منابع مشابه
Characterizations of Multivariate Normal-Poisson Model
‎Multivariate normal-Poisson model has been recently introduced as a special case of normal stable Tweedie models‎. ‎The model is composed of a univariate Poisson variable‎, ‎and the remaining variables given the Poisson one are independent Gaussian variables with variance the value of the Poisson component‎. ‎Two characterizations of this model are shown‎, ‎...
متن کاملA Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data
Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, G...
متن کاملSparse Estimation of Multivariate Poisson Log-Normal Models from Count Data
Modeling data with multivariate count responses is a challenging problem due to the discrete nature of the responses. Existing methods for univariate count response cannot be easily extended to the multivariate case since the dependency among multiple responses needs to be properly accommodated. In this paper, we propose a multivariate Poisson log-normal regression model for multivariate count ...
متن کاملA Multivariate Poisson-Log Normal Mixture Model for Clustering Transcriptome Sequencing Data
High-dimensional data of discrete and skewed nature is commonly encountered in high-throughput sequencing studies. Analyzing the network itself or the interplay between genes in this type of data continues to present many challenges. As data visualization techniques become cumbersome for higher dimensions and unconvincing when there is no clear separation between homogeneous subgroups within th...
متن کاملA Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data
Gene network inference from transcriptomic data is an important methodological challenge and a key aspect of systems biology. Although several methods have been proposed to infer networks from microarray data, there is a need for inference methods able to model RNA-seq data, which are count-based and highly variable. In this work we propose a hierarchical Poisson log-normal model with a Lasso p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Risks
سال: 2018
ISSN: 2227-9091
DOI: 10.3390/risks6030070