title: Learning regulatory programs by threshold SVD regression.
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 (lncRNA) 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.
authors.affiliation: Departments of Statistics and.
authors.forename: Xin
authors.initials: X
authors.lastname: Ma
authors.affiliation: Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205.
authors.forename: Luo
authors.initials: L
authors.lastname: Xiao
authors.affiliation: Departments of Statistics and Health Research & Policy, Stanford University, Stanford, CA 94305; and whwong@stanford.edu.
authors.forename: Wing Hung
authors.initials: WH
authors.lastname: Wong
mesh_terms: D003198:Computer Simulation; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D035683:MicroRNAs / Q000096:biosynthesis* / Q000235:genetics; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D012334:RNA, Neoplasm / Q000096:biosynthesis* / Q000235:genetics
chemical_list: D035683:MicroRNAs; D012334:RNA, Neoplasm
keywords: SVD; multivariate; regression; regulatory program; sparse