Sc Differential Abundance

What it does

When your agent uses it

• Tasks that involve Bioinformatics
• Tasks that involve DataFrames

Example prompts

• “/sc-differential-abundance”

Requirements

• Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1Load AnnData; validate --method, --fdr, --n-neighbors, --prop, --n-permutations.
2. 2Run preflight on --condition-key, --sample-key, --cell-type-key — fail fast on missing columns or under-replication.
3. 3Build the universal composition summary (counts / proportions / condition means) and save them.
4. 4Dispatch to the method-specific runner (run_milo_da / run_sccoda_da / simple proportion test / R proportion test).
5. 5Append method-specific summary fields to result.json (n_nhoods / n_effect_rows / n_cell_types / n_significant, plus backend for…
6. 6Save figures, report.md, result.json.

What it can do on your machine

Read from SKILL.md and the folder at commit 90a3bec. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Python), which the agent can run.

  Shell commands in SKILL.md call:

  • python

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Sc Differential Abundance loads about 2.1k tokens when it runs, and up to ~3.3k if it reads all its reference files. Until then it costs about 79 tokens; SKILL.md has 675 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~79

When it runs · the whole SKILL.md, loaded when a task matches

~2.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~3.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from TianGzlab/OmicsClaw at commit 90a3bec, republished under its Apache-2.0 licence (© TianGzlab). 675 words, ~2,067 tokens.

name

sc-differential-abundance

description

Load when testing whether cell-type / cluster proportions or neighbourhood densities differ between conditions in a multi-sample scRNA AnnData via Milo, scCODA, simple proportion screen, or R Monte-Carlo permutation. Skip when ranking marker genes (use sc-markers); per-cell DE (use sc-de).

tags

singlecell, scrna, differential-abundance, compositional, milo, sccoda, proportion-test

# Recover a threefold change in a synthetic cell type using independent samples.
# Reads multisample_synthetic: four control and four treated samples.
# Calls sc-differential-abundance: test_abundance, composition, proportion_figure.

from skills._sdk.notebook import load_demo, load_skill, write_output

abundance = load_skill('sc-differential-abundance')
adata = load_demo('multisample_synthetic')

table = abundance.test_abundance(adata, method='simple')
counts, proportions = abundance.composition(adata, sample_key='sample',
    condition_key='condition', celltype_key='cell_type')
write_output(table, 'tables/abundance.csv')
write_output(counts, 'tables/sample_counts.csv')
write_output(abundance.proportion_figure(proportions), 'figures/sample_proportions.png')

enriched = table.set_index('cell_type').loc['Enriched']
assert enriched['significant']
assert enriched['log2fc_group_b_over_a'] > 1
assert counts.shape == (8, 3)

# Demo (built-in synthetic 2-condition × 4-sample data)
python skills/singlecell/scrna/sc-differential-abundance/sc_differential_abundance.py --demo \
  --method milo --output /tmp/sc_da_demo

# Milo (replicate-aware neighbourhood DA)
python skills/singlecell/scrna/sc-differential-abundance/sc_differential_abundance.py \
  --input integrated.h5ad --output results/ \
  --method milo --condition-key treatment --sample-key donor

# scCODA Bayesian compositional analysis
python skills/singlecell/scrna/sc-differential-abundance/sc_differential_abundance.py \
  --input integrated.h5ad --output results/ \
  --method sccoda --reference-cell-type "B cell" \
  --condition-key treatment --sample-key donor --cell-type-key cell_type

# Lightweight proportion screen (no pertpy)
python skills/singlecell/scrna/sc-differential-abundance/sc_differential_abundance.py \
  --input integrated.h5ad --output results/ --method simple

<!-- api:begin generated from _api.py; regenerate with run.py api <skill dir> --write -->

<!-- api:end -->

© TianGzlab, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files (references) in skills/singlecell/scrna/sc-differential-abundance of TianGzlab/OmicsClaw.

• SKILL.md
• _api.py
• examples/example_step.py
• references/methodology.md
• references/output_contract.md
• references/parameters.md
• references/r_visualization.md
• sc_differential_abundance.py
• tests/test_abundance_api.py
• tests/test_sc_differential_abundance_methods.py

Open the folder on GitHubat commit 90a3bec