BuenColors Single Cell Demo¶

This notebook demonstrates the single-cell analysis features in BuenColors, designed to work with Scanpy and AnnData objects.

Installation¶

To use the single-cell features, you need to have scanpy and anndata installed:

pip install buencolors scanpy anndata

✓ Scanpy and AnnData are available

Load Example Dataset¶

We'll use the PBMC3k dataset, a standard example in single-cell analysis that contains ~3000 peripheral blood mononuclear cells.

Dataset shape: (2638, 1838)
Available observations: ['n_genes', 'percent_mito', 'n_counts', 'louvain']

Cell type distribution:
louvain
CD4 T cells          1144
CD14+ Monocytes       480
B cells               342
CD8 T cells           316
NK cells              154
FCGR3A+ Monocytes     150
Dendritic cells        37
Megakaryocytes         15
Name: count, dtype: int64

Clean UMAP Visualization¶

The clean_umap() function creates beautiful, publication-ready UMAP plots with:

• Minimal decorations (no borders, ticks, or frame)
• Custom L-shaped axis indicators with arrowheads
• Automatically ejected legend to the right side
• Shuffled cells to avoid non-random ordering artifacts

Basic Usage - Cell Type Coloring¶

<Figure size 1000x800 with 0 Axes>

Gene Expression Visualization¶

You can also color cells by gene expression levels:

Customizing Axis Indicators¶

You can customize the appearance of the axis indicators:

Comparison: Standard vs Clean UMAP¶

Let's compare the standard Scanpy UMAP with BuenColors' clean version:

Using BuenColors Palettes with UMAPs¶

You can combine clean_umap() with BuenColors' beautiful color palettes:

<Figure size 1000x800 with 0 Axes>

Multiple Color Variables¶

Create a multi-panel figure showing different aspects of your data:

Combining with Pretty Plot Style¶

For even better-looking plots, combine clean_umap() with the BuenColors pretty-plot style:

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Summary¶

The clean_umap() function provides:

1. Minimal decorations - Clean, publication-ready plots without unnecessary visual clutter
2. Custom axis indicators - Professional L-shaped axes with arrowheads
3. Auto-ejected legends - Legends placed outside the plot area to avoid obscuring data
4. Cell shuffling - Automatic randomization to prevent ordering artifacts
5. Full customization - Compatible with all Scanpy UMAP parameters

Parameters¶

• adata: AnnData object containing UMAP coordinates
• color: Column in adata.obs or gene name for coloring
• ax: Optional matplotlib axis (if None, creates new)
• axis_len: Length of axis indicators (default: 0.2)
• thickness: Line width of axis indicators (default: 3.0)
• **kwargs: Additional arguments passed to sc.pl.umap()

Compatibility¶

Requires:

• anndata - for AnnData objects
• scanpy - for UMAP plotting
• matplotlib - for plotting (already a BuenColors dependency)