bio-flow-cytometry-clustering-phenotyping
Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-clustering-phenotyping --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: FlowSOM 2.10+, scanpy 1.10+ 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. # Clustering and Phenotyping **"Cluster my cytometry data to find cell types"** → Discover cell populations in high-dimensional flow/mass cytometry data using unsupervised clustering without predefined gates. - R: `FlowSOM::FlowSOM()` for self-organizing map clustering - R: `CATALYST::cluster()` with Phenograph or FlowSOM ## FlowSOM Clustering **Goal:** Cluster cytometry events into cell populations using self-organizing maps. **Approach:** Build a FlowSOM grid on marker channels, then extract metacluster assignments per cell. ```r library(FlowSOM) # Prepare data expr <- exprs(fcs) marker_cols <- grep('CD|HLA', colnames(fcs), value = TRUE) # Build SOM fsom <- FlowSOM(fcs, colsToUse = marker_cols, xdim = 10, ydim = 10, nClus = 20, seed = 42) # Get cluster assignments clusters <- GetMetaclust
- Version Compatibility
- FlowSOM Clustering
- CATALYST Workflow (Full Pipeline)
- Phenograph Clustering
- Dimensionality Reduction
- Cluster Annotation
- Cluster Merging
- Abundance Analysis (per sample)
- Marker Expression Summary
- Export Results
- Choosing Number of Clusters
- Batch Integration
- Related Skills
What does the bio-flow-cytometry-clustering-phenotyping skill do?
Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-flow-cytometry-clustering-phenotyping --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.