Feature-based clustering of stomach cancer gene expression data

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2018-05

Authors

Bramble, Matthew David

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Abstract

This report presents the results of using a probabilistic clustering technique in the analysis of microRNAseq and RNAseq data from gastric cancer tumor samples deposited at TCGA (The Cancer Genome Atlas). Using the method of Hoff, who has proposed a Dirichlet process unsupervised clustering framework with feature selection, it is possible to reveal interesting structure in gastric cancer gene expression data that relates to Epstein-Barr virus (EBV) microRNA levels. This structure is not as readily identified by a typical hierarchical clustering method, and the results of this analysis contribute to an understanding of the role of EBV viral microRNAs in gastric cancer tumors.

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