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bioRxiv · 10.1101/380071

Tensor decomposition--based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter methylation of genes in ovarian cancer

Taguchi, Y.-h.·Ng, K.-L.
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Abstract

Integrated analysis of epigenetic profiles is important but difficult. Tensor decomposition-based unsupervised feature extraction was applied here to data on microRNA (miRNA) expression and promoter methylation of genes in ovarian cancer. It selected seven miRNAs and 241 genes by expression levels and promoter methylation degrees, respectively, such that they showed differences between eight normal ovarian tissue samples and 569 tumor samples. The expression levels of the seven miRNAs and the degrees of promoter methylation of the 241 genes also correlated significantly. Conventional Students t test-based feature selection failed to identify miRNAs and genes that have the above properties. On the other hand, biological evaluation of the seven identified miRNAs and 241 identified genes suggests that they are strongly related to cancer as expected.

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Taguchi, Y.-h., Ng, K.-L.. 2018-07-30. Tensor decomposition--based unsupervised feature extraction for integrated analysis of TCGA data on microRNA expression and promoter methylation of genes in ovarian cancer. https://doi.org/10.1101/380071

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