A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

Automated summary

The study proposes a zero-inflated non-negative matrix factorization method for count data to address the phenomenon of excessive zeros, achieving improved accuracy and the smallest bias in simulations. The method is demonstrated to have superior performance in analyzing brain gene expression and fecal microbiome datasets.