Manifold preserving marker selection for single-cell data
Project description
SCMER - Manifold Preserving Feature Selection
SCMER is a feature selection methods designed for single-cell data analysis. It selects a compact sets of markers that preserve the manifold in the original data. It can also be used for data integration by using features in one modality to match the manifold of another modality.
Tutorials
Tutorials are available at https://scmer.readthedocs.io/en/latest/examples.html
You may start with the Melanoma data (Tiorsh et al.).
Full Documentation
Detailed documentation is available at https://scmer.readthedocs.io/en/latest/
The mechanism and capabilities of SCMER is detailed in our pre-print Single-Cell Manifold Preserving Feature Selection (SCMER)
Publication
Single-cell manifold-preserving feature selection for detecting rare cell populations Nature Computational Science (2021)
- Paid access: https://www.nature.com/articles/s43588-021-00070-7
- Free access (no download): https://rdcu.be/ckZGT
- BioRxiv preprint: https://www.biorxiv.org/content/10.1101/2020.12.01.407262v1.full
Contact
I do monitor the "Issues" and aim to clear any issues in a few weeks. If you have an urgent request, please email liang.shaoheng@rice.edu.
Project details
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