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Journal Article

Learning regulatory programs by threshold SVD regression

Xin Ma, Luo Xiao and Wing Hung Wong
Proceedings of the National Academy of Sciences of the United States of America
Vol. 111, No. 44 (November 4, 2014), pp. 15675-15680
Stable URL: https://www.jstor.org/stable/43189808
Page Count: 6
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Abstract

We formulate a statistical model for the regulation of global gene expression by multiple regulatory programs and propose a thresholding singular value decomposition (T-SVD) regression method for learning such a model from data. Extensive simulations demonstrate that this method offers improved computational speed and higher sensitivity and specificity over competing approaches. The method is used to analyze microRNA (miRNA) and long noncoding RNA (IncRNA) data from The Cancer Genome Atlas (TCGA) consortium. The analysis yields previously unidentified insights into the combinatorial regulation of gene expression by noncoding RNAs, as well as findings that are supported by evidence from the literature.

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