davisidarta/topometry
Systematically learn and evaluate the latent geometry from high-dimensional data, with a focus on scRNAseq analysis
This tool helps single-cell researchers understand the true shape and relationships within their high-dimensional genomics data. It takes your single-cell gene expression data (like scRNAseq) and produces reliable, low-dimensional maps and cell cluster assignments, along with metrics to confirm the accuracy of these visualizations. Biologists and bioinformaticians working with complex single-cell omics data will find this useful.
105 stars. Available on PyPI.
Use this if you need to visualize and cluster large, diverse single-cell datasets, ensuring that the underlying biological geometry is accurately preserved, rather than just reducing variance.
Not ideal if your sample sizes are very small, or if you require real-time updates or the ability to map new data points without re-analyzing the entire dataset.
Stars
105
Forks
5
Language
Python
License
MIT
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
Dependencies
7
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