Re-constructing Schmidt et al. (2022)

This use-case re-constructs a result of the research project of Schmidt el al. (2022) via several scripts & notebooks.

A genome-wide phenotypic CRISPRa screen was used to find transcriptional cell states that correlate with causal drivers of proteomic IFNG expression.

To understand how this result was obtained, one can look at the lineage:

You can explore it here together with code and data artifacts.

You can also see all code and how it can be executed in build.yml here:

schmidt22/.github/workflows/build.yml

Lines 19 to 29 in 1f63452

	# setup the project: define types, features, and labels
	- run: python scripts/define_types_features_labels.py
	# process the 10x Chromium upload data
	- run: python scripts/curate_chromium_10x_upload.py --s3-folder s3://lamindata/fastq --experiment Schmidt22-EXP002 --biosample Schmidt22-S001 --project Schmidt22
	- run: python scripts/run_cellranger_count.py --experiment Schmidt22-EXP002 --biosample Schmidt22-S001 --project Schmidt22 --dry-run
	- run: python scripts/run_scrna_normalization_clustering.py --experiment Schmidt22-EXP002 --biosample Schmidt22-S001 --project Schmidt22 --dry-run
	# process the GWS CRISPR IFNg readout data in two notebooks
	- run: jupyter nbconvert --to notebook --inplace --execute notebooks/curate_gws_crispr_ifng_readout.ipynb
	- run: jupyter nbconvert --to notebook --inplace --execute notebooks/analyze_gws_crispr_ifng_readout.ipynb
	# analyze the joint data in a notebook
	- run: jupyter nbconvert --to notebook --inplace --execute notebooks/analyze_scrna_joined_on_ifng_readout.ipynb