scPCA - A probabilistic factor model for single-cell data
scPCA is a versatile matrix factorisation framework designed to analyze single-cell data across diverse experimental designs.
scPCA is a young project and breaking changes are to be expected.
scPCA in a nutshell
scPCA enables the analysis of single-cell RNA-seq data across condtions. In simple words, it enables the incorporation of a design (model) matrix that encodes the experimental design of the dataset and infers how the gene loading weight vectors change from a specified reference condition to the treated condtion.
https://github.com/user-attachments/assets/182af56e-14e0-4357-ab31-1b392dd45d18
Quick install
scPCA makes use torch, pyro and anndata. We highly recommend to run scPCA on a GPU device.
Via Pypi
The easiest option to install scpca is via Pypi. Simply type
$ pip install scpca
into your shell and hit enter.
- Free software: MIT license
- Documentation: https://sagar87.github.io/scPCA/index.html
Credits
- Harald Vöhringer
1 maintainer
Metadata
1 maintainer
Release files for scpca 0.3.3
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| scpca-0.3.3.tar.gz | 32.9 kB | Without provenance | Details for scpca-0.3.3.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| scpca-0.3.3-py3-none-any.whl | Python 3 | none | any | Details for scpca-0.3.3-py3-none-any.whl |
Total release size: 75.1 kB
Release files / scpca-0.3.3.tar.gz
| Download URL | scpca-0.3.3.tar.gz |
|---|---|
| Size | 32.9 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / scpca-0.3.3-py3-none-any.whl
| Download URL | scpca-0.3.3-py3-none-any.whl |
|---|---|
| Size | 42.2 kB |
| Tags | Python 3 |
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Security for scpca 0.3.3
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