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Single-cell RNA-seq analysis with Scanpy. QC, normalization, clustering, visualization, marker gene identification.
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Basé sur la classification professionnelle SOC
Differential gene expression analysis with PyDESeq2. Design matrices, contrasts, multiple testing correction, volcano plots.
End-to-end single-cell RNA-seq analysis including data loading, QC, integration, clustering, cell type annotation, and differential expression.
Critically review AI-agent-conducted scientific analyses for correctness, rigor, and completeness. Use this skill whenever an analysis session has completed and needs validation, when a user asks to "review," "validate," "check," or "audit" a computational analysis, or when an agent pipeline produces scientific results that require quality control before reporting. Also trigger when the user references an execution trace, notebook, or conversation history from a prior analysis session. This skill should run as the final step of any autonomous scientific analysis pipeline.
| name | scanpy |
| description | Single-cell RNA-seq analysis with Scanpy. QC, normalization, clustering, visualization, marker gene identification. |
| metadata | {"skill-author":"Albert Ying"} |
import scanpy as sc
import anndata as ad
# Read 10X data
adata = sc.read_10x_mtx('path/to/data/', var_names='gene_symbols')
# QC
sc.pp.filter_cells(adata, min_genes=200)
sc.pp.filter_genes(adata, min_cells=3)
adata.var['mt'] = adata.var_names.str.startswith('MT-')
sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
adata = adata[adata.obs.pct_counts_mt < 20].copy()
# Normalize and log-transform
sc.pp.normalize_total(adata, target_sum=1e4)
sc.pp.log1p(adata)
# HVG selection
sc.pp.highly_variable_genes(adata, n_top_genes=2000, batch_key='batch')
# Scale, PCA, neighbors, clustering
sc.pp.scale(adata, max_value=10)
sc.tl.pca(adata, n_comps=50)
sc.pp.neighbors(adata, n_pcs=30)
sc.tl.leiden(adata, resolution=0.5)
sc.tl.umap(adata)
# Marker genes
sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')