The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

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.

Installation

library(remotes)
remotes::install_github("carmonalab/GeneNMF")

Test your installation

library(GeneNMF)
data(sampleObj)
sampleObj <- runNMF(sampleObj, k=5)

GeneNMF demo

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

References

Coming soon!

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.