bio-single-cell-differential-abundance
bio-single-cell-differential-abundance is a Bioinformatics & life sciences skill for AI agents, published by GPTomics in the bioSkills repository on GitHub. Skillspace rates bio-single-cell-differential-abundance B (81/100) and it passed the safety scan; the rating is based on static analysis only.
Test whether cell-type proportions or composition changed between conditions in single-cell data using Milo (miloR), scCODA, sccomp, and propeller. Use when comparing cell-type proportions / composition between conditions, asking which populations expanded or contracted with treatment or disease, running neighborhood-level (cluster-free) abundance testing, or guarding against compositional shifts that masquerade as differential expression.
- Grade
- B
- Quality
- 81 / 100
- Safety
- Passed
- GitHub stars
- 1,025
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 3 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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