Meeting 12/16/2019 - Notes

[gwaybio]

• Genes have annotations to function relating to specific cell health phenotypes (Investigate specific CRISPR genes #53 (comment))
• There are RNAseq data for this perturbations too. Can we compare predictive performance? Maybe certain cell health models are better with either datatype? What about combined?
  • We may also already have L1000 data for these perturbations (Check L1000 for CRISPR profiles #89)
• ES2 Cell Line has superior performance in CRISPR experiments. It has high CAS9 expression and behaves nicely (Is there anything remarkable about ES2 cell line compared to A549 and HCT116? #80 (comment))
• There may be room for various improvements in model building
  • Adjust for single cell penetrance (only build profiles with cells with infections)
  • There may be a copy number effect here for CRISPR knockouts. Could adjust scores by some sort of copy number effect.
• Perform leave one cell out experiments - this will tell us how well models might generalize to new cell lines.
• Perform CCA on X and Y matrices and look at loadings to determine clearly, which cell painting features are aligned with cell health phenotypes
  • This is more informative than looking at specific feature names (Analyzing Model Coefficients #81)