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 expres

SUBMITTER: Wan C 

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

REPOSITORIES: biostudies-literature