scverse / scverse/spatialdata-plot
Additive RGB color blending for render_shapes / render_labels (and render_points)
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- Langage dominant
- Python
- Étoiles
- 86
- Forks
- 21
- Merge moyen
- 14 h 50 min
- PR mergées (30 j)
- 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.
Guide de contribution
Ouvrir le guide de contribution
Par où commencer
- Lisez l'issue en entier, puis le guide de contribution du projet.
- Signalez en commentaire que vous la prenez — cela évite que deux personnes fassent le même travail.
- Forkez le dépôt et travaillez sur une branche.
- Ouvrez une pull request qui référence le numéro de l'issue.
Piste de recherche
Commencez dans pl/render.py en lisant les branches multicanal existantes de render_images 2A/2B/2C, puis suivez render_shapes, render_labels et render_points. Le travail est terminé lorsque 2–3 colonnes continues sont prises en charge comme couleurs par canal sur un axe avec un mélange additif, tout en définissant les comportements associés à la longueur de palette, aux types mixtes, à NaN, à la normalisation et à groups indiqués.
Rédigé par le modèle d'indexation à partir du texte de l'issue.
Évaluation
- Stack technique
- python
- Domaine
- data-visualization
- Type d'issue
- Fonctionnalité
- Difficulté
- 5/5
- Temps estimé
- Plus d'une semaine
- Activité
- Calme
- Clarté
- Plutôt claire
- Accessibilité débutants
- 45/100