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WayScience / phenotypic_profiling

Machine learning for predicting 15 single-cell phenotypes from cell morphology profiles
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Process jump single cell data #56

Closed gwaybio closed 8 months ago

gwaybio commented 9 months ago

The notebook and associated files load in the JUMP single-cell results (KS tests) from https://github.com/WayScience/JUMP-single-cell/tree/main/3.analyze_data and performs three operations:

  1. Briefly explores the data
  2. Outputs the top 10 results per phenotype, per treatment type, per model type for a focused exploration and results reporting
  3. Outputs a wide format phenotype profile per model type

These results are important for the manuscript and for adding to a visualization I started working on in #55

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gwaybio commented 9 months ago

@MattsonCam - I think you're the best to review this - you performed the KS test analysis and are familiar with the JUMP dataset. Please review when you are able. Thanks!

gwaybio commented 8 months ago

FYI - I integrated the extended JUMP metadata in the two most recent commits. We probably should mention this in the JUMP-single-cell repo somewhere, but given that the time points and cell lines were all independent plates (and we performed our KS-test analysis per plate) we do not need to rerun any analysis in the JUMP-single-cell repo. Thanks!

gwaybio commented 8 months ago

I added a UMAP fit of phenotypic profile probabilities in the last set of commits. Thanks!

gwaybio commented 8 months ago

Thanks for the thorough review @MattsonCam ! I've addressed all of your comments. I've also added a new output file that summarizes the replicate information (using mean) and pivots the pandas dataframe to compare results across cell types and time points (a minor update). Merging now!