bio-flow-cytometry-differential-analysis
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.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-differential-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Version Compatibility Reference examples tested with: R stats (base), edgeR 4.0+, ggplot2 3.5+, limma 3.58+ Before using code patterns, verify installed versions match. If versions differ: - R: `packageVersion('<pkg>')` then `?function_name` to verify parameters If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Differential Analysis **"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()` ## 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. ```r library(CATALYST) library(diffcyt) # Load clustered data sce <- readRDS('sce_clustered.rds') # Create design matrix design <- createDesignMatrix
- Version Compatibility
- Differential Abundance (DA)
- Differential State (DS)
- Visualization
- Manual Statistical Testing
- Mixed Effects Models
- CITRUS (Automated Discovery)
- Volcano Plot
- Export Results
- Multiple Comparisons
- Related Skills
What does the bio-flow-cytometry-differential-analysis skill do?
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.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-differential-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From FreedomIntelligence/OpenClaw-Medical-Skills, a repository with 2,909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.