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Load .h5ad\u002F10X data, QC, normalization, PCA\u002FUMAP\u002Ft-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002FSKILL.md",[],[5,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36],"PCA","single","for","load","seq","h5ad","leiden","trajectory","annotation","qc","data","type","umap","cell","RNA","10x","normalization","analysis","clustering","2026-11-10 10:12:30",false,"---\nname: scanpy\ndescription: \"Single-cell RNA-seq analysis. Load .h5ad\u002F10X data, QC, normalization, PCA\u002FUMAP\u002Ft-SNE, Leiden clustering, marker genes, cell type annotation, trajectory, for scRNA-seq analysis.\"\n---\n\n# Scanpy: Single-Cell Analysis\n\n## Overview\n\nScanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)\n- Performing quality control on scRNA-seq datasets\n- Creating UMAP, t-SNE, or PCA visualizations\n- Identifying cell clusters and finding marker genes\n- Annotating cell types based on gene expression\n- Conducting trajectory inference or pseudotime analysis\n- Generating publication-quality single-cell plots\n\n## Quick Start\n\n### Basic Import and Setup\n\n```python\nimport scanpy as sc\nimport pandas as pd\nimport numpy as np\n\n# Configure settings\nsc.settings.verbosity = 3\nsc.settings.set_figure_params(dpi=80, facecolor='white')\nsc.settings.figdir = '.\u002Ffigures\u002F'\n```\n\n### Loading Data\n\n```python\n# From 10X Genomics\nadata = sc.read_10x_mtx('path\u002Fto\u002Fdata\u002F')\nadata = sc.read_10x_h5('path\u002Fto\u002Fdata.h5')\n\n# From h5ad (AnnData format)\nadata = sc.read_h5ad('path\u002Fto\u002Fdata.h5ad')\n\n# From CSV\nadata = sc.read_csv('path\u002Fto\u002Fdata.csv')\n```\n\n### Understanding AnnData Structure\n\nThe AnnData object is the core data structure in scanpy:\n\n```python\nadata.X          # Expression matrix (cells × genes)\nadata.obs        # Cell metadata (DataFrame)\nadata.var        # Gene metadata (DataFrame)\nadata.uns        # Unstructured annotations (dict)\nadata.obsm       # Multi-dimensional cell data (PCA, UMAP)\nadata.raw        # Raw data backup\n\n# Access cell and gene names\nadata.obs_names  # Cell barcodes\nadata.var_names  # Gene names\n```\n\n## Standard Analysis Workflow\n\n### 1. Quality Control\n\nIdentify and filter low-quality cells and genes:\n\n```python\n# Identify mitochondrial genes\nadata.var['mt'] = adata.var_names.str.startswith('MT-')\n\n# Calculate QC metrics\nsc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)\n\n# Visualize QC metrics\nsc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],\n             jitter=0.4, multi_panel=True)\n\n# Filter cells and genes\nsc.pp.filter_cells(adata, min_genes=200)\nsc.pp.filter_genes(adata, min_cells=3)\nadata = adata[adata.obs.pct_counts_mt \u003C 5, :]  # Remove high MT% cells\n```\n\n**Use the QC script for automated analysis:**\n```bash\npython scripts\u002Fqc_analysis.py input_file.h5ad --output filtered.h5ad\n```\n\n### 2. Normalization and Preprocessing\n\n```python\n# Normalize to 10,000 counts per cell\nsc.pp.normalize_total(adata, target_sum=1e4)\n\n# Log-transform\nsc.pp.log1p(adata)\n\n# Save raw counts for later\nadata.raw = adata\n\n# Identify highly variable genes\nsc.pp.highly_variable_genes(adata, n_top_genes=2000)\nsc.pl.highly_variable_genes(adata)\n\n# Subset to highly variable genes\nadata = adata[:, adata.var.highly_variable]\n\n# Regress out unwanted variation\nsc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])\n\n# Scale data\nsc.pp.scale(adata, max_value=10)\n```\n\n### 3. Dimensionality Reduction\n\n```python\n# PCA\nsc.tl.pca(adata, svd_solver='arpack')\nsc.pl.pca_variance_ratio(adata, log=True)  # Check elbow plot\n\n# Compute neighborhood graph\nsc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)\n\n# UMAP