Dataset Information

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 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.

SUBMITTER: Wan C

PROVIDER: S-EPMC6765121 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Wan Changlin C Chang Wennan W Zhang Yu Y Shah Fenil F Lu Xiaoyu X Zang Yong Y Zhang Anru A Cao Sha S Fishel Melissa L ML Ma Qin Q Zhang Chi C

Nucleic acids research 20191001 18

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 sing ...[more]

PMID: 31372654

Similar Datasets

Project description:BackgroundSingle-cell RNA Sequencing is gaining popularity in recent years. Compared to bulk RNA-Seq, single-cell RNA Sequencing allows the gene expression being measured within individual cells instead of mean gene expression levels across all cells in the sample. Thus, cell-to-cell variation of gene expressions could be examined. Gene differential expression analysis remains the major purpose in most single-cell RNA sequencing experiments and many methods have been developed in recent years to conduct gene differential expression analysis for single-cell RNA sequencing data.ResultsThrough simulation studies and real data examples, we evaluated the performance of five open-source popular methods used for gene differential expression analysis in single-cell RNA sequencing data. The five methods included DEsingle (Zero-inflated negative binomial model), Linnorm (Empirical Bayes method on transformed count data using the limma package), monocle (An approximate Chi-Square likelihood ratio test), MAST (A generalized linear hurdle model), and DESeq2 (A generalized linear model with empirical Bayes approach and also commonly used for bulk RNA sequencing differential express analyses). We assessed the false discovery rate (FDR) control, sensitivity, specificity, accuracy, and area under the receiver operating characteristics (AUROC) curve for all five methods under different sample sizes, distribution assumptions, and proportions of zeros in the data.ConclusionsWe found the MAST method performed the best among the five methods compared with the largest AUROC values across all tested sample sizes and different proportion of truly differential expressed genes, when the data followed negative binomial distributions. When the sample size increased to 100 in each group, the MAST method showed the best performance with the highest AUROC regardless of the data distributions. If the excess zeros were first filtered out before the gene differential analyses, the DESingle, Linnorm, and DESeq2 performed relatively better than the MAST and the monocle methods with higher AUROC values.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets