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Day 9: RNA-Seq II, Differential Expression Analysis

Day 9: RNA-Seq II, Differential Expression Analysis

Welcome to Day 9 of the Short-Read Sequencing Analysis Workshop. The second day of the RNA-Seq section of the workshop will focus on differential expression analysis methods. We will be using two different methods based on different ways to measure expression levels. The first program we will use is Cufflinks, which is based on using FPKM as a measure of expression levels, and the second program, DESeq, uses read counts as a measure of expression levels. We have included several papers that have looked at these differences in the Additional Resources section of this page.


Videos for Day 9

Video 1: Introduction to differential expression analysis (6:35) This video introduces differential expression analysis and the two programs we will be using in this workshop, DESeq and Cufflinks.

Video 2: Differential expression using DESeq  **Please note that this video is from 2015, although the same version of DESeq will be used in class. The input count data table that Mary uses in the video was created using BEDTools, we will be using HTSeq-counts to generate the count table from the Tophat2 alignment in the workshop. This video will be reviewing some of the concepts of differential expression with RNA-Seq data, starting with how programs like DESeq estimate dispersion of natural variability instead of using fold-change to call differentially expressed transcripts. You will be introduced to the R programming package in order to work through a DESeq example.

Video 3: Using Cufflinks for differential expression analysis (13:36)  This video introduces the Cufflinks package, which includes a suite of programs for isoform detection and differential expression. The Cufflinks, Cuffmerge, and Cuffdiff 2 programs are introduces along with some basic options.


Files for Day 9

          Day 9 In-class slides


Additional Resources

DESeq Documentation 

The R Project This is the website for the R Project, you can read about using R, get the manual, and download R from this site. If you are interested in downloading and running R on your own computer RStudio has a nice desktop program that is free. 

Cufflinks Documentation This website has documentation for Cufflinks, Cuffmerge and Cuffdiff, all of which are part of the Cufflinks package. Here is a link to the Cuffdiff 2 paper.

Conesa, et al (2016) Genome Biology. 17:13. This is a great paper reviewing the best practices for RNA-seq analysis for several different applications. I highly recommend reading through this before planning any RNA-seq experiment.

Recent papers comparing DE methods

Anders, et al (2013) Nature Protocols 8: 1765–1786.  This is an older paper but it has a detailed protocol for running both edgeR and DESeq, this would be a useful resource to try comparing the outputs of both programs for your own data sets.

Sayednasrollah, et al (2015) Brief Bioinform. 16 (1): 59-70.This paper is an update of a comparison study performed in 2013. It compares edgeR, DESeq, baySeq, NOIseq, SAMseq, limma, Cuffdiff 2, and EBSeq using real data sets from mouse and human. It has a very good description of each program and is one of the few studies to look at real data as opposed to simulated data.

Schurch, et al (2016) RNA. 22: 839-851. This paper is the first to examine the influence of biological replicates on the performance of DE programs. It compares baySeq, Cuffdiff, DEGSeq, DESeq, DESeq2, EBSeq, edgeR, limma, NOIseq, PoissonSeq, and SAMSeq using a set of 48 biological replicates from 2 yeast strains.

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