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BI
bio-flow-cytometry-differential-analysis
Maintainer FreedomIntelligence · Last updated April 1, 2026
Differential abundance and state analysis for cytometry data. Compare cell populations between conditions using statistical methods. Use when testing for significant changes in cell frequencies or marker expression between groups.
Original source
FreedomIntelligence/OpenClaw-Medical-Skills
https://github.com/FreedomIntelligence/OpenClaw-Medical-Skills/tree/main/skills/bio-flow-cytometry-differential-analysis
- Maintainer
- FreedomIntelligence
- License
- MIT
- Last updated
- April 1, 2026
Skill Snapshot
Key Details From SKILL.md
Key Notes
- R: CATALYST::testDA_edgeR() or diffcyt::testDA_GLMM().
- Compare cell populations between my conditions" → Test for significant changes in cell type frequencies (differential abundance) or marker expression levels (differential state) between experimental groups. R: CATALYST::testDA_edgeR() or diffcyt::testDA_GLMM().
- sce <- readRDS('sce_clustered.rds').
Source Doc
Excerpt From SKILL.md
Differential Abundance (DA)
Goal: Test which cell population clusters differ in frequency between experimental conditions.
Approach: Create a design matrix and contrast from sample metadata, then run edgeR-based differential abundance testing on cluster counts per sample using testDA_edgeR from the diffcyt framework.
library(CATALYST)
library(diffcyt)
## Create design matrix
design <- createDesignMatrix(ei(sce), cols_design = 'condition')
## Create contrast
contrast <- createContrast(c(0, 1)) # Treatment vs Control
Use cases
- Use when testing for significant changes in cell frequencies or marker expression between groups.
Not for
- Do not rely on this catalog entry alone for installation or maintenance details.
Upstream Related Skills
- clustering-phenotyping - Cluster data first
- gating-analysis - Compare gated populations
- differential-expression/de-results - Similar statistical concepts
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