scverse / scverse/spatialdata-plot
Additive RGB color blending for render_shapes / render_labels (and render_points)
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- Dominant language
- Python
- Stars
- 86
- Forks
- 21
- Avg merge
- 14h 50m
- Merged PRs (30d)
- 3
Description
Motivation
Spun out from #321 (the thread there mixes two unrelated asks — this issue is the second one, originally raised by @brainfo).
`render_images` already supports multi-channel additive compositing: you pass several channels with per-channel colormaps and they blend into a single RGB image, mirroring napari / ImageJ / FIJI for fluorescence microscopy. The same idiom is useful for sequencing-based spatial data, where a user wants to visualize co-expression of 2–3 genes per cell/spot/region on a single axis — red for gene A, green for gene B, blue for gene C, additively blended so co-expressing cells appear yellow/cyan/white.
Today this requires manual post-processing (the user in #321 mentions doing it in Illustrator).
Proposed API
Mirror the existing `render_images` multi-channel convention by letting `color` accept a list of obs/var columns on `render_shapes`, `render_labels`, and `render_points`:
```python
sdata.pl.render_shapes(
color=["Sox2", "Pax6", "Tbr2"],
palette=["red", "green", "blue"], # one color per channel
channels_as_legend=True,
).pl.show()
sdata.pl.render_labels(
color=["Sox2", "Pax6"],
palette=["red", "green"],
).pl.show()
sdata.pl.render_points(
color=["Sox2", "Pax6"],
).pl.show()
```
Output: a single axis (no multi-panel grid). Each shape / label / point gets one composited color derived from its per-column values blended via per-channel colormaps, identical in spirit to the additive multi-channel path already in `render_images`.
Why this is feasible
- The additive-blending machinery already exists in `pl/render.py` for the multi-channel image path (see `render_images` branches 2A/2B/2C). The same per-channel cmap + sum + clip logic applies.
- Single-axis output sidesteps the hierarchy-of-axes complexity that made multi-panel `color=[...]` (the other ask from #321) undesirable.
- `channels_as_legend` already exists for `render_images`; can extend.
Scope
- In scope: 2–3-column color lists on `render_shapes`, `render_labels`, `render_points`. Per-channel `cmap` or `palette`. Additive blend on a single axis.
- Out of scope (defer or decline):
- Multi-panel grids (`sc.pl.umap`-style) — already declined in #321.
- Arbitrary blend modes beyond additive — start with the existing `render_images` semantics.
- PCA-based or learned color reductions for >3 channels.
Edge cases to design for
- Length mismatch between `color` list and `palette` list.
- Mixing categorical and continuous columns in the same `color` list (probably reject with a clear error).
- NaN handling per column (skip vs zero vs error — likely error, matching the recent `render_images` NaN rejection).
- Per-column `norm` / `vmin` / `vmax` — accept a list, parallel to the per-channel `norm` already supported on `render_images`.
- `groups` semantics when `color` is a list — likely ignored with a warning.
Relation to #321
#321 conflates two asks under "plotting multiple genes":
- Multi-panel grid for several genes — declined; possibly hosted in a future Squidpy 2.0 wrapper.
- Single-axis additive blend for co-expression visualization — this issue.
The documentation sub-tasks in #321 (`save`, `ncols` on `pl.show()`) are already resolved.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in pl/render.py by reading the existing render_images multi-channel branches 2A/2B/2C, then trace render_shapes, render_labels, and render_points. Done means supporting 2–3 continuous columns as per-channel colors on one axis with additive blending, while defining the listed palette-length, mixed-type, NaN, normalization, and groups behaviors.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100