Browsing by Subject "Models, Statistical"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data
    (Oxford University Press, 2019-10-10) Wan, Changlin; Chang, Wennan; Zhang, Yu; Shah, Fenil; Lu, Xiaoyu; Zang, Yong; Zhang, Anru; Cao, Sha; Fishel, Melissa L.; Ma, Qin; Zhang, Chi; Medical and Molecular Genetics, School of Medicine
    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.
  • Thumbnail Image
    Item
    A semiparametric recurrent events model with time-varying coefficients
    (Wiley Blackwell (John Wiley & Sons), 2013-03-15) Yu, Zhangsheng; Liu, Lei; Bravata, Dawn M.; Williams, Linda S.; Tepper, Robert S.; Department of Biostatistics, Richard M. Fairbanks School of Public Health
    We consider a recurrent events model with time-varying coefficients motivated by two clinical applications. We use a random effects (Gaussian frailty) model to describe the intensity of recurrent events. The model can accommodate both time-varying and time-constant coefficients. We use the penalized spline method to estimate the time-varying coefficients. We use Laplace approximation to evaluate the penalized likelihood without a closed form. We estimate the smoothing parameters in a similar way to variance components. We conduct simulations to evaluate the performance of the estimates for both time-varying and time-independent coefficients. We apply this method to analyze two data sets: a stroke study and a child wheeze study.