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

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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.
Publisher
WALTER DE GRUYTER GMBH
Issue Date
2022-06
Language
English
Article Type
Article
Citation

INTERNATIONAL JOURNAL OF BIOSTATISTICS, v.18, no.1, pp.203 - 218

ISSN
2194-573X
DOI
10.1515/ijb-2020-0039
URI
http://hdl.handle.net/10203/296948
Appears in Collection
MA-Journal Papers(저널논문)
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