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View Code? Open in Web Editor NEWMeta-analysis of drug target evidence in single-cell data
License: BSD 3-Clause "New" or "Revised" License
Meta-analysis of drug target evidence in single-cell data
License: BSD 3-Clause "New" or "Revised" License
Using total expression levels, run dendrogram with scanpy. Ideally make a separate function for this, to use in diagnostic plots.
A bunch of diseases gets excluded in DE in disease analysis because no disease pseudobulk is left after filtering for low quality annotation, although in prev version of the pipeline there were cell types to do comparison:
To check
MONDO_0005575
MONDO_0006156
MONDO_0006249
MONDO_0024660
MONDO_0024661
MONDO_0024885
MONDO_0001056 # gastric cancer
cellontology_utils
- functions to handle cell ontologiespreprocessing_utils
- things like anndata2pseudobulk
, adding similarity btw cell types to the pseudobulk objects, functions to save and read pseudobulk objects?cxg_utils
- functions to download data and metadata using cxg censusplotting_utils
- functions to make diagnostic plotsde_utils
- functions for DE analysis (cleaning data, running DE analysis with different regimes, saving outputs)sc_evidence
- transform DE analysis results to evidence for drug targetsopentargets_utils
- functions to clean OT datasetsThe idea is to compare expression of the target in the relevant tissue with expression across usual suspects for side effects (blood, liver, heart) or surrounding organs.
The problem is defining what a surrounding organ is.
Compare targets with single-cell evidence for coarse vs fine uniformed cell annotations using lung samples/diseases, comparing ontology based coarse annotations with annotations from Extended Lung cell atlas.
Brain files fail because of insufficient RAM even when requesting 700GB. Download of stomach files from cxg hangs forever, looks like something is wrong in the census side.
Try downloading datasets directly from cellxgene website or sfaira, then filtering and pseudobulking.
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