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Zhongwei Li
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skills/scanpy/references/plotting_guide.md
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skills/scanpy/references/plotting_guide.md
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# Scanpy Plotting Guide
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Comprehensive guide for creating publication-quality visualizations with scanpy.
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## General Plotting Principles
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All scanpy plotting functions follow consistent patterns:
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- Functions in `sc.pl.*` mirror analysis functions in `sc.tl.*`
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- Most accept `color` parameter for gene names or metadata columns
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- Results are saved via `save` parameter
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- Multiple plots can be generated in a single call
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## Essential Quality Control Plots
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### Visualize QC Metrics
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```python
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# Violin plots for QC metrics
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sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
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jitter=0.4, multi_panel=True, save='_qc_violin.pdf')
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# Scatter plots to identify outliers
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sc.pl.scatter(adata, x='total_counts', y='pct_counts_mt', save='_qc_mt.pdf')
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sc.pl.scatter(adata, x='total_counts', y='n_genes_by_counts', save='_qc_genes.pdf')
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# Highest expressing genes
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sc.pl.highest_expr_genes(adata, n_top=20, save='_highest_expr.pdf')
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```
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### Post-filtering QC
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```python
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# Compare before and after filtering
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sc.pl.violin(adata, ['n_genes_by_counts', 'total_counts'],
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groupby='sample', save='_post_filter.pdf')
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```
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## Dimensionality Reduction Visualizations
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### PCA Plots
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```python
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# Basic PCA
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sc.pl.pca(adata, color='leiden', save='_pca.pdf')
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# PCA colored by gene expression
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sc.pl.pca(adata, color=['gene1', 'gene2', 'gene3'], save='_pca_genes.pdf')
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# Variance ratio plot (elbow plot)
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sc.pl.pca_variance_ratio(adata, log=True, n_pcs=50, save='_variance.pdf')
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# PCA loadings
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sc.pl.pca_loadings(adata, components=[1, 2, 3], save='_loadings.pdf')
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```
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### UMAP Plots
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```python
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# Basic UMAP with clusters
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sc.pl.umap(adata, color='leiden', legend_loc='on data', save='_umap_leiden.pdf')
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# UMAP colored by multiple variables
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sc.pl.umap(adata, color=['leiden', 'cell_type', 'batch'],
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save='_umap_multi.pdf')
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# UMAP with gene expression
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sc.pl.umap(adata, color=['CD3D', 'CD14', 'MS4A1'],
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use_raw=False, save='_umap_genes.pdf')
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# Customize appearance
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sc.pl.umap(adata, color='leiden',
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palette='Set2',
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size=50,
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alpha=0.8,
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frameon=False,
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title='Cell Types',
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save='_umap_custom.pdf')
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```
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### t-SNE Plots
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```python
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# t-SNE with clusters
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sc.pl.tsne(adata, color='leiden', legend_loc='right margin', save='_tsne.pdf')
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# Multiple t-SNE perplexities (if computed)
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sc.pl.tsne(adata, color='leiden', save='_tsne_default.pdf')
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```
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## Clustering Visualizations
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### Basic Cluster Plots
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```python
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# UMAP with cluster annotations
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sc.pl.umap(adata, color='leiden', add_outline=True,
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legend_loc='on data', legend_fontsize=12,
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legend_fontoutline=2, frameon=False,
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save='_clusters.pdf')
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# Show cluster proportions
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sc.pl.umap(adata, color='leiden', size=50, edges=True,
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edges_width=0.1, save='_clusters_edges.pdf')
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```
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### Cluster Comparison
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```python
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# Compare clustering results
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sc.pl.umap(adata, color=['leiden', 'louvain'],
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save='_cluster_comparison.pdf')
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# Cluster dendrogram
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sc.tl.dendrogram(adata, groupby='leiden')
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sc.pl.dendrogram(adata, groupby='leiden', save='_dendrogram.pdf')
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```
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## Marker Gene Visualizations
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### Ranked Marker Genes
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```python
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# Overview of top markers per cluster
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sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False,
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save='_marker_overview.pdf')
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# Heatmap of top markers
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sc.pl.rank_genes_groups_heatmap(adata, n_genes=10, groupby='leiden',
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show_gene_labels=True,
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save='_marker_heatmap.pdf')
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# Dot plot of markers
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sc.pl.rank_genes_groups_dotplot(adata, n_genes=5,
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save='_marker_dotplot.pdf')
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# Stacked violin plots
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sc.pl.rank_genes_groups_stacked_violin(adata, n_genes=5,
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save='_marker_violin.pdf')
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# Matrix plot
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sc.pl.rank_genes_groups_matrixplot(adata, n_genes=5,
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save='_marker_matrix.pdf')
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```
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### Specific Gene Expression
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```python
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# Violin plots for specific genes
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marker_genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7', 'FCGR3A']
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sc.pl.violin(adata, keys=marker_genes, groupby='leiden',
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save='_markers_violin.pdf')
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# Dot plot for curated markers
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sc.pl.dotplot(adata, var_names=marker_genes, groupby='leiden',
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save='_markers_dotplot.pdf')
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# Heatmap for specific genes
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sc.pl.heatmap(adata, var_names=marker_genes, groupby='leiden',
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swap_axes=True, save='_markers_heatmap.pdf')
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# Stacked violin for gene sets
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sc.pl.stacked_violin(adata, var_names=marker_genes, groupby='leiden',
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save='_markers_stacked.pdf')
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```
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### Gene Expression on Embeddings
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```python
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# Multiple genes on UMAP
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genes = ['CD3D', 'CD14', 'MS4A1', 'NKG7']
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sc.pl.umap(adata, color=genes, cmap='viridis',
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save='_umap_markers.pdf')
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# Gene expression with custom colormap
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sc.pl.umap(adata, color='CD3D', cmap='Reds',
