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Subject Level Clustering Using A Negative Binomial Model For Small Transcriptomic Studies., Qian Li, Janelle R. Noel-Macdonnell, Devin C. Koestler, Ellen L. Goode, Brooke L. Fridley
Subject Level Clustering Using A Negative Binomial Model For Small Transcriptomic Studies., Qian Li, Janelle R. Noel-Macdonnell, Devin C. Koestler, Ellen L. Goode, Brooke L. Fridley
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BACKGROUND: Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput 'omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. Nāā„ā500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.
RESULTS: We have developed a Negative ā¦