/bulkrna-deconvolution

name: bulkrna-deconvolution
description: Load when estimating cell-type proportions in bulk RNA-seq samples from a single-cell or
  signature-matrix reference. Skip when the data is already single-cell (no deconvolution needed); spatial
  deconvolution (use spatial-deconv).
trigger: bulk deconvolution, cell type proportion, NNLS, CIBERSORTx, bulk deconv, cell fraction
tags:
- bulkrna
- deconvolution
- NNLS
- cell-type-proportion

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](references/parameters.md) and [methodology](references/methodology.md).

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](references/output_contract.md)

Dependencies

`matplotlib`, `numpy`, `pandas`, `scipy`