for visualization\nsc.tl.umap(adata)\nsc.pl.umap(adata, color='leiden')\n\n# Alternative: t-SNE\nsc.tl.tsne(adata)\n```\n\n### 4. Clustering\n\n```python\n# Leiden clustering (recommended)\nsc.tl.leiden(adata, resolution=0.5)\nsc.pl.umap(adata, color='leiden', legend_loc='on data')\n\n# Try multiple resolutions to find optimal granularity\nfor res in [0.3, 0.5, 0.8, 1.0]:\n    sc.tl.leiden(adata, resolution=res, key_added=f'leiden_{res}')\n```\n\n### 5. Marker Gene Identification\n\n```python\n# Find marker genes for each cluster\nsc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')\n\n# Visualize results\nsc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)\nsc.pl.rank_genes_groups_heatmap(adata, n_genes=10)\nsc.pl.rank_genes_groups_dotplot(adata, n_genes=5)\n\n# Get results as DataFrame\nmarkers = sc.get.rank_genes_groups_df(adata, group='0')\n```\n\n### 6. Cell Type Annotation\n\n```python\n# Define marker genes for known cell types\nmarker_genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7', 'FCGR3A']\n\n# Visualize markers\nsc.pl.umap(adata, color=marker_genes, use_raw=True)\nsc.pl.dotplot(adata, var_names=marker_genes, groupby='leiden')\n\n# Manual annotation\ncluster_to_celltype = {\n    '0': 'CD4 T cells',\n    '1': 'CD14+ Monocytes',\n    '2': 'B cells',\n    '3': 'CD8 T cells',\n}\nadata.obs['cell_type'] = adata.obs['leiden'].map(cluster_to_celltype)\n\n# Visualize annotated types\nsc.pl.umap(adata, color='cell_type', legend_loc='on data')\n```\n\n### 7. Save Results\n\n```python\n# Save processed data\nadata.write('results\u002Fprocessed_data.h5ad')\n\n# Export metadata\nadata.obs.to_csv('results\u002Fcell_metadata.csv')\nadata.var.to_csv('results\u002Fgene_metadata.csv')\n```\n\n## Common Tasks\n\n### Creating Publication-Quality Plots\n\n```python\n# Set high-quality defaults\nsc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5))\nsc.settings.file_format_figs = 'pdf'\n\n# UMAP with custom styling\nsc.pl.umap(adata, color='cell_type',\n           palette='Set2',\n           legend_loc='on data',\n           legend_fontsize=12,\n           legend_fontoutline=2,\n           frameon=False,\n           save='_publication.pdf')\n\n# Heatmap of marker genes\nsc.pl.heatmap(adata, var_names=genes, groupby='cell_type',\n              swap_axes=True, show_gene_labels=True,\n              save='_markers.pdf')\n\n# Dot plot\nsc.pl.dotplot(adata, var_names=genes, groupby='cell_type',\n              save='_dotplot.pdf')\n```\n\nRefer to `references\u002Fplotting_guide.md` for comprehensive visualization examples.\n\n### Trajectory Inference\n\n```python\n# PAGA (Partition-based graph abstraction)\nsc.tl.paga(adata, groups='leiden')\nsc.pl.paga(adata, color='leiden')\n\n# Diffusion pseudotime\nadata.uns['iroot'] = np.flatnonzero(adata.obs['leiden'] == '0')[0]\nsc.tl.dpt(adata)\nsc.pl.umap(adata, color='dpt_pseudotime')\n```\n\n### Differential Expression Between Conditions\n\n```python\n# Compare treated vs control within cell types\nadata_subset = adata[adata.obs['cell_type'] == 'T cells']\nsc.tl.rank_genes_groups(adata_subset, groupby='condition',\n                         groups=['treated'], reference='control')\nsc.pl.rank_genes_groups(adata_subset, groups=['treated'])\n```\n\n### Gene Set Scoring\n\n```python\n# Score cells for gene set expression\ngene_set = ['CD3D', 'CD3E', 'CD3G']\nsc.tl.score_genes(adata, gene_set, score_name='T_cell_score')\nsc.pl.umap(adata, color='T_cell_score')\n```\n\n### Batch Correction\n\n```python\n# ComBat batch correction\nsc.pp.combat(adata, key='batch')\n\n# Alternative: use Harmony or scVI (separate packages)\n```\n\n## Key Parameters to Adjust\n\n### Quality Control\n- `min_genes`: Minimum genes per cell (typically 200-500)\n- `min_cells`: Minimum cells per gene (typically 