title: SPARCoC: a new framework for molecular pattern discovery and cancer gene identification.
abstract: It is challenging to cluster cancer patients of a certain histopathological type into molecular subtypes of clinical importance and identify gene signatures directly relevant to the subtypes. Current clustering approaches have inherent limitations, which prevent them from gauging the subtle heterogeneity of the molecular subtypes. In this paper we present a new framework: SPARCoC (Sparse-CoClust), which is based on a novel Common-