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

Kong Yixin, Kozik Ariangela, Nakatsu Cindy H., Jones-Hall Yava L. and Chun Hyonho ()
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Kong Yixin: Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
Kozik Ariangela: Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48104, USA
Nakatsu Cindy H.: Department of Agronomy, Purdue University, West Lafayette, IN 47905, USA
Jones-Hall Yava L.: College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, Texas 77843, USA
Chun Hyonho: Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea

The International Journal of Biostatistics, 2022, vol. 18, issue 1, 203-218

Abstract: 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.

Keywords: deconvolution; latent factor model; non-negative matrix factorization; zero-inflation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/ijb-2020-0039

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