tiangzlab/omicsclaw/bulkrna-qc

bulkrna-qc

When to use

Assess raw integer count matrices before bulk differential expression. 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-qc")
# Supply DataFrames read with read_input(..., reader=...) for your CSV layout.
result = library.assess(counts)
write_output(result, "tables/result.csv")

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

API

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assess(counts)

Measure library size and detection on raw counts without changing input.

:param counts: Gene-by-sample nonnegative integer DataFrame with unique labels. :returns: Sample-indexed DataFrame with QC metrics and diagnostic attrs. :raises ValueError: The matrix is invalid or a sample has no counts.

run_info(result, *, keep=True)

Read QC diagnostics and auxiliary matrices.

:param result: DataFrame returned by assess. :param keep: Default True; False removes diagnostics from result.attrs. :returns: A diagnostic dictionary, empty when no record remains.

normalized_counts(result)

Return CPM for visualization, not differential-expression input.

:param result: DataFrame returned by assess with its diagnostics retained. :returns: New gene-by-sample CPM DataFrame. :raises KeyError: QC diagnostics have been removed.

library_figure(result)

Plot total counts per sample without saving files.

:param result: QC table returned by assess. :returns: A matplotlib Figure; the caller saves and closes it. :raises KeyError: total_counts is missing.

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Methods and parameters

See parameters and methodology.

Gotchas

• assess rejects empty libraries instead of producing undefined CPM.
• normalized_counts returns CPM for figures, not DE input.
• run_info()['outlier_samples'] is correlation-based; check biological groups before removing samples.

Inputs and outputs

The library returns objects without file writes. CLI inventory:

Inputs

• File types: .csv

Outputs

• tables/cpm_normalized.csv
• tables/sample_stats.csv
• figures/expression_density.png
• figures/gene_detection.png
• figures/library_sizes.png
• figures/sample_correlation.png
• report.md
• result.json

CLI

python skills/bulkrna/bulkrna-qc/bulkrna_qc.py --demo --output /tmp/bulkrna-qc

See also

• Output contract

Dependencies

matplotlib, numpy, pandas, scipy