Qiong Wu1, Tianzhou Ma2, Qingzhi Liu3, Donald K Milton2, Yuan Zhang4, Shuo Chen5. 1. Department of Mathematics, University of Maryland, College Park, MD. 2. School of Public Health, University of Maryland, College Park, MD. 3. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI. 4. Department of Statistics, Ohio State University, Columbus, OH. 5. Division of Biostatistics and Bioinformatics, School of Medicine, University of Maryland, Baltimore, MD.
Abstract
MOTIVATION: The analysis of gene co-expression network (GCN) is critical in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. Numerous clustering methods have been developed to detect communities of co-expressed genes in the large network. The assumed independent community structure, however, can be oversimplified and may not adequately characterize the complex biological processes. RESULTS: We develop a new computational package to extract interconnected communities from gene co-expression network. We consider a pair of communities be interconnected if a subset of genes from one community is correlated with a subset of genes from another community. The interconnected community structure is more flexible and provides a better fit to the empirical co-expression matrix. To overcome the computational challenges, we develop efficient algorithms by leveraging advanced graph norm shrinkage approach. We validate and show the advantage of our method by extensive simulation studies. We then apply our interconnected community detection method to an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study and identify essential interacting biological pathways related to the immune evasion mechanism of tumor cells. AVAILABILITY: The software is available at Github: https://github.com/qwu1221/ICN and Figshare: https://figshare.com/articles/software/ICN-package/13229093. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: The analysis of gene co-expression network (GCN) is critical in examining the gene-gene interactions and learning the underlying complex yet highly organized gene regulatory mechanisms. Numerous clustering methods have been developed to detect communities of co-expressed genes in the large network. The assumed independent community structure, however, can be oversimplified and may not adequately characterize the complex biological processes. RESULTS: We develop a new computational package to extract interconnected communities from gene co-expression network. We consider a pair of communities be interconnected if a subset of genes from one community is correlated with a subset of genes from another community. The interconnected community structure is more flexible and provides a better fit to the empirical co-expression matrix. To overcome the computational challenges, we develop efficient algorithms by leveraging advanced graph norm shrinkage approach. We validate and show the advantage of our method by extensive simulation studies. We then apply our interconnected community detection method to an RNA-seq data from The Cancer Genome Atlas (TCGA) Acute Myeloid Leukemia (AML) study and identify essential interacting biological pathways related to the immune evasion mechanism of tumor cells. AVAILABILITY: The software is available at Github: https://github.com/qwu1221/ICN and Figshare: https://figshare.com/articles/software/ICN-package/13229093. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: M Milella; S M Kornblau; Z Estrov; B Z Carter; H Lapillonne; D Harris; M Konopleva; S Zhao; E Estey; M Andreeff Journal: J Clin Invest Date: 2001-09 Impact factor: 14.808
Authors: Timothy J Ley; Christopher Miller; Li Ding; Benjamin J Raphael; Andrew J Mungall; A Gordon Robertson; Katherine Hoadley; Timothy J Triche; Peter W Laird; Jack D Baty; Lucinda L Fulton; Robert Fulton; Sharon E Heath; Joelle Kalicki-Veizer; Cyriac Kandoth; Jeffery M Klco; Daniel C Koboldt; Krishna-Latha Kanchi; Shashikant Kulkarni; Tamara L Lamprecht; David E Larson; Ling Lin; Charles Lu; Michael D McLellan; Joshua F McMichael; Jacqueline Payton; Heather Schmidt; David H Spencer; Michael H Tomasson; John W Wallis; Lukas D Wartman; Mark A Watson; John Welch; Michael C Wendl; Adrian Ally; Miruna Balasundaram; Inanc Birol; Yaron Butterfield; Readman Chiu; Andy Chu; Eric Chuah; Hye-Jung Chun; Richard Corbett; Noreen Dhalla; Ranabir Guin; An He; Carrie Hirst; Martin Hirst; Robert A Holt; Steven Jones; Aly Karsan; Darlene Lee; Haiyan I Li; Marco A Marra; Michael Mayo; Richard A Moore; Karen Mungall; Jeremy Parker; Erin Pleasance; Patrick Plettner; Jacquie Schein; Dominik Stoll; Lucas Swanson; Angela Tam; Nina Thiessen; Richard Varhol; Natasja Wye; Yongjun Zhao; Stacey Gabriel; Gad Getz; Carrie Sougnez; Lihua Zou; Mark D M Leiserson; Fabio Vandin; Hsin-Ta Wu; Frederick Applebaum; Stephen B Baylin; Rehan Akbani; Bradley M Broom; Ken Chen; Thomas C Motter; Khanh Nguyen; John N Weinstein; Nianziang Zhang; Martin L Ferguson; Christopher Adams; Aaron Black; Jay Bowen; Julie Gastier-Foster; Thomas Grossman; Tara Lichtenberg; Lisa Wise; Tanja Davidsen; John A Demchok; Kenna R Mills Shaw; Margi Sheth; Heidi J Sofia; Liming Yang; James R Downing; Greg Eley Journal: N Engl J Med Date: 2013-05-01 Impact factor: 91.245