PAGA connectivity score
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- Python
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Description
Hi
I am recently transformed Seurat object to scanpy and use it for further pseudotime analysis (PAGA) and it performs really well.
But I have three question here:
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I am wondering if anybody here knows how to make PAGA connectivity score heatmap (ref: Popescu et al, 2019, Nature) which shows connections strength between partitions. I've tried dendrogram in scanpy (pl.coorelation.matrix) but we'd like to try more.
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And also if anyone knows if we could perform differential expression on the partitions by PAGA to find the marker gene along the potential path?
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PAGA generated a pie chart in every partition But does anyone know whether I could acquire the real percentage of the pie representing different Seurat cluster I input?
Thanks in advance for everyone's help!
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Research direction
Start by reviewing the PAGA documentation and existing plotting and differential-expression entry points in Scanpy, including the dendrogram correlation-matrix functionality mentioned in the issue. The requested outcomes are a connectivity-score heatmap, partition-based differential expression or marker discovery, and percentages for the partition pie charts, but the issue does not identify files, tests, or a single acceptance criterion.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- bioinformatics, data-visualization
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 15/100