[Question] Dealing with Single Cell expression data
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Description
Hello,
I was considering testing UMAP on single cell gene expression.
I got 3 questions:
1. How should I load the data. features (aka genes) as rows and cells as columns or the reverse?
The dataset I'm trying it on is composed of 43773 genes and 1194 cells.
2. Is UMAP able to deal with zero inflated data because single cell RNAseq has a lot.
3. Should I consider log transforming the data prior to running UMAP or not?
Thanks for the help
[EDIT]: Question 1 has been answered while testing.
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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 by reading issue #67 and its seven-comment thread, then review the project's UMAP usage guidance. Address the remaining questions about zero-inflated single-cell expression data and log transformation for the stated 43,773-gene, 1,194-cell dataset. Done means providing a clear, documented answer to those questions.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100