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+# Scanpy API Quick Reference
+
+Quick reference for commonly used scanpy functions organized by module.
+
+## Import Convention
+
+```python
+import scanpy as sc
+```
+
+## Reading and Writing Data (sc.read_*)
+
+### Reading Functions
+
+```python
+sc.read_10x_h5(filename)                    # Read 10X HDF5 file
+sc.read_10x_mtx(path)                       # Read 10X mtx directory
+sc.read_h5ad(filename)                      # Read h5ad (AnnData) file
+sc.read_csv(filename)                       # Read CSV file
+sc.read_excel(filename)                     # Read Excel file
+sc.read_loom(filename)                      # Read loom file
+sc.read_text(filename)                      # Read text file
+sc.read_visium(path)                        # Read Visium spatial data
+```
+
+### Writing Functions
+
+```python
+adata.write_h5ad(filename)                  # Write to h5ad format
+adata.write_csvs(dirname)                   # Write to CSV files
+adata.write_loom(filename)                  # Write to loom format
+adata.write_zarr(filename)                  # Write to zarr format
+```
+
+## Preprocessing (sc.pp.*)
+
+### Quality Control
+
+```python
+sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], inplace=True)
+sc.pp.filter_cells(adata, min_genes=200)
+sc.pp.filter_genes(adata, min_cells=3)
+```
+
+### Normalization and Transformation
+
+```python
+sc.pp.normalize_total(adata, target_sum=1e4)    # Normalize to target sum
+sc.pp.log1p(adata)                               # Log(x + 1) transformation
+sc.pp.sqrt(adata)                                # Square root transformation
+```
+
+### Feature Selection
+
+```python
+sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
+sc.pp.highly_variable_genes(adata, flavor='seurat_v3', n_top_genes=2000)
+```
+
+### Scaling and Regression
+
+```python
+sc.pp.scale(adata, max_value=10)                      # Scale to unit variance
+sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])  # Regress out unwanted variation
+```
+
+### Dimensionality Reduction (Preprocessing)
+
+```python
+sc.pp.pca(adata, n_comps=50)                     # Principal component analysis
+sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40) # Compute neighborhood graph
+```
+
+### Batch Correction
+
+```python
+sc.pp.combat(adata, key='batch')                 # ComBat batch correction
+```
+
+## Tools (sc.tl.*)
+
+### Dimensionality Reduction
+
+```python
+sc.tl.pca(adata, svd_solver='arpack')            # PCA
+sc.tl.umap(adata)                                 # UMAP embedding
+sc.tl.tsne(adata)                                 # t-SNE embedding
+sc.tl.diffmap(adata)                              # Diffusion map
+sc.tl.draw_graph(adata, layout='fa')             # Force-directed graph
+```
+
+### Clustering
+
+```python
+sc.tl.leiden(adata, resolution=0.5)              # Leiden clustering (recommended)
+sc.tl.louvain(adata, resolution=0.5)             # Louvain clustering
+sc.tl.kmeans(adata, n_clusters=10)               # K-means clustering
+```
+
+### Marker Genes and Differential Expression
+
+```python
+sc.tl.rank_genes_groups(adata, groupby='leiden', method='wilcoxon')
+sc.tl.rank_genes_groups(adata, groupby='leiden', method='t-test')
+sc.tl.rank_genes_groups(adata, groupby='leiden', method='logreg')
+
+# Get results as dataframe
+sc.get.rank_genes_groups_df(adata, group='0')
+```
+
+### Trajectory Inference
+
+```python
+sc.tl.paga(adata, groups='leiden')               # PAGA trajectory
+sc.tl.dpt(adata)                                  # Diffusion pseudotime
+```
+
+### Gene Scoring
+
+```python
+sc.tl.score_genes(adata, gene_list, score_name='score')
+sc.tl.score_genes_cell_cycle(adata, s_genes, g2m_genes)
+```
+
+### Embeddings and Projections
+
+```python
+sc.tl.ingest(adata, adata_ref)                   # Map to reference
+sc.tl.embedding_density(adata, basis='umap', groupby='leiden')
+```