3-10)\n- `pct_counts_mt`: Mitochondrial threshold (typically 5-20%)\n\n### Normalization\n- `target_sum`: Target counts per cell (default 1e4)\n\n### Feature Selection\n- `n_top_genes`: Number of HVGs (typically 2000-3000)\n- `min_mean`, `max_mean`, `min_disp`: HVG selection parameters\n\n### Dimensionality Reduction\n- `n_pcs`: Number of principal components (check variance ratio plot)\n- `n_neighbors`: Number of neighbors (typically 10-30)\n\n### Clustering\n- `resolution`: Clustering granularity (0.4-1.2, higher = more clusters)\n\n## Common Pitfalls and Best Practices\n\n1. **Always save raw counts**: `adata.raw = adata` before filtering genes\n2. **Check QC plots carefully**: Adjust thresholds based on dataset quality\n3. **Use Leiden over Louvain**: More efficient and better results\n4. **Try multiple clustering resolutions**: Find optimal granularity\n5. **Validate cell type annotations**: Use multiple marker genes\n6. **Use `use_raw=True` for gene expression plots**: Shows original counts\n7. **Check PCA variance ratio**: Determine optimal number of PCs\n8. **Save intermediate results**: Long workflows can fail partway through\n\n## Bundled Resources\n\n### scripts\u002Fqc_analysis.py\nAutomated quality control script that calculates metrics, generates plots, and filters data:\n\n```bash\npython scripts\u002Fqc_analysis.py input.h5ad --output filtered.h5ad \\\n    --mt-threshold 5 --min-genes 200 --min-cells 3\n```\n\n### references\u002Fstandard_workflow.md\nComplete step-by-step workflow with detailed explanations and code examples for:\n- Data loading and setup\n- Quality control with visualization\n- Normalization and scaling\n- Feature selection\n- Dimensionality reduction (PCA, UMAP, t-SNE)\n- Clustering (Leiden, Louvain)\n- Marker gene identification\n- Cell type annotation\n- Trajectory inference\n- Differential expression\n\nRead this reference when performing a complete analysis from scratch.\n\n### references\u002Fapi_reference.md\nQuick reference guide for scanpy functions organized by module:\n- Reading\u002Fwriting data (`sc.read_*`, `adata.write_*`)\n- Preprocessing (`sc.pp.*`)\n- Tools (`sc.tl.*`)\n- Plotting (`sc.pl.*`)\n- AnnData structure and manipulation\n- Settings and utilities\n\nUse this for quick lookup of function signatures and common parameters.\n\n### references\u002Fplotting_guide.md\nComprehensive visualization guide including:\n- Quality control plots\n- Dimensionality reduction visualizations\n- Clustering visualizations\n- Marker gene plots (heatmaps, dot plots, violin plots)\n- Trajectory and pseudotime plots\n- Publication-quality customization\n- Multi-panel figures\n- Color palettes and styling\n\nConsult this when creating publication-ready figures.\n\n### assets\u002Fanalysis_template.py\nComplete analysis template providing a full workflow from data loading through cell type annotation. Copy and customize this template for new analyses:\n\n```bash\ncp assets\u002Fanalysis_template.py my_analysis.py\n# Edit parameters and run\npython my_analysis.py\n```\n\nThe template includes all standard steps with configurable parameters and helpful comments.\n\n## Additional Resources\n\n- **Official scanpy documentation**: https:\u002F\u002Fscanpy.readthedocs.io\u002F\n- **Scanpy tutorials**: https:\u002F\u002Fscanpy-tutorials.readthedocs.io\u002F\n- **scverse ecosystem**: https:\u002F\u002Fscverse.org\u002F (related tools: squidpy, scvi-tools, cellrank)\n- **Best practices**: Luecken & Theis (2019) \"Current best practices in single-cell RNA-seq\"\n\n## Tips for Effective Analysis\n\n1. **Start with the template**: Use `assets\u002Fanalysis_template.py` as a starting point\n2. **Run QC script first**: Use `scripts\u002Fqc_analysis.py` for initial filtering\n3. **Consult references as needed**: Load