• Manuals

Browse the manual

• Introduction to the Advanced RNA-Seq plugin
  • Input data and normalization
    • Normalization
    • Metadata
  • PCA for RNA-Seq
    • Principal component analysis plot (2D)
    • Principal component analysis plot (3D)
  • Differential Expression for RNA-Seq
    • The statistical model
    • Output of the Differential Expression for RNA-Seqtool
    • Statistical comparison tracks
    • The volcano plot
  • Create Heat Map for RNA-Seq
    • Clustering of features and samples
    • The heat map view
  • Create Expression Browser
    • The expression browser
  • Create Venn Diagram for RNA-Seq
    • Venn diagram table view
• Installation of the Advanced RNA-Seq plugin
• Uninstall
• Bibliography

Normalization

Since the sequencing depth might differ between samples, a per-sample library size normalization must be performed before samples can be compared. In contrast to the classic Transcriptomics Analysis tools, this normalization is automatically applied by the tools.

All of the tools in the Advanced RNA-Seq plugin use the TMM (trimmed mean of M values) normalization method [Robinson and Oshlack, 2010] to calculate effective libraries sizes, which are then used as part of the per-sample normalization. TMM normalization is the normalization used in EdgeR [Robinson et al., 2010].

TMM normalization adjusts library sizes based on the assumption that most genes are not differentially expressed. Therefore, it is important not to make subsets of the count data before doing statistical analysis or visualization, as this can lead to differences being normalized away.

For the expression visualization tools (Create Heat Map and PCA for RNA-Seq) additional filtering and normalization are performed:

• 'log CPM' (Counts per Million) values are calculated for each gene. The CPM calculation uses the effective library sizes as calculated by the TMM normalization.
• After this first normalization, a second one is performed across samples for each gene: the counts for each gene are mean centered, and scaled to unit variance.
• Genes or transcripts with zero expression across all samples or invalid values (NaN or +/- Infinity) are removed.

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