Yue Li1, Cheng Liang2, Ka-Chun Wong1, Jiawei Luo2, Zhaolei Zhang3. 1. Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada. 2. Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada. 3. Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada Department of Computer Science, The Donnelly Centre, University of Toronto, Toronto, Ontario M5S 3E1, Canada, College of Information Science and Engineering, Hunan University, Changsha, Hunan, 410082, China and Banting and Best Department of Medical Research and Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 3E1, Canada.
Abstract
MOTIVATION: Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules. RESULTS: We propose Mirsynergy, an efficient deterministic overlapping clustering algorithm adapted from a recently developed framework. Mirsynergy operates in two stages: it first forms MiRMs based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions. Using expression data for ovarian, breast and thyroid cancer from The Cancer Genome Atlas, we compared Mirsynergy with internal controls and existing methods. Mirsynergy-MiRMs exhibit significantly higher functional enrichment and more coherent miRNA-mRNA expression anti-correlation. Based on Kaplan-Meier survival analysis, we proposed several prognostically promising MiRMs and envisioned their utility in cancer research. AVAILABILITY AND IMPLEMENTATION: Mirsynergy is implemented/available as an R/Bioconductor package at www.cs.utoronto.ca/∼yueli/Mirsynergy.html.
MOTIVATION: Identification of microRNA regulatory modules (MiRMs) will aid deciphering aberrant transcriptional regulatory network in cancer but is computationally challenging. Existing methods are stochastic or require a fixed number of regulatory modules. RESULTS: We propose Mirsynergy, an efficient deterministic overlapping clustering algorithm adapted from a recently developed framework. Mirsynergy operates in two stages: it first forms MiRMs based on co-occurring microRNA (miRNA) targets and then expands each MiRM by greedily including (excluding) mRNAs into (from) the MiRM to maximize the synergy score, which is a function of miRNA-mRNA and gene-gene interactions. Using expression data for ovarian, breast and thyroid cancer from The Cancer Genome Atlas, we compared Mirsynergy with internal controls and existing methods. Mirsynergy-MiRMs exhibit significantly higher functional enrichment and more coherent miRNA-mRNA expression anti-correlation. Based on Kaplan-Meier survival analysis, we proposed several prognostically promising MiRMs and envisioned their utility in cancer research. AVAILABILITY AND IMPLEMENTATION: Mirsynergy is implemented/available as an R/Bioconductor package at www.cs.utoronto.ca/∼yueli/Mirsynergy.html.
Authors: Suying Bao; Lilong Jia; Xueya Zhou; Zhi-Gang Zhang; Hazel Wai Lan Wu; Zhe Yu; Gordon Ng; Yanhui Fan; Dana S M Wong; Shishu Huang; Kelvin Kai Wang To; Kwok-Yung Yuen; Man Lung Yeung; You-Qiang Song Journal: Funct Integr Genomics Date: 2018-03-21 Impact factor: 3.410