Because different samples have different total numbers of reads, it would be misleading to calculate differential expression by comparing read count numbers for genes across samples without normalizing for the total number of reads. 

• Click the Filtered counts data node
• Click Normalization and scaling in the task menu
• Click Normalization (Figure 1)
  
  

Figure 1. Invoking Normalize counts

The Count normalization menu will open (Figure 2).

 

Figure 2. Read counts normalization task menu

Normalization can be performed by sample or by feature. By sample is selected by default; this is appropriate for the tutorial data set. 

Available normalization methods are listed in the left-hand panel. For more information about these options, please see the Normalize counts user guide.

For this tutorial, we will use the recommended default normalization settings.

• Select

This adds  the Median ratio normalization method, which is suitable for performing differential expression analysis using DESeq2 (Figure 3). 

Figure 3. Recommended normalization settings

• Click Finish to perform normalization

A Normalize counts task node and a Normalized counts data node are added to the pipeline (Figure 4)

Figure 4. Normalize counts task node and Normalized counts data node

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