tiangzlab/omicsclaw/bulkrna-deconvolution

bulkrna-deconvolution

When to use

Estimate bulk cell-type proportions with an explicit signature matrix. See the description for adjacent skills.

Use from a step

from skills._sdk.notebook import load_skill, read_input, write_output
library = load_skill("bulkrna-deconvolution")
# Supply DataFrames read with read_input(..., reader=...) for your CSV layout.
result = library.deconvolve(counts, signature=signature)
write_output(result, "tables/result.csv")

The synthetic worked step is in examples/example_step.py.

API

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deconvolve(counts, *, signature)

Estimate sample proportions without changing either input.

:param counts: Nonnegative gene-by-sample expression DataFrame. :param signature: Required gene-by-cell-type reference, matching the CLI --reference. :returns: Sample-by-cell-type DataFrame; diagnostics contain residuals and shared genes. :raises ValueError: Matrices have invalid values, duplicate labels or no shared genes.

run_info(result, *, keep=True)

Read reference overlap and reconstruction diagnostics.

:param result: DataFrame returned by deconvolve. :param keep: Default True; False removes diagnostics from attrs. :returns: Diagnostic dictionary, empty after removal.

proportions_figure(result)

Plot estimated cell-type proportions per sample.

:param result: Sample-by-cell-type DataFrame from deconvolve. :returns: A matplotlib Figure, without writing files. :raises ValueError: The table cannot be plotted as numeric proportions.

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Methods and parameters

See parameters and methodology.

Gotchas

• deconvolve requires shared gene identifiers and nonnegative matrices.
• run_info()['residuals'] measures fit to the supplied reference, not confidence intervals.
• NNLS is the only backend; deconvolve does not call CIBERSORTx or MuSiC.

Inputs and outputs

The library returns objects without file writes. CLI inventory:

Inputs

• File types: .csv

Outputs

• tables/dominant_types.csv
• tables/proportions.csv
• figures/mean_proportions_pie.png
• figures/proportions_heatmap.png
• figures/proportions_stacked.png
• report.md
• result.json

CLI

python skills/bulkrna/bulkrna-deconvolution/bulkrna_deconvolution.py --demo --output /tmp/bulkrna-deconvolution

See also

• Output contract

Dependencies

matplotlib, numpy, pandas, scipy