GeneNMF: unsupervised discovery of gene programs in single-cell data

Non-negative matrix factorization is a method for the analysis of high dimensional data that allows extracting sparse and meaningful features from a set of non-negative data vectors. It is well suited for decomposing scRNA-seq data, effectively reducing large complex matrices (\(10^4\) of genes times \(10^5\) of cells) into a few interpretable gene programs. It has been especially used to extract recurrent gene programs in cancer cells (see e.g. Barkely et al. (2022) and Gavish et al. (2023)), which are otherwise difficult to integrate and analyse jointly.

GeneNMF is a package that implements methods for NMF decomposition for single-cell omics data. It can be applied directly on Seurat objects to reduce the dimensionality of the data and to detect robust gene programs across multiple samples.



Test your installation

sampleObj <- runNMF(sampleObj, k=5)

GeneNMF demo

Find a demo of the functionalities of GeneNMF in the following tutorial: HTML and repository.


Coming soon!