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vmin=0, vmax=3, save='_umap_cd3d.pdf')
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```
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## Trajectory and Pseudotime Visualizations
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### PAGA Plots
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```python
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# PAGA graph
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sc.pl.paga(adata, color='leiden', save='_paga.pdf')
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# PAGA with gene expression
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sc.pl.paga(adata, color=['leiden', 'dpt_pseudotime'],
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save='_paga_pseudotime.pdf')
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# PAGA overlaid on UMAP
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sc.pl.umap(adata, color='leiden', save='_umap_with_paga.pdf',
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edges=True, edges_color='gray')
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```
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### Pseudotime Plots
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```python
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# DPT pseudotime on UMAP
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sc.pl.umap(adata, color='dpt_pseudotime', save='_umap_dpt.pdf')
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# Gene expression along pseudotime
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sc.pl.dpt_timeseries(adata, save='_dpt_timeseries.pdf')
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# Heatmap ordered by pseudotime
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sc.pl.heatmap(adata, var_names=genes, groupby='leiden',
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use_raw=False, show_gene_labels=True,
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save='_pseudotime_heatmap.pdf')
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```
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## Advanced Visualizations
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### Tracks Plot (Gene Expression Trends)
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```python
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# Show gene expression across cell types
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sc.pl.tracksplot(adata, var_names=marker_genes, groupby='leiden',
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save='_tracks.pdf')
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```
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### Correlation Matrix
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```python
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# Correlation between clusters
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sc.pl.correlation_matrix(adata, groupby='leiden',
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save='_correlation.pdf')
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```
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### Embedding Density
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```python
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# Cell density on UMAP
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sc.tl.embedding_density(adata, basis='umap', groupby='cell_type')
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sc.pl.embedding_density(adata, basis='umap', key='umap_density_cell_type',
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save='_density.pdf')
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```
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## Multi-Panel Figures
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### Creating Panel Figures
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```python
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import matplotlib.pyplot as plt
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# Create multi-panel figure
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fig, axes = plt.subplots(2, 2, figsize=(12, 12))
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# Plot on specific axes
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sc.pl.umap(adata, color='leiden', ax=axes[0, 0], show=False)
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sc.pl.umap(adata, color='CD3D', ax=axes[0, 1], show=False)
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sc.pl.umap(adata, color='CD14', ax=axes[1, 0], show=False)
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sc.pl.umap(adata, color='MS4A1', ax=axes[1, 1], show=False)
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plt.tight_layout()
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plt.savefig('figures/multi_panel.pdf')
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plt.show()
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```
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## Publication-Quality Customization
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### High-Quality Settings
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```python
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# Set publication-quality defaults
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sc.settings.set_figure_params(dpi=300, frameon=False, figsize=(5, 5),
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facecolor='white')
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# Vector graphics output
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sc.settings.figdir = './figures/'
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sc.settings.file_format_figs = 'pdf' # or 'svg'
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```
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### Custom Color Palettes
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```python
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# Use custom colors
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custom_colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']
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sc.pl.umap(adata, color='leiden', palette=custom_colors,
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save='_custom_colors.pdf')
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# Continuous color maps
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sc.pl.umap(adata, color='CD3D', cmap='viridis', save='_viridis.pdf')
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sc.pl.umap(adata, color='CD3D', cmap='RdBu_r', save='_rdbu.pdf')
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```
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### Remove Axes and Frames
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```python
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# Clean plot without axes
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sc.pl.umap(adata, color='leiden', frameon=False,
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save='_clean.pdf')
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# No legend
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sc.pl.umap(adata, color='leiden', legend_loc=None,
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save='_no_legend.pdf')
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```
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## Exporting Plots
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### Save Individual Plots
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```python
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# Automatic saving with save parameter
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sc.pl.umap(adata, color='leiden', save='_leiden.pdf')
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# Saves to: sc.settings.figdir + 'umap_leiden.pdf'
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# Manual saving
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import matplotlib.pyplot as plt
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fig = sc.pl.umap(adata, color='leiden', show=False, return_fig=True)
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fig.savefig('figures/my_umap.pdf', dpi=300, bbox_inches='tight')
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```
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### Batch Export
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```python
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# Save multiple versions
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for gene in ['CD3D', 'CD14', 'MS4A1']:
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sc.pl.umap(adata, color=gene, save=f'_{gene}.pdf')
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```
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## Common Customization Parameters
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### Layout Parameters
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- `figsize`: Figure size (width, height)
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- `frameon`: Show frame around plot
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- `title`: Plot title
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- `legend_loc`: 'right margin', 'on data', 'best', or None
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- `legend_fontsize`: Font size for legend
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- `size`: Point size
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### Color Parameters
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- `color`: Variable(s) to color by
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- `palette`: Color palette (e.g., 'Set1', 'viridis')
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- `cmap`: Colormap for continuous variables
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- `vmin`, `vmax`: Color scale limits
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- `use_raw`: Use raw counts for gene expression
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### Saving Parameters
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- `save`: Filename suffix for saving
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- `show`: Whether to display plot
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- `dpi`: Resolution for raster formats
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## Tips for Publication Figures
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1. **Use vector formats**: PDF or SVG for scalable graphics
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2. **High DPI**: Set dpi=300 or higher for raster images
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3. **Consistent styling**: Use the same color palette across figures
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4. **Clear labels**: Ensure gene names and cell types are readable
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5. **White background**: Use `facecolor='white'` for publications
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6. **Remove clutter**: Set `frameon=False` for cleaner appearance
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7. **Legend placement**: Use 'on data' for compact figures
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8. **Color blind friendly**: Consider palettes like 'colorblind' or 'Set2'
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