workflow and API references into context\n4. **Iterate on clustering**: Try multiple resolutions and visualization methods\n5. **Validate biologically**: Check marker genes match expected cell types\n6. **Document parameters**: Record QC thresholds and analysis settings\n7. **Save checkpoints**: Write intermediate results at key steps\n","# Scanpy: Single-Cell Analysis\n\n## Overview\n\nScanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Analyzing single-cell RNA-seq data (.h5ad, 10X, CSV formats)\n- Performing quality control on scRNA-seq datasets\n- Creating UMAP, t-SNE, or PCA visualizations\n- Identifying cell clusters and finding marker genes\n- Annotating cell types based on gene expression\n- Conducting trajectory inference or pseudotime analysis\n- Generating publication-quality single-cell plots\n\n## Quick Start\n\n### Basic Import and Setup\n\n```python\nimport scanpy as sc\nimport pandas as pd\nimport numpy as np\n\n# Configure settings\nsc.settings.verbosity = 3\nsc.settings.set_figure_params(dpi=80, facecolor='white')\nsc.settings.figdir = '.\u002Ffigures\u002F'\n```\n\n### Loading Data\n\n```python\n# From 10X Genomics\nadata = sc.read_10x_mtx('path\u002Fto\u002Fdata\u002F')\nadata = sc.read_10x_h5('path\u002Fto\u002Fdata.h5')\n\n# From h5ad (AnnData format)\nadata = sc.read_h5ad('path\u002Fto\u002Fdata.h5ad')\n\n# From CSV\nadata = sc.read_csv('path\u002Fto\u002Fdata.csv')\n```\n\n### Understanding AnnData Structure\n\nThe AnnData object is the core data structure in scanpy:\n\n```python\nadata.X          # Expression matrix (cells × genes)\nadata.obs        # Cell metadata (DataFrame)\nadata.var        # Gene metadata (DataFrame)\nadata.uns        # Unstructured annotations (dict)\nadata.obsm       # Multi-dimensional cell data (PCA, UMAP)\nadata.raw        # Raw data backup\n\n# Access cell and gene names\nadata.obs_names  # Cell barcodes\nadata.var_names  # Gene names\n```\n\n## Standard Analysis Workflow\n\n### 1. Quality Control\n\nIdentify and filter low-quality cells and genes:\n\n```python\n# Identify mitochondrial genes\nadata.var['mt'] = adata.var_names.str.startswith('MT-')\n\n# Calculate QC metrics\nsc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)\n\n# Visualize QC metrics\nsc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],\n             jitter=0.4, multi_panel=True)\n\n# Filter cells and genes\nsc.pp.filter_cells(adata, min_genes=200)\nsc.pp.filter_genes(adata, min_cells=3)\nadata = adata[adata.obs.pct_counts_mt \u003C 5, :]  # Remove high MT% cells\n```\n\n**Use the QC script for automated analysis:**\n```bash\npython scripts\u002Fqc_analysis.py input_file.h5ad --output filtered.h5ad\n```\n\n### 2. Normalization and Preprocessing\n\n```python\n# Normalize to 10,000 counts per cell\nsc.pp.normalize_total(adata, target_sum=1e4)\n\n# Log-transform\nsc.pp.log1p(adata)\n\n# Save raw counts for later\nadata.raw = adata\n\n# Identify highly variable genes\nsc.pp.highly_variable_genes(adata, n_top_genes=2000)\nsc.pl.highly_variable_genes(adata)\n\n# Subset to highly variable genes\nadata = adata[:, adata.var.highly_variable]\n\n# Regress out unwanted variation\nsc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])\n\n# Scale data\nsc.pp.scale(adata, max_value=10)\n```\n\n### 3. Dimensionality Reduction\n\n```python\n# PCA\nsc.tl.pca(adata, svd_solver='arpack')\nsc.pl.pca_variance_ratio(adata, log=True)  # Check elbow plot\n\n# Compute neighborhood graph\nsc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)\n\n# UMAP for visualization\nsc.tl.umap(adata)\nsc.pl.umap(adata, color='leiden')\n\n# Alternative: t-SNE\nsc.tl.tsne(adata)\n```\n\n### 4. Clustering\n\n```python\n# Leiden clustering (recommended)\nsc.tl.leiden(adata, resolution=0.5)\nsc.pl.umap(adata, color='leiden', legend_loc='on