GO Enrichment
Classical enrichment (using DEGs) or rank based enrichment (using all genes, ranked by fold change/pvalue) will tell us which functional terms are overrepresented among our differentially expressed genes. This gives us an insight into the functions and pathways that may potentially have been dysregulated by the condition of our study.
Clustering using WGCNA
WGCNA is a useful tool to identify modules of genes that are coexpressed.
- Use all genes (perhaps after filtering out low count, low variance genes) for this analysis- do not restrict to just DEGs.
- Requires at least 15 samples for this analysis.
- Benefits from having sample metadata factors that the gene modules can be correlated to.
Tag-Seq
Tag-Seq is targeted sequencing of just the 3' ends of mRNA.
- This is done at GSAF using a template switching protocol.
- It offers a reduction in library prep and sequencing cost.
- Consider it when looking to do differential expression analysis of known genes with more replicates, conditions, timepoints etc.
- Analysis involves some extra preprocessing. Other steps are similar to standard RNA-Seq analysis.
