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• W2892476522 abstract "A key challenge in statistical modeling of single cell RNA-seq (scRNA-seq) data is to assess the multimodality of single gene expression with a simultaneous handling of the largely observed zero and low expressions. In this study, started from a mathematical derivation of the relationship between the mRNA abundance, transcriptional regulatory signals, and mRNA metabolism on a single cell level, we developed a left truncated mixture Gaussian (LTMG) distribution to accurately infer the modality and distribution of individual gene expression profile in a scRNA-seq data, with modeling the dropout and low expressions as left censored data caused by a limited experimental resolution. We validated the LTMG model is with a much better goodness of fitting on an extensive data set, comparing to the statistical models commonly utilized in other scRNA-seq analysis methods. An LTMG based differential gene expression testing method, namely LTMG-DEG, is further developed. We experimentally validated a higher sensitivity and specificity of the LTMG-DEG comparing other differential gene expression testing methods. In addition, the assumption of the LTMG model, derived from our system biological derivation, is validated on several independent experimental data sets. A user friendly R package of LTMG and its downstream analysis is provided." @default.
• W2892476522 created "2018-10-05" @default.
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• W2892476522 date "2018-09-29" @default.
• W2892476522 modified "2023-10-16" @default.
• W2892476522 title "LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism" @default.
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• W2892476522 hasPublicationYear "2018" @default.
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