data')\n\n# Try multiple resolutions to find optimal granularity\nfor res in [0.3, 0.5, 0.8, 1.0]:\n    sc.tl.leiden(adata, resolution=res, key_added=f'leiden_{res}')\n```\n\n### 5. Marker Gene Identification\n\n```python\n# Find marker genes for each cluster\nsc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')\n\n# Visualize results\nsc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)\nsc.pl.rank_genes_groups_heatmap(adata, n_genes=10)\nsc.pl.rank_genes_groups_dotplot(adata, n_genes=5)\n\n# Get results as DataFrame\nmarkers = sc.get.rank_genes_groups_df(adata, group='0')\n```\n\n### 6. Cell Type Annotation\n\n```python\n# Define marker genes for known cell types\nmarker_genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7', 'FCGR3A']\n\n# Visualize markers\nsc.pl.umap(adata, color=marker_genes, use_raw=True)\nsc.pl.dotplot(adata, var_names=marker_genes, groupby='leiden')\n\n# Manual annotation\ncluster_to_celltype = {\n    '0': 'CD4 T cells',\n    '1': 'CD14+ Monocytes',\n    '2': 'B cells',\n    '3': 'CD8 T cells',\n}\nadata.obs['cell_type'] = adata.obs['leiden'].map(cluster_to_celltype)\n\n# Visualize annotated types\nsc.pl.umap(adata, color='cell_type', legend_loc='on data')\n```\n\n### 7. Save Results\n\n```python\n# Save processed data\nadata.write('results\u002Fprocessed_data.h5ad')\n\n# Export metadata\nadata.obs.to_csv('results\u002Fcell_metadata.csv')\nadata.var.to_csv('results\u002Fgene_metadata.csv')\n```\n\n## Common Tasks\n\n### Creating Publication-Quality Plots\n\n```python\n# Set high-quality defaults\nsc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5))\nsc.settings.file_format_figs = 'pdf'\n\n# UMAP with custom styling\nsc.pl.umap(adata, color='cell_type',\n           palette='Set2',\n           legend_loc='on data',\n           legend_fontsize=12,\n           legend_fontoutline=2,\n           frameon=False,\n           save='_publication.pdf')\n\n# Heatmap of marker genes\nsc.pl.heatmap(adata, var_names=genes, groupby='cell_type',\n              swap_axes=True, show_gene_labels=True,\n              save='_markers.pdf')\n\n# Dot plot\nsc.pl.dotplot(adata, var_names=genes, groupby='cell_type',\n              save='_dotplot.pdf')\n```\n\nRefer to `references\u002Fplotting_guide.md` for comprehensive visualization examples.\n\n### Trajectory Inference\n\n```python\n# PAGA (Partition-based graph abstraction)\nsc.tl.paga(adata, groups='leiden')\nsc.pl.paga(adata, color='leiden')\n\n# Diffusion pseudotime\nadata.uns['iroot'] = np.flatnonzero(adata.obs['leiden'] == '0')[0]\nsc.tl.dpt(adata)\nsc.pl.umap(adata, color='dpt_pseudotime')\n```\n\n### Differential Expression Between Conditions\n\n```python\n# Compare treated vs control within cell types\nadata_subset = adata[adata.obs['cell_type'] == 'T cells']\nsc.tl.rank_genes_groups(adata_subset, groupby='condition',\n                         groups=['treated'], reference='control')\nsc.pl.rank_genes_groups(adata_subset, groups=['treated'])\n```\n\n### Gene Set Scoring\n\n```python\n# Score cells for gene set expression\ngene_set = ['CD3D', 'CD3E', 'CD3G']\nsc.tl.score_genes(adata, gene_set, score_name='T_cell_score')\nsc.pl.umap(adata, color='T_cell_score')\n```\n\n### Batch Correction\n\n```python\n# ComBat batch correction\nsc.pp.combat(adata, key='batch')\n\n# Alternative: use Harmony or scVI (separate packages)\n```\n\n## Key Parameters to Adjust\n\n### Quality Control\n- `min_genes`: Minimum genes per cell (typically 200-500)\n- `min_cells`: Minimum cells per gene (typically 3-10)\n- `pct_counts_mt`: Mitochondrial threshold (typically 5-20%)\n\n### Normalization\n- `target_sum`: Target counts per cell (default 1e4)\n\n### Feature Selection\n- `n_top_genes`: Number of HVGs (typically 2000-3000)\n- `min_mean`, `max_mean`, `min_disp`: HVG selection