feat(plotting): Add group_cmaps parameter to sc.pl.dotplot to specify unique colormaps for each group

[rwbaber]

• Closes #
• Tests included: New tests were added for the core functionality, swap_axes compatibility, and error conditions.

• Release notes not necessary because:

Description

This PR introduces a new feature to sc.pl.dotplot: the group_cmaps parameter. This allows users to assign a unique colormap to each category in a dot plot, which is particularly useful for creating publication-ready figures that align with a paper's established color scheme for different cell types.

Key Changes

• group_cmaps parameter: Added a new parameter to sc.pl.dotplot and the DotPlot class that accepts a dictionary mapping group names to colormap names.
• Stacked Colorbar Legend: Implemented a new stacked horizontal colorbar legend (via _plot_stacked_colorbars function) that appears when group_cmaps is used. This legend clearly displays the colormap and for each group with corresponding label.
• Robust Error Handling: Added a ValueError that is raised if group_cmaps is used but a group in the plot data is not defined in the dictionary.
• Pre-existing Layout Bug Fix: Set the width of the dot plot legend area within the dotplot wrapper function to 2.0 (instead of DEFAULT_LEGENDS_WIDTH=1.5) to fix a pre-existing issue where the circles of the size legend could overlap (especially with dendrogram=True).
  • As a result, the reference images (expected.png) for several existing dotplot tests were updated to reflect the new, corrected layout.
• Code Refactoring: The DotPlot.__init__ method was refactored into a smaller helper function (_prepare_dot_data) to improve readability and pass the automated code complexity checks by pre-commit.
• Comprehensive Tests: Added new tests to verify the core functionality, ensure compatibility with swap_axes, and confirm that the correct errors and warnings are raised.

Example Plot

from testing.scanpy._helpers.data import (pbmc3k_processed)
import scanpy as sc

adata = pbmc3k_processed()

markers = ["SERPINB1", "IGFBP7", "GNLY", "IFITM1", "IMP3", "UBALD2", "LTB", "CLPP"]

group_cmaps = {
    "CD4 T cells":              "pink_r",
    "CD14+ Monocytes":          "Reds",
    "B cells":                  "Greys",
    "CD8 T cells":              "Greens",
    "NK cells":                 "Oranges",
    "FCGR3A+ Monocytes":        "Purples",
    "Dendritic cells":          "Blues",
    "Megakaryocytes":           "bone_r",
}

sc.pl.dotplot(
    adata,
    markers,
    groupby="louvain",
    group_cmaps=group_cmaps,  # Define custom color maps for each group from 'groupby'
    dendrogram=True,
    standard_scale='var',
    dot_max=1.0,
    title="Dot Plot with Unique Per-Group Colormaps\n(pbmc3k_processed)",
)

[flying-sheep]

Good idea! However I’m concerned that the color maps aren’t visually comparable.

• Did you think about adding a color bar instead, like for the heatmap

• alternatively, I’d replace the parameter with group_colors or so. We could then use cspace_convert to make colormaps on the fly, like this: https://gist.github.com/flying-sheep/a38e925d0b0f7ad5c6eb1ada8fa5c128

  Unfortunately colorspacious seems unmaintained, and I haven’t figured out to make this code numerically robust. Document range of JCh channels njsmith/colorspacious#18

[rwbaber]

Thanks for the feedback! The visual comparability is a good point, I didn’t think about that.
I personally prefer your second suggestion of making (perceptually uniform) colormaps on the fly.
Though, the heatmap-like color bar could be an additional option. I think that should be fairly straightforward to implement.

Since colorspacious is unmaintained, perhaps we can check some alternatives. After a quick search, I found this post on the Oklab color space which might be another way to implement this: Converting from linear sRGB to Oklab.
I’ll look into it a bit more some other time and report back!