. You must also know the library type (either "single-end" or "paired-end") for each column in your input file.
- Resources: http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf
- Tool: https://bioconductor.org/packages/release/bioc/html/DESeq2.html
Test Data
Test data for this app appears directly in the Discovery Environment in the Data window under Community Data -> iplantcollaborative -> example_data -> DESeq.
Input File(s)
Use DESeq_test_data.tsv from the directory above as test input. This example data comes from the RNASeq Drosophilia example used in the DESeq paper.