parameters\n\n### Dimensionality Reduction\n- `n_pcs`: Number of principal components (check variance ratio plot)\n- `n_neighbors`: Number of neighbors (typically 10-30)\n\n### Clustering\n- `resolution`: Clustering granularity (0.4-1.2, higher = more clusters)\n\n## Common Pitfalls and Best Practices\n\n1. **Always save raw counts**: `adata.raw = adata` before filtering genes\n2. **Check QC plots carefully**: Adjust thresholds based on dataset quality\n3. **Use Leiden over Louvain**: More efficient and better results\n4. **Try multiple clustering resolutions**: Find optimal granularity\n5. **Validate cell type annotations**: Use multiple marker genes\n6. **Use `use_raw=True` for gene expression plots**: Shows original counts\n7. **Check PCA variance ratio**: Determine optimal number of PCs\n8. **Save intermediate results**: Long workflows can fail partway through\n\n## Bundled Resources\n\n### scripts\u002Fqc_analysis.py\nAutomated quality control script that calculates metrics, generates plots, and filters data:\n\n```bash\npython scripts\u002Fqc_analysis.py input.h5ad --output filtered.h5ad \\\n    --mt-threshold 5 --min-genes 200 --min-cells 3\n```\n\n### references\u002Fstandard_workflow.md\nComplete step-by-step workflow with detailed explanations and code examples for:\n- Data loading and setup\n- Quality control with visualization\n- Normalization and scaling\n- Feature selection\n- Dimensionality reduction (PCA, UMAP, t-SNE)\n- Clustering (Leiden, Louvain)\n- Marker gene identification\n- Cell type annotation\n- Trajectory inference\n- Differential expression\n\nRead this reference when performing a complete analysis from scratch.\n\n### references\u002Fapi_reference.md\nQuick reference guide for scanpy functions organized by module:\n- Reading\u002Fwriting data (`sc.read_*`, `adata.write_*`)\n- Preprocessing (`sc.pp.*`)\n- Tools (`sc.tl.*`)\n- Plotting (`sc.pl.*`)\n- AnnData structure and manipulation\n- Settings and utilities\n\nUse this for quick lookup of function signatures and common parameters.\n\n### references\u002Fplotting_guide.md\nComprehensive visualization guide including:\n- Quality control plots\n- Dimensionality reduction visualizations\n- Clustering visualizations\n- Marker gene plots (heatmaps, dot plots, violin plots)\n- Trajectory and pseudotime plots\n- Publication-quality customization\n- Multi-panel figures\n- Color palettes and styling\n\nConsult this when creating publication-ready figures.\n\n### assets\u002Fanalysis_template.py\nComplete analysis template providing a full workflow from data loading through cell type annotation. Copy and customize this template for new analyses:\n\n```bash\ncp assets\u002Fanalysis_template.py my_analysis.py\n# Edit parameters and run\npython my_analysis.py\n```\n\nThe template includes all standard steps with configurable parameters and helpful comments.\n\n## Additional Resources\n\n- **Official scanpy documentation**: https:\u002F\u002Fscanpy.readthedocs.io\u002F\n- **Scanpy tutorials**: https:\u002F\u002Fscanpy-tutorials.readthedocs.io\u002F\n- **scverse ecosystem**: https:\u002F\u002Fscverse.org\u002F (related tools: squidpy, scvi-tools, cellrank)\n- **Best practices**: Luecken & Theis (2019) \"Current best practices in single-cell RNA-seq\"\n\n## Tips for Effective Analysis\n\n1. **Start with the template**: Use `assets\u002Fanalysis_template.py` as a starting point\n2. **Run QC script first**: Use `scripts\u002Fqc_analysis.py` for initial filtering\n3. **Consult references as needed**: Load workflow and API references into context\n4. **Iterate on clustering**: Try multiple resolutions and visualization methods\n5. **Validate biologically**: Check marker genes match expected cell types\n6. **Document parameters**: Record QC thresholds and analysis settings\n7. **Save