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PreprintCited by 45Single-cell and spatial transcriptomicsGene expression and cancer classificationGene Regulatory Network Analysis

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

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Wan, Changlin, Chang, Wennan, Zhang, Yu, Shah, Fenil, Lu, Xiaoyu, Zang, Yong, Zhang, Anru, Cao, Sha, Fishel, Melissa L., Ma, Qin, Zhang, Chi·2018·Nucleic Acids Research
DOI 10.1101/430009PMC PMC6765121

Preprint — published as 10.1093/nar/gkz655

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Abstract

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the 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 .

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Cites

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    “To validate our hypothesis, we collected a data set of experimentally measured mRNA kinetics of mouse fibroblast cells 15 and tested the correlations between the mRNA half-life and the LTMG fitted parameters of each gene, in three scRNA-seq data set (GSE99235, GSE98816 and GSE87038) of mouse…”
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Metadata

Authors
Wan, C., Chang, W., Zhang, Y., Shah, F., Lu, X., Zang, Y., Zhang, A., Cao, S., Fishel, M. L., Ma, Q., & Zhang, C.
Affiliations
  • Purdue University West Lafayette (US)
  • Jilin University (CN)
  • Indiana University Indianapolis (US)
  • University of Wisconsin–Madison (US)
  • The Ohio State University (US)
  • Purdue University in Indianapolis (US)
Corresponding author
  • Fishel, Melissa L.
  • Ma, Qin
  • Zhang, Chi
ORCID
Venue
Nucleic Acids Research
Publisher
openRxiv
Published
2018-09-29
Volume
47
Issue
18
Pages
e111-e111
ISSN
0305-1048 (ISSN-L)
DOI
10.1101/430009
PMC
PMC6765121
OpenAlex
W2965118801
Cited by
45
References
1
Citations by year
2026: 1 · 2025: 1 · 2024: 4 · 2023: 10 · 2022: 5 · 2021: 11 · 2020: 10 · 2019: 2
FWCI
2.32
Type
journal article
Language
en
Open access
Licence
cc-by-nc
Locations
  • Nucleic Acids Research (publishedVersion, cc-by-nc)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • IUScholarWorks (Indiana University) (submittedVersion, cc-by-nc)
  • PMC
Funders
  • Indiana Clinical and Translational Sciences Institute
  • University of Georgia
  • Tianjin University
  • National Institutes of Health
  • School of Medicine, Indiana University
  • National Cancer Institute (R01CA167291)
  • National Institute of General Medical Sciences (R01 award 1R01GM131399-01, R01GM131399)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
single-cell RNA sequencing, dropout, transcriptional regulation, mRNA metabolism, differential gene expression
MeSH terms
Algorithms, Gene Expression Regulation, RNA, Software, Models, Statistical, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing

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