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4 changes: 2 additions & 2 deletions README.md
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cNMF is a pipeline for inferring gene expression programs from scRNA-Seq. It takes a count matrix (N cells X G genes) as input and produces a (K x G) matrix of gene expression programs (GEPs) and a (N x K) matrix specifying the usage of each program for each cell in the data. Read more about the method in the [publication](https://elifesciences.org/articles/43803) and check out examples on [simulated data](Tutorials/analyze_simulated_example_data.ipynb) and [PBMCs](Tutorials/analyze_pbmc_example_data.ipynb).

We have also created a tutorial for running cNMF from R. See the [Rmd notebook](Tutorials/R_vignette.Rmd) or the [compiled html](Tutorials/R_vignette.nb.html) for this.
We have also created a tutorial for running cNMF in R. See the [Rmd notebook](Tutorials/R_vignette.Rmd) or the [compiled html](Tutorials/R_vignette.nb.html) for this.

# Installation
cNMF has been tested with Python 3.7 and 3.10 and requires scikit-learn>=1.0, scanpy>=1.8, and AnnData>=0.9
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# Change log

### New in version 1.7
- Use scipy hierachical clsutering grather than fastcluster for compatibility with numpy>2.0
- Use scipy hierarchical clustering rather than fastcluster for compatibility with numpy>2.0
- More efficient sparse + batched OLS computation uses significantly less memory
- Implemented basic testing suite

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