checkpoints**: Write intermediate results at key steps\n",{"name":5,"description":7},"main","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add JSGforever\u002Fskillguard --skill scanpy",{"tool":55,"command":56},"bunx","bunx skills add JSGforever\u002Fskillguard --skill scanpy",{"tool":58,"command":59},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill scanpy","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002FSKILL.md",true,[63,69,75,79,83],{"path":64,"resourceType":65,"resourceTypeDesc":66,"sizeBytes":67,"sourceHtmlUrl":68},"assets\u002Fanalysis_template.py","SCRIPT","脚本",9515,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002Fassets\u002Fanalysis_template.py",{"path":70,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":73,"sourceHtmlUrl":74},"references\u002Fapi_reference.md","REFERENCE","参考文档",7415,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002Freferences\u002Fapi_reference.md",{"path":76,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fplotting_guide.md",9695,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002Freferences\u002Fplotting_guide.md",{"path":80,"resourceType":71,"resourceTypeDesc":72,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fstandard_workflow.md",5529,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002Freferences\u002Fstandard_workflow.md",{"path":84,"resourceType":65,"resourceTypeDesc":66,"sizeBytes":85,"sourceHtmlUrl":86},"scripts\u002Fqc_analysis.py",6706,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fscanpy\u002Fscripts\u002Fqc_analysis.py",[88,120,135,162,184,205],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":99,"publishedAt":37},"01M71HY2SNXBA9Q00WZDTZQ31Z","skillguard-codex-scanner","Hybrid deterministic+LLM agent-skill security scanner. Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[94],{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","when","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":121,"name":122,"nameZh":6,"description":123,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":124,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":132,"publishedAt":37},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[127],{"categoryNo":128,"categoryName":129,"taxonomyType":130,"taxonomyTypeDesc":131},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[28,133,134],"data exfiltrator","files",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":141,"publishedAt":37},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","access","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":171,"publishedAt":37},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[168],{"categoryNo":169,"categoryName":170,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000009","前端开发",[151,172,159,160,173,174,175,176,177,178,142,179,180,155,157,181,158,161,182,183],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":191,"publishedAt":37},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[190],{"categoryNo":169,"categoryName":170,"taxonomyType":97,"taxonomyTypeDesc":98},[192,161,193,194,195,196,151,142,197,198,199,157,158,159,200,160,201,202,203,204],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":206,"name":207,"nameZh":6,"description":208,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":209,"categories":210,"keywords":218,"publishedAt":37},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[211,214,217],{"categoryNo":212,"categoryName":213,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":215,"categoryName":216,"taxonomyType":97,"taxonomyTypeDesc":98},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":95,"categoryName":96,"taxonomyType":97,"taxonomyTypeDesc":98},[219,220,221,222,223,224,225,149,177,226,227,228,158,183,229,204,230,231,232,233],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]