scverse / scverse/scanpy

Store multiple Umap Coordinates

Open
#2,245 2 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Dominant language
Python
Stars
2.6k
Forks
779
Avg merge
1d 4h
Merged PRs (30d)
27

Description

Set name for storing Umap coordinates explicitly in tl.umap and pl.umap
tl.umap(..., x_umap = "X_umap")
pl.umap(..., x_umap = "X_umap")

Sometimes it would be helpful to specify the adata obsm field for storing the umap coordinates.
For example :
-if I want to compute and plot the umap for the raw data and afterwards for the integrated or in any way modified data. The first x_umap is going to be overwritten and needs to be computed again, if I need to plot the first step again.
So it would be cool to enable a workflow like the following:

tl.umap(adata, ..., x_umap = "X_umap_raw")
# do some operations ...
tl.umap(adata, ..., x_umap = "X_umap_mod)

# now after computation I might need to take a look on both umaps again, or plot them in direct comparison
pl.umap(adata, ..., x_umap = "X_umap_raw")
pl.umap(adata, ..., x_umap = "X_umap_mod")

I hope I was able to explain what I mean and did not oversee such feature or misunderstood the usage.
All the best
maflot

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start by reading the implementations and existing storage behavior of tl.umap and pl.umap. Trace how UMAP coordinates are written to and retrieved from adata.obsm, then determine how named coordinate sets should be computed and plotted without overwriting one another. Done means both entry points support the requested x_umap names and the raw and modified examples can be compared.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
data-visualization, machine-learning
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.