LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
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 truncat...
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| Published in | Nucleic acids research Vol. 47; no. 18; p. e111 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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Oxford University Press
10.10.2019
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| Abstract | 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. |
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| AbstractList | 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
. 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. 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. 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.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. |
| Author | Wan, Changlin Fishel, Melissa L Zhang, Anru Zhang, Yu Zang, Yong Lu, Xiaoyu Cao, Sha Shah, Fenil Zhang, Chi Chang, Wennan Ma, Qin |
| AuthorAffiliation | 2 Department of Electrical and Computer Engineering, Purdue University , West Lafayette, IN 47907, USA 4 Colleges of Computer Science and Technology, Jilin University , Changchun 130012, China 3 Department of Electrical and Computer Engineering, Purdue University , Indianapolis, IN 46202, USA 8 Department of Pharmacology and Toxicology, Indiana University, School of Medicine , Indianapolis, IN,46202, USA 5 Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University, School of Medicine , Indianapolis, IN 46202, USA 7 Department of Statistics, University of Wisconsin–Madison , Madison, WI 53706, USA 9 Department of Biomedical Informatics, the Ohio State University , Columbus, OH 43210, USA 1 Department of Medical and Molecular Genetics, Indiana University, School of Medicine , Indianapolis, IN 46202, USA 6 Department of Biostatistics, Indiana University, School of Medicine , Indianapolis, IN 46202, USA |
| AuthorAffiliation_xml | – name: 2 Department of Electrical and Computer Engineering, Purdue University , West Lafayette, IN 47907, USA – name: 9 Department of Biomedical Informatics, the Ohio State University , Columbus, OH 43210, USA – name: 3 Department of Electrical and Computer Engineering, Purdue University , Indianapolis, IN 46202, USA – name: 4 Colleges of Computer Science and Technology, Jilin University , Changchun 130012, China – name: 7 Department of Statistics, University of Wisconsin–Madison , Madison, WI 53706, USA – name: 6 Department of Biostatistics, Indiana University, School of Medicine , Indianapolis, IN 46202, USA – name: 1 Department of Medical and Molecular Genetics, Indiana University, School of Medicine , Indianapolis, IN 46202, USA – name: 5 Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University, School of Medicine , Indianapolis, IN 46202, USA – name: 8 Department of Pharmacology and Toxicology, Indiana University, School of Medicine , Indianapolis, IN,46202, USA |
| Author_xml | – sequence: 1 givenname: Changlin orcidid: 0000-0002-6106-7175 surname: Wan fullname: Wan, Changlin organization: Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 2 givenname: Wennan surname: Chang fullname: Chang, Wennan organization: Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 3 givenname: Yu surname: Zhang fullname: Zhang, Yu email: czhang87@iu.edu organization: Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 4 givenname: Fenil surname: Shah fullname: Shah, Fenil organization: Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 5 givenname: Xiaoyu surname: Lu fullname: Lu, Xiaoyu organization: Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 6 givenname: Yong surname: Zang fullname: Zang, Yong organization: Department of Biostatistics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 7 givenname: Anru surname: Zhang fullname: Zhang, Anru email: czhang87@iu.edu organization: Department of Statistics, University of Wisconsin–Madison, Madison, WI 53706, USA – sequence: 8 givenname: Sha surname: Cao fullname: Cao, Sha organization: Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 9 givenname: Melissa L surname: Fishel fullname: Fishel, Melissa L email: mfishel@iu.edu organization: Department of Pediatrics and Herman B Wells Center for Pediatric Research, Indiana University, School of Medicine, Indianapolis, IN 46202, USA – sequence: 10 givenname: Qin surname: Ma fullname: Ma, Qin email: qin.ma@osumc.edu organization: Department of Biomedical Informatics, the Ohio State University, Columbus, OH 43210, USA – sequence: 11 givenname: Chi surname: Zhang fullname: Zhang, Chi email: czhang87@iu.edu organization: Department of Medical and Molecular Genetics, Indiana University, School of Medicine, Indianapolis, IN 46202, USA |
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A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional... A key challenge in modeling single-cell RNA-seq data is to capture the diversity of gene expression states regulated by different transcriptional regulatory... |
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| SubjectTerms | Algorithms Gene Expression Profiling Gene Expression Regulation - genetics High-Throughput Nucleotide Sequencing - methods Methods Online Models, Statistical RNA - genetics Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software |
| Title | LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data |
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