5.4.8-9. Differential gene expression and gene set analysis.

5.4.8. Finding differentially expressed genes (DEG)

  • Common analysis when working with bulk RNA-seq
    • 2 conditions, e.g. mutant and wild-type
  • Common packages:
  • But single-cell data is somewhat different, so

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Differential gene expression in single-cell data

Differences

  • Usually do not have a defined set of experimental conditions
    • Rather you derive the clusters
      • Cluster vs background (all)
      • Cluster 1 vs Cluster 2
  • The distribution of data is very different: think of the zeros.
    • → Assumptions of bulk methods might not be valid
    • → New methods are designed for scDE, and they are typically part of pipelines.

5.4.9. Gene set analysis

Problem:

  • Many DEG, but what do they really mean?
  • Is there a common function?
  • GO-term analysis can help summarising the results.

Each gene is associated with GO-terms

  • Calculate enrichment.

\[Terms.in.your.genes \over Terms.in.ALL.genes\]

GO-term annotation & protein interaction networks

  • Open up STRING website for interactive exploration