+
+## Plotting (sc.pl.*)
+
+### Basic Embeddings
+
+```python
+sc.pl.umap(adata, color='leiden')                # UMAP plot
+sc.pl.tsne(adata, color='gene_name')             # t-SNE plot
+sc.pl.pca(adata, color='leiden')                 # PCA plot
+sc.pl.diffmap(adata, color='leiden')             # Diffusion map plot
+```
+
+### Heatmaps and Dot Plots
+
+```python
+sc.pl.heatmap(adata, var_names=genes, groupby='leiden')
+sc.pl.dotplot(adata, var_names=genes, groupby='leiden')
+sc.pl.matrixplot(adata, var_names=genes, groupby='leiden')
+sc.pl.stacked_violin(adata, var_names=genes, groupby='leiden')
+```
+
+### Violin and Scatter Plots
+
+```python
+sc.pl.violin(adata, keys=['gene1', 'gene2'], groupby='leiden')
+sc.pl.scatter(adata, x='gene1', y='gene2', color='leiden')
+```
+
+### Marker Gene Visualization
+
+```python
+sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
+sc.pl.rank_genes_groups_violin(adata, groups='0')
+sc.pl.rank_genes_groups_heatmap(adata, n_genes=10)
+sc.pl.rank_genes_groups_dotplot(adata, n_genes=5)
+```
+
+### Trajectory Visualization
+
+```python
+sc.pl.paga(adata, color='leiden')                # PAGA graph
+sc.pl.dpt_timeseries(adata)                      # DPT timeseries
+```
+
+### QC Plots
+
+```python
+sc.pl.highest_expr_genes(adata, n_top=20)
+sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'])
+sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts')
+```
+
+### Advanced Plots
+
+```python
+sc.pl.dendrogram(adata, groupby='leiden')
+sc.pl.correlation_matrix(adata, groupby='leiden')
+sc.pl.tracksplot(adata, var_names=genes, groupby='leiden')
+```
+
+## Common Parameters
+
+### Color Parameters
+- `color`: Variable(s) to color by (gene name, obs column)
+- `use_raw`: Use `.raw` attribute of adata
+- `palette`: Color palette to use
+- `vmin`, `vmax`: Color scale limits
+
+### Layout Parameters
+- `basis`: Embedding basis ('umap', 'tsne', 'pca', etc.)
+- `legend_loc`: Legend location ('on data', 'right margin', etc.)
+- `size`: Point size
+- `alpha`: Point transparency
+
+### Saving Parameters
+- `save`: Filename to save plot
+- `show`: Whether to show plot
+
+## AnnData Structure
+
+```python
+adata.X                    # Expression matrix (cells × genes)
+adata.obs                  # Cell annotations (DataFrame)
+adata.var                  # Gene annotations (DataFrame)
+adata.uns                  # Unstructured annotations (dict)
+adata.obsm                 # Multi-dimensional cell annotations (e.g., PCA, UMAP)
+adata.varm                 # Multi-dimensional gene annotations
+adata.layers               # Additional data layers
+adata.raw                  # Raw data backup
+
+# Access
+adata.obs_names            # Cell barcodes
+adata.var_names            # Gene names
+adata.shape                # (n_cells, n_genes)
+
+# Slicing
+adata[cell_indices, gene_indices]
+adata[:, adata.var_names.isin(gene_list)]
+adata[adata.obs['leiden'] == '0', :]
+```
+
+## Settings
+
+```python
+sc.settings.verbosity = 3              # 0=error, 1=warning, 2=info, 3=hint
+sc.settings.set_figure_params(dpi=80, facecolor='white')
+sc.settings.autoshow = False           # Don't show plots automatically
+sc.settings.autosave = True            # Autosave figures
+sc.settings.figdir = './figures/'      # Figure directory
+sc.settings.cachedir = './cache/'      # Cache directory
+sc.settings.n_jobs = 8                 # Number of parallel jobs
+```
+
+## Useful Utilities
+
+```python
+sc.logging.print_versions()            # Print version information
+sc.logging.print_memory_usage()        # Print memory usage
+adata.copy()                           # Create a copy of AnnData object
+adata.concatenate([adata1, adata2])    # Concatenate AnnData objects
+```
--- a/skills/scanpy/references/plotting_guide.md
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+# Scanpy Plotting Guide
+
+Comprehensive guide for creating publication-quality visualizations with scanpy.
