Skip to content

Latest commit

 

History

History
9 lines (8 loc) · 464 Bytes

Notes.md

File metadata and controls

9 lines (8 loc) · 464 Bytes
  • Use different aggregation statistics per pathway
    • E.g. a gene-level: Proportion of genes altered (in case one gene w/ multiple hits dominates)
    • E.g. use a hypergeometric P-value to illustrate significance.
    • E.g. use proportion of disruptions out of total possible

Feedback

  • Add gene expression aggregation - how many outlier genes in pathway?
  • Add in your own geneset
  • Use predicted functional impact of a mutation instead of just presence/absence?