Group Test for RNASeq
For microarrays, the abundance of a particular transcript is measured as a fluorescence intensity, effectively a continuous response, whereas for digital gene expression (DGE) data the abundance is observed as a count. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. The software package edgeR (empirical analysis of DGE in R), which forms part of the Bioconductor project, is designed to examine differential expression of count-based expression data between two or more groups.
The Group Test for RNASeq analysis is recommended to be performed on high-dimensional data nodes containing RNASeq-based read count observations. The results of the analysis comprise an ordered table of the differentially expressed genes (or tags, or exons, etc.) and plots visualizing the level of (dis)similarity of individual samples (MDS plot) as well as the DGE data (MA plot).
To begin the analysis, see Running the Analyses, then perform the following steps.
To perform a Group Test for RNASeq analysis:
Click the Advanced Workflow tab, and then open the Analysis menu.
Select Group Test for RNASeq.
The Variable Selection section appears.
Define the following variables:
- RNASeq: A high-dimensional data node containing RNASeq-based read count data.
- Group: Categorical data nodes separating the samples into two or more groups.
- Analysis Type: Select the type of analysis to perform:
- two group unpaired
Click Run Analysis. As this may take a while, consider selecting the option Run Job in Background in the popup window. The analysis can be retrieved at a later time in the Analysis Jobs tab.
Results appear in two sections:
- An ordered table of the differentially expressed genes (or tags or exons, etc.) including fault changes, abundances, p-values, and false discovery rates.
- An MDS plot visualizing the level of (dis)similarity of individual samples, and an MA plot (fold change versus abundance) visualizing the RNASeq data.
- Mark D. Robinson, Davis J. McCarthy and Gordon K. Smyth (2009) “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics (2010) 26 (1): 139-140.