Clustering gene expression time series data using an infinite Gaussian process mixture model
نویسندگان
چکیده
منابع مشابه
Clustering gene expression time series data using an infinite Gaussian process mixture model
Transcriptome-wide time series expression profiling is used to characterize the cellular response to environmental perturbations. The first step to analyzing transcriptional response data is often to cluster genes with similar responses. Here, we present a nonparametric model-based method, Dirichlet process Gaussian process mixture model (DPGP), which jointly models data clusters with a Dirichl...
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MOTIVATION The biologic significance of results obtained through cluster analyses of gene expression data generated in microarray experiments have been demonstrated in many studies. In this article we focus on the development of a clustering procedure based on the concept of Bayesian model-averaging and a precise statistical model of expression data. RESULTS We developed a clustering procedur...
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Efficiently and effectively finding the genes with similar behaviors from microarray data is an important task in bioinformatics community. Co-expression genes have the same behavior or are controlled by the same regulatory mechanisms. Clustering analysis is a very popular technique to group the co-expressed genes into the same cluster. One of the key issues for clustering gene expression time ...
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MOTIVATION Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. On account of the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clu...
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ژورنال
عنوان ژورنال: PLOS Computational Biology
سال: 2018
ISSN: 1553-7358
DOI: 10.1371/journal.pcbi.1005896