Differential Expression Detection

Begin your RNASeq analysis by testing for differential expression of all of the unique reads. To do this, we will use a module called edgeR, based on the Empirical Analysis of Digital Gene Expression data in R package written by Mark Robinson.

  1. In the row of colorful buttons across the top of the MultiExperiment Viewer window, EdgeR module selection: The edgeR module can be found in the Statistics drop-down menu.click the one labeled Statistics. Choose Empirical Analysis of Digital Gene Expression data in R (edgeR). An initialization dialog will appear. 
  2. Select the group membership for each of the six samples. Click "Group 1" for the first four samples, and "Group 2" for the remaining two samples.
  3. Leave the default values for the Inference Algorithm and p-value/FDR parameters.
  4. Click Ok. The analysis will run and display the results in the result tree, on the left of the Multiple Array Viewer window.

EdgeR Initialization Dialog: The edgeR initialization dialog.

 

 

 

Differential Expression Results

 

  1. Open up the result node labeled edgeR, and expand the nodes to find one labeled Significant Gene List. Click on this node to select it and display the list of genes found to be differentially expressed between the two sample groups you selected in the previous section. You can click on the links to launch a web browser displaying more information about individual genes. 
  2. EdgeR Output: Results of the edgeR module, showing significantly differentially expressed genes/transcripts. Right-click to reveal a context menu with many powerful options.Right-click on the window in a cell with no links (the Stored Color column is a good bet). Choose Store entire cluster and click Ok to label each of the genes in this window with a color. This color label will be visible anywhere a gene display is shown in MeV - even in the results of other modules.