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Nucleic Acids Res. 2019 Aug 2. pii: gkz655. doi: 10.1093/nar/gkz655. [Epub ahead of print]

LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.

Wan C1,2,3, Chang W1,2,3, Zhang Y1,4, Shah F5, Lu X1, Zang Y6, Zhang A7, Cao S1,6, Fishel ML5,8, Ma Q9, Zhang C1,3.

Author information

1
Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.
2
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907, USA.
3
Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN 46202, USA.
4
Colleges of Computer Science and Technology, Jilin University, Changchun 130012, China.
5
Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.
6
Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA.
7
Department of Statistics, University of Wisconsin-Madison, Madison, WI 53706, USA.
8
Department of Pharmacology and Toxicology, Indiana University, School of Medicine, Indianapolis, IN,46202, USA.
9
Department of Biomedical Informatics, the Ohio State University, Columbus, OH 43210, USA.

Abstract

A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory inputs across individual cells, which is further complicated by largely observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model, from the kinetic relationships of the transcriptional regulatory inputs, mRNA metabolism and abundance in single cells. LTMG infers the expression multi-modalities across single cells, meanwhile, the dropouts and low expressions are treated as left truncated. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of scRNA-seq data, comparing to three other state-of-the-art models. Our biological assumption of the low non-zero expressions, rationality of the multimodality setting, and the capability of LTMG in extracting expression states specific to cell types or functions, are validated on independent experimental data sets. A differential gene expression test and a co-regulation module identification method are further developed. We experimentally validated that our differential expression test has higher sensitivity and specificity, compared with other five popular methods. The co-regulation analysis is capable of retrieving gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

PMID:
31372654
DOI:
10.1093/nar/gkz655

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