bio-imaging-mass-cytometry-phenotyping
Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in multiplexed imaging data.
npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-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+, anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scikit-learn 1.4+ Before using code patterns, verify installed versions match. If versions differ: - Python: `pip show <package>` then `help(module.function)` to check signatures If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying. # Cell Phenotyping for IMC **"Assign cell types to my segmented IMC cells"** → Classify cells based on protein marker expression using clustering, manual gating, or supervised classification approaches. - Python: `scanpy.tl.leiden()` for unsupervised clustering, then manual annotation - R: `FlowSOM` for self-organizing map-based phenotyping ## Load Single-Cell Data ```python import anndata as ad import scanpy as sc import pandas as pd import numpy as np # Load from h5ad adata = ad.read_h5ad('imc_segmented.h5ad') # Or create from CSVs intensities = pd.read_csv('cell_intensities.csv') cell_info = pd.read_csv('cell_info.csv') adata = ad.AnnData(X=intensities.values) adata.var_names = intensities.columns adata.ob
- Version Compatibility
- Load Single-Cell Data
- Data Transformation
- Clustering-Based Phenotyping
- Manual Gating
- Cluster Annotation
- SOM-Based Clustering (FlowSOM-Style)
- Automated Annotation
- Visualize Phenotypes
- Cell Type Frequencies
- Save Results
- Related Skills
What does the bio-imaging-mass-cytometry-phenotyping skill do?
Cell type assignment from marker expression in IMC data. Covers manual gating, clustering, and automated classification approaches. Use when assigning cell types to segmented IMC cells based on protein marker expression or when phenotyping cells in multiplexed imaging data.
How do I install it?
Run `npx skills add FreedomIntelligence/OpenClaw-Medical-Skills --skill bio-imaging-mass-cytometry-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.