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 majiayu000/claude-skill-registry --skill 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.
# Cell Phenotyping for IMC ## 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.obs = cell_info ``` ## Data Transformation ```python # Arcsinh transformation (standard for cytometry) def arcsinh_transform(adata, cofactor=5): adata.X = np.arcsinh(adata.X / cofactor) return adata adata = arcsinh_transform(adata) # Z-score normalization sc.pp.scale(adata, max_value=10) ``` ## Clustering-Based Phenotyping ```python # PCA and neighbors sc.pp.pca(adata, n_comps=15) sc.pp.neighbors(adata, n_neighbors=15, n_pcs=15) # Clustering sc.tl.leiden(adata, resolution=0.5) # UMAP for visualization sc.tl.umap(adata) # Plot sc.pl.umap(adata, color='leiden', save='_clusters.png') ``` ## Manual Gating ```python def gate_cells(adata, marker, threshold, above=True): '''Gate cells based on marker expression''' values = adata[:, marker].X.flatten() if above: return values > threshold else: ret
- 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 majiayu000/claude-skill-registry --skill 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 majiayu000/claude-skill-registry, a repository with 534 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.