+
+## General Plotting Principles
+
+All scanpy plotting functions follow consistent patterns:
+- Functions in `sc.pl.*` mirror analysis functions in `sc.tl.*`
+- Most accept `color` parameter for gene names or metadata columns
+- Results are saved via `save` parameter
+- Multiple plots can be generated in a single call
+
+## Essential Quality Control Plots
+
+### Visualize QC Metrics
+
+```python
+# Violin plots for QC metrics
+sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
+             jitter=0.4, multi_panel=True, save='_qc_violin.pdf')
+
+# Scatter plots to identify outliers
+sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt', save='_qc_mt.pdf')
+sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts', save='_qc_genes.pdf')
+
+# Highest expressing genes
+sc.pl.highest_expr_genes(adata, n_top=20, save='_highest_expr.pdf')
+```
+
+### Post-filtering QC
+
+```python
+# Compare before and after filtering
+sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts'],
+             groupby='sample', save='_post_filter.pdf')
+```
+
+## Dimensionality Reduction Visualizations
+
+### PCA Plots
+
+```python
+# Basic PCA
+sc.pl.pca(adata, color='leiden', save='_pca.pdf')
+
+# PCA colored by gene expression
+sc.pl.pca(adata, color=['gene1', 'gene2', 'gene3'], save='_pca_genes.pdf')
+
+# Variance ratio plot (elbow plot)
+sc.pl.pca_variance_ratio(adata, log=True, n_pcs=50, save='_variance.pdf')
+
+# PCA loadings
+sc.pl.pca_loadings(adata, components=[1, 2, 3], save='_loadings.pdf')
+```
+
+### UMAP Plots
+
+```python
+# Basic UMAP with clusters
+sc.pl.umap(adata, color='leiden', legend_loc='on data', save='_umap_leiden.pdf')
+
+# UMAP colored by multiple variables
+sc.pl.umap(adata, color=['leiden', 'cell_type', 'batch'],
+           save='_umap_multi.pdf')
+
+# UMAP with gene expression
+sc.pl.umap(adata, color=['CD3D', 'CD14', 'MS4A1'],
+           use_raw=False, save='_umap_genes.pdf')
+
+# Customize appearance
+sc.pl.umap(adata, color='leiden',
+           palette='Set2',
+           size=50,
+           alpha=0.8,
+           frameon=False,
+           title='Cell Types',
+           save='_umap_custom.pdf')
+```
+
+### t-SNE Plots
+
+```python
+# t-SNE with clusters
+sc.pl.tsne(adata, color='leiden', legend_loc='right margin', save='_tsne.pdf')
+
+# Multiple t-SNE perplexities (if computed)
+sc.pl.tsne(adata, color='leiden', save='_tsne_default.pdf')
+```
+
+## Clustering Visualizations
+
+### Basic Cluster Plots
+
+```python
+# UMAP with cluster annotations
+sc.pl.umap(adata, color='leiden', add_outline=True,
+           legend_loc='on data', legend_fontsize=12,
+           legend_fontoutline=2, frameon=False,
+           save='_clusters.pdf')
+
+# Show cluster proportions
+sc.pl.umap(adata, color='leiden', size=50, edges=True,
+           edges_width=0.1, save='_clusters_edges.pdf')
+```
+
+### Cluster Comparison
+
+```python
+# Compare clustering results
+sc.pl.umap(adata, color=['leiden', 'louvain'],
+           save='_cluster_comparison.pdf')
+
+# Cluster dendrogram
+sc.tl.dendrogram(adata, groupby='leiden')
+sc.pl.dendrogram(adata, groupby='leiden', save='_dendrogram.pdf')
+```
+
+## Marker Gene Visualizations
+
+### Ranked Marker Genes
+
+```python
+# Overview of top markers per cluster
+sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False,
+                        save='_marker_overview.pdf')
+
+# Heatmap of top markers
+sc.pl.rank_genes_groups_heatmap(adata, n_genes=10, groupby='leiden',
+                                 show_gene_labels=True,
+                                 save='_marker_heatmap.pdf')
+
+# Dot plot of markers
+sc.pl.rank_genes_groups_dotplot(adata, n_genes=5,
+                                 save='_marker_dotplot.pdf')
+
+# Stacked violin plots
+sc.pl.rank_genes_groups_stacked_violin(adata, n_genes=5,
+                                        save='_marker_violin.pdf')
+
+# Matrix plot
+sc.pl.rank_genes_groups_matrixplot(adata, n_genes=5,
