--- name: scanpy-singlecell-d669e29d60cc description: "Single-cell RNA-seq analysis with scanpy and anndata. Use when: (1) scRNA-seq preprocessing and QC, (2) clustering and cell type annotation, (3) differential expression analysis, (4) trajectory and pseudotime analysis, (5) UMAP/tSNE visualization. NOT for: bulk RNA-seq differential expression (use DESeq2/edgeR), protein structure analysis (use pdb-structure), or imaging data." metadata: { "openclaw": { "emoji": "🧬", "requires": { "bins": ["python3"] }, "install": [{ "id": "uv-scanpy", "kind": "uv", "package": "scanpy anndata" }] } } --- # Scanpy Single-Cell Analysis Single-cell RNA-seq analysis using scanpy and anndata for preprocessing, clustering, differential expression, and visualization. ## When to Use - scRNA-seq data preprocessing and quality control - Cell clustering and cell type annotation - Differential expression between cell groups - Trajectory inference and pseudotime analysis - Dimensionality reduction visualization (UMAP, tSNE) - Integration of multiple scRNA-seq datasets ## When NOT to Use - Bulk RNA-seq differential expression (use DESeq2 or edgeR) - Protein structure prediction or analysis (use pdb-structure) - Imaging or spatial transcriptomics without companion tools - General-purpose statistics (use scipy-analysis) ## Reading Data and Preprocessing ```python import scanpy as sc # Read data: h5ad, 10x mtx, or 10x h5 adata = sc.read_h5ad('dataset.h5ad') adata = sc.read_10x_mtx('filtered_feature_bc_matrix/', var_names='gene_symbols') # QC filtering 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'], percent_top=None, inplace=True) adata = adata[adata.obs.pct_counts_mt < 20, :] # Normalize, log-transform, select HVGs sc.pp.normalize_total(adata, target_sum=1e4) sc.pp.log1p(adata) adata.raw = adata # preserve full gene set for DE sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5) adata = adata[:, adata.var.highly_variable] sc.pp.scale(adata, max_value=10) ``` ## Dimensionality Reduction and Clustering ```python # PCA sc.tl.pca(adata, svd_solver='arpack', n_comps=50) sc.pl.pca_variance_ratio(adata, n_pcs=50, log=True) # Neighborhood graph and clustering sc.pp.neighbors(adata, n_neighbors=15, n_pcs=40) sc.tl.umap(adata) sc.tl.leiden(adata, resolution=0.5) # Alternative: Louvain clustering sc.tl.louvain(adata, resolution=0.8) # tSNE (alternative to UMAP) sc.tl.tsne(adata, n_pcs=40) ``` ## Differential Expression ```python # Rank genes per cluster (Wilcoxon is recommended) sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon', use_raw=True) sc.pl.rank_genes_groups(adata, n_genes=20, sharey=False) # Extract DE results as DataFrame result = adata.uns['rank_genes_groups'] de_df = sc.get.rank_genes_groups_df(adata, group='0') de_df_filtered = de_df[(de_df['pvals_adj'] < 0.05) & (de_df['logfoldchanges'].abs() > 1)] # Compare specific groups sc.tl.rank_genes_groups(adata, groupby='leiden', groups=['0'], reference='1', method='wilcoxon', use_raw=True) ``` ## Visualization ```python # UMAP colored by cluster sc.pl.umap(adata, color=['leiden'], frameon=False, save='_clusters.pdf') # UMAP colored by gene expression sc.pl.umap(adata, color=['CST3', 'NKG7', 'MS4A1'], frameon=False) # Dot plot for marker genes across clusters marker_genes = ['CD3D', 'CD79A', 'CST3', 'NKG7', 'PPBP'] sc.pl.dotplot(adata, marker_genes, groupby='leiden', save='_markers.pdf') # Stacked violin plot sc.pl.stacked_violin(adata, marker_genes, groupby='leiden', rotation=90) # Heatmap of top DE genes sc.pl.rank_genes_groups_heatmap(adata, n_genes=5, groupby='leiden', show_gene_labels=True) # Matrix plot sc.pl.matrixplot(adata, marker_genes, groupby='leiden', standard_scale='var') ``` ## Trajectory Analysis ```python # Diffusion map and pseudotime sc.tl.diffmap(adata) sc.tl.dpt(adata, n_dcs=10) # PAGA (partition-based graph abstraction) sc.tl.paga(adata, groups='leiden') sc.pl.paga(adata, plot=True, threshold=0.03) sc.tl.umap(adata, init_pos='paga') ``` ## Best Practices 1. Always start with QC: filter low-quality cells and doublets before analysis. 2. Use `adata.raw` to preserve full gene set for DE testing after subsetting HVGs. 3. Prefer Leiden over Louvain clustering (better modularity optimization). 4. Use Wilcoxon rank-sum for DE; it is robust and non-parametric. 5. Save intermediate results with `adata.write('checkpoint.h5ad')`. 6. Set `sc.settings.figdir` and use `save=` parameter for reproducible figures. 7. Use `sc.logging.print_versions()` to record environment for reproducibility. 8. Adjust `resolution` parameter in Leiden to control cluster granularity. --- Source attribution: beita6969 Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/scanpy-singlecell/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2025 Peter Steinberger Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: beita6969 Original project: https://github.com/beita6969/ScienceClaw/blob/e925f732c7ca435f557e88075bc47a58f1e984f1/skills/scanpy-singlecell/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research, Healthcare & Life Sciences Roles: Clinical Research, Data Scientist, Researcher Specialties: API Tools: Anndata, Scanpy Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: d2451f0217712554ab31e20881aef5bb81062f9eaac4b0329d8342ea214e0fa4