bio-single-cell-markers-annotation
Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.
npx skills add majiayu000/claude-skill-registry --skill markers-annotation-gptomics-bioskills-2 --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.
# Marker Genes and Cell Type Annotation Find differentially expressed genes between clusters and annotate cell types. ## Scanpy (Python) ### Required Imports ```python import scanpy as sc import pandas as pd ``` ### Find Markers for All Clusters ```python # Find marker genes for each cluster vs all others sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon') # View top markers sc.pl.rank_genes_groups(adata, n_genes=10, sharey=False) # Get results as DataFrame markers = sc.get.rank_genes_groups_df(adata, group=None) print(markers.head(20)) ``` ### Marker Detection Methods ```python # Wilcoxon rank-sum test (default, recommended) sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon') # t-test sc.tl.rank_genes_groups(adata, groupby='leiden', method='t-test') # Logistic regression sc.tl.rank_genes_groups(adata, groupby='leiden', method='logreg') ``` ### Filter Markers ```python # Get markers with filters markers = sc.get.rank_genes_groups_df(adata, group='0') significant = markers[(markers['pvals_adj'] < 0.05) & (markers['logfoldchanges'] > 1)] print(f'Cluster 0 significant markers: {len(significant)}') # Filter all groups sc.tl.filter_rank_genes_groups(adata
- Scanpy (Python)
- Required Imports
- Find Markers for All Clusters
- Marker Detection Methods
- Filter Markers
- Compare Specific Clusters
- Visualize Marker Expression
- Gene Set Scoring
- Cell Cycle Scoring
- Manual Cell Type Annotation
- Export Markers
- Seurat (R)
- Required Libraries
- Find All Markers
What does the bio-single-cell-markers-annotation skill do?
Find marker genes and annotate cell types in single-cell RNA-seq using Seurat (R) and Scanpy (Python). Use for differential expression between clusters, identifying cluster-specific markers, scoring gene sets, and assigning cell type labels. Use when finding marker genes and annotating clusters.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill markers-annotation-gptomics-bioskills-2 --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.