+                                    save='_marker_matrix.pdf')
+```
+
+### Specific Gene Expression
+
+```python
+# Violin plots for specific genes
+marker_genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7', 'FCGR3A']
+sc.pl.violin(adata, keys=marker_genes, groupby='leiden',
+             save='_markers_violin.pdf')
+
+# Dot plot for curated markers
+sc.pl.dotplot(adata, var_names=marker_genes, groupby='leiden',
+              save='_markers_dotplot.pdf')
+
+# Heatmap for specific genes
+sc.pl.heatmap(adata, var_names=marker_genes, groupby='leiden',
+              swap_axes=True, save='_markers_heatmap.pdf')
+
+# Stacked violin for gene sets
+sc.pl.stacked_violin(adata, var_names=marker_genes, groupby='leiden',
+                     save='_markers_stacked.pdf')
+```
+
+### Gene Expression on Embeddings
+
+```python
+# Multiple genes on UMAP
+genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7']
+sc.pl.umap(adata, color=genes, cmap='viridis',
+           save='_umap_markers.pdf')
+
+# Gene expression with custom colormap
+sc.pl.umap(adata, color='CD3D', cmap='Reds',
+           vmin=0, vmax=3, save='_umap_cd3d.pdf')
+```
+
+## Trajectory and Pseudotime Visualizations
+
+### PAGA Plots
+
+```python
+# PAGA graph
+sc.pl.paga(adata, color='leiden', save='_paga.pdf')
+
+# PAGA with gene expression
+sc.pl.paga(adata, color=['leiden', 'dpt_pseudotime'],
+           save='_paga_pseudotime.pdf')
+
+# PAGA overlaid on UMAP
+sc.pl.umap(adata, color='leiden', save='_umap_with_paga.pdf',
+           edges=True, edges_color='gray')
+```
+
+### Pseudotime Plots
+
+```python
+# DPT pseudotime on UMAP
+sc.pl.umap(adata, color='dpt_pseudotime', save='_umap_dpt.pdf')
+
+# Gene expression along pseudotime
+sc.pl.dpt_timeseries(adata, save='_dpt_timeseries.pdf')
+
+# Heatmap ordered by pseudotime
+sc.pl.heatmap(adata, var_names=genes, groupby='leiden',
+              use_raw=False, show_gene_labels=True,
+              save='_pseudotime_heatmap.pdf')
+```
+
+## Advanced Visualizations
+
+### Tracks Plot (Gene Expression Trends)
+
+```python
+# Show gene expression across cell types
+sc.pl.tracksplot(adata, var_names=marker_genes, groupby='leiden',
+                 save='_tracks.pdf')
+```
+
+### Correlation Matrix
+
+```python
+# Correlation between clusters
+sc.pl.correlation_matrix(adata, groupby='leiden',
+                         save='_correlation.pdf')
+```
+
+### Embedding Density
+
+```python
+# Cell density on UMAP
+sc.tl.embedding_density(adata, basis='umap', groupby='cell_type')
+sc.pl.embedding_density(adata, basis='umap', key='umap_density_cell_type',
+                        save='_density.pdf')
+```
+
+## Multi-Panel Figures
+
+### Creating Panel Figures
+
+```python
+import matplotlib.pyplot as plt
+
+# Create multi-panel figure
+fig, axes = plt.subplots(2, 2, figsize=(12, 12))
+
+# Plot on specific axes
+sc.pl.umap(adata, color='leiden', ax=axes[0, 0], show=False)
+sc.pl.umap(adata, color='CD3D', ax=axes[0, 1], show=False)
+sc.pl.umap(adata, color='CD14', ax=axes[1, 0], show=False)
+sc.pl.umap(adata, color='MS4A1', ax=axes[1, 1], show=False)
+
+plt.tight_layout()
+plt.savefig('figures/multi_panel.pdf')
+plt.show()
+```
+
+## Publication-Quality Customization
+
+### High-Quality Settings
+
+```python
+# Set publication-quality defaults
+sc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5),
+                               facecolor='white')
+
+# Vector graphics output
+sc.settings.figdir = './figures/'
+sc.settings.file_format_figs = 'pdf'  # or 'svg'
+```
+
+### Custom Color Palettes
+
+```python
+# Use custom colors
+custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
+sc.pl.umap(adata, color='leiden', palette=custom_colors,
+           save='_custom_colors.pdf')
+
+# Continuous color maps
+sc.pl.umap(adata, color='CD3D', cmap='viridis', save='_viridis.pdf')
+sc.pl.umap(adata, color='CD3D', cmap='RdBu_r', save='_rdbu.pdf')
+```
+
+### Remove Axes and Frames
+
+```python
+# Clean plot without axes
+sc.pl.umap(adata, color='leiden', frameon=False,
+           save='_clean.pdf')
+
+# No legend
+sc.pl.umap(adata, color='leiden', legend_loc=None,
+           save='_no_legend.pdf')
+```
+
+## Exporting Plots
+
+### Save Individual Plots
+
+```python
+# Automatic saving with save parameter
+sc.pl.umap(adata, color='leiden', save='_leiden.pdf')
+# Saves to: sc.settings.figdir + 'umap_leiden.pdf'
+
+# Manual saving
+import matplotlib.pyplot as plt
+fig = sc.pl.umap(adata, color='leiden', show=False, return_fig=True)
+fig.savefig('figures/my_umap.pdf', dpi=300, bbox_inches='tight')
+```
+
+### Batch Export
+
+```python
+# Save multiple versions
+for gene in ['CD3D', 'CD14', 'MS4A1']:
+    sc.pl.umap(adata, color=gene, save=f'_{gene}.pdf')
+```
+
+## Common Customization Parameters
+
+### Layout Parameters
+- `figsize`: Figure size (width, height)
+- `frameon`: Show frame around plot
+- `title`: Plot title
+- `legend_loc`: 'right margin', 'on data', 'best', or None
+- `legend_fontsize`: Font size for legend
+- `size`: Point size
+
+### Color Parameters
+- `color`: Variable(s) to color by
+- `palette`: Color palette (e.g., 'Set1', 'viridis')
+- `cmap`: Colormap for continuous variables
+- `vmin`, `vmax`: Color scale limits
+- `use_raw`: Use raw counts for gene expression
+
+### Saving Parameters
+- `save`: Filename suffix for saving
+- `show`: Whether to display plot
+- `dpi`: Resolution for raster formats
+
+## Tips for Publication Figures
+
+1. **Use vector formats**: PDF or SVG for scalable graphics
+2. **High DPI**: Set dpi=300 or higher for raster images
+3. **Consistent styling**: Use the same color palette across figures
+4. **Clear labels**: Ensure gene names and cell types are readable
+5. **White background**: Use `facecolor='white'` for publications
+6. **Remove clutter**: Set `frameon=False` for cleaner appearance
+7. **Legend placement**: Use 'on data' for compact figures
+8. **Color blind friendly**: Consider palettes like 'colorblind' or 'Set2'
--- a/skills/scanpy/references/standard_workflow.md
+++ b/skills/scanpy/references/standard_workflow.md
@@ -0,0 +1,206 @@
+# Standard Scanpy Workflow for Single-Cell Analysis
+
+This document outlines the standard workflow for analyzing single-cell RNA-seq data using scanpy.
+
+## Complete Analysis Pipeline
+
+### 1. Data Loading and Initial Setup
+
+```python
+import scanpy as sc
+import pandas as pd
+import numpy as np
+
+# Configure scanpy settings
+sc.settings.verbosity = 3  # verbosity: errors (0), warnings (1), info (2), hints (3)
+sc.settings.set_figure_params(dpi=80, facecolor='white')
+
+# Load data (various formats)
+adata = sc.read_10x_mtx('path/to/data/')  # For 10X data
+# adata = sc.read_h5ad('path/to/data.h5ad')  # For h5ad format
+# adata = sc.read_csv('path/to/data.csv')  # For CSV format
+```
+
+### 2. Quality Control (QC)
+
+```python
+# Calculate QC metrics
+sc.pp.calculate_qc_metrics(adata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)
+
+# Common filtering thresholds (adjust based on dataset)
+sc.pp.filter_cells(adata, min_genes=200)
+sc.pp.filter_genes(adata, min_cells=3)
+
+# Remove cells with high mitochondrial content
+adata = adata[adata.obs.pct_counts_mt < 5, :]
+
+# Visualize QC metrics
+sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
+             jitter=0.4, multi_panel=True)
+sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt')
+sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts')
+```
+
+### 3. Normalization
+
+```python
+# Normalize to 10,000 counts per cell
+sc.pp.normalize_total(adata, target_sum=1e4)
+
+# Log-transform the data
+sc.pp.log1p(adata)
+
+# Store normalized data in raw for later use
+adata.raw = adata
+```
+
+### 4. Feature Selection
+
+```python
+# Identify highly variable genes
+sc.pp.highly_variable_genes(adata, min_mean=0.0125, max_mean=3, min_disp=0.5)
+
+# Visualize highly variable genes
+sc.pl.highly_variable_genes(adata)
+
+# Subset to highly variable genes
+adata = adata[:, adata.var.highly_variable]
+```
+
+### 5. Scaling and Regression
+
+```python
+# Regress out effects of total counts per cell and percent mitochondrial genes
+sc.pp.regress_out(adata, ['total_counts', 'pct_counts_mt'])
+
+# Scale data to unit variance and zero mean
+sc.pp.scale(adata, max_value=10)
+```
+
+### 6. Dimensionality Reduction
+
+```python
+# Principal Component Analysis (PCA)
+sc.tl.pca(adata, svd_solver='arpack')
+
+# Visualize PCA results
+sc.pl.pca(adata, color='CST3')
+sc.pl.pca_variance_ratio(adata, log=True)
+
+# Computing neighborhood graph
+sc.pp.neighbors(adata, n_neighbors=10, n_pcs=40)
+
+# UMAP for visualization
+sc.tl.umap(adata)
+
+# t-SNE (alternative to UMAP)
+# sc.tl.tsne(adata)
+```
+
+### 7. Clustering
+
+```python
+# Leiden clustering (recommended)
+sc.tl.leiden(adata, resolution=0.5)
+
+# Alternative: Louvain clustering
+# sc.tl.louvain(adata, resolution=0.5)
+
+# Visualize clustering results
+sc.pl.umap(adata, color=['leiden'], legend_loc='on data')
+```
+
+### 8. Marker Gene Identification
+
+```python
+# Find marker genes for each cluster
+sc.tl.rank_genes_groups(adata, 'leiden', method='wilcoxon')
+
+# Visualize top marker genes
+sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)
+
+# Get marker gene dataframe
+marker_genes = sc.get.rank_genes_groups_df(adata, group='0')
+
+# Visualize specific markers
+sc.pl.umap(adata, color=['leiden', 'CST3', 'NKG7'])
+```
+
+### 9. Cell Type Annotation
+
+```python
+# Manual annotation based on marker genes
+cluster_annotations = {
+    '0': 'CD4 T cells',
+    '1': 'CD14+ Monocytes',
+    '2': 'B cells',
+    '3': 'CD8 T cells',
+    # ... add more annotations
+}
+adata.obs['cell_type'] = adata.obs['leiden'].map(cluster_annotations)
+
+# Visualize annotated cell types
+sc.pl.umap(adata, color='cell_type', legend_loc='on data')
+```
+
+### 10. Saving Results
+
+```python
+# Save the processed AnnData object
+adata.write('results/processed_data.h5ad')
+
+# Export results to CSV
+adata.obs.to_csv('results/cell_metadata.csv')
+adata.var.to_csv('results/gene_metadata.csv')
+```
+
+## Additional Analysis Options
+
+### Trajectory Inference
+
+```python
+# PAGA (Partition-based graph abstraction)
+sc.tl.paga(adata, groups='leiden')
+sc.pl.paga(adata, color=['leiden'])
+
+# Diffusion pseudotime (DPT)
+adata.uns['iroot'] = np.flatnonzero(adata.obs['leiden'] == '0')[0]
+sc.tl.dpt(adata)
+sc.pl.umap(adata, color=['dpt_pseudotime'])
+```
+
+### Differential Expression Between Conditions
+
+```python
+# Compare conditions within a cell type
+sc.tl.rank_genes_groups(adata, groupby='condition', groups=['treated'],
+                         reference='control', method='wilcoxon')
+sc.pl.rank_genes_groups(adata, groups=['treated'])
+```
+
+### Gene Set Scoring
+
+```python
+# Score cells for gene set expression
+gene_set = ['CD3D', 'CD3E', 'CD3G']
+sc.tl.score_genes(adata, gene_set, score_name='T_cell_score')
+sc.pl.umap(adata, color='T_cell_score')
+```
+
+## Common Parameters to Adjust
+
+- **QC thresholds**: `min_genes`, `min_cells`, `pct_counts_mt` - depends on dataset quality
+- **Normalization target**: Usually 1e4, but can be adjusted
+- **HVG parameters**: Affects feature selection stringency
+- **PCA components**: Check variance ratio plot to determine optimal number
+- **Clustering resolution**: Higher values give more clusters (typically 0.4-1.2)
+- **n_neighbors**: Affects granularity of UMAP and clustering (typically 10-30)
+
+## Best Practices
+
+1. Always visualize QC metrics before filtering
+2. Save raw counts before normalization (`adata.raw = adata`)
+3. Use Leiden instead of Louvain for clustering (more efficient)
+4. Try multiple clustering resolutions to find optimal granularity
+5. Validate cell type annotations with known marker genes
+6. Save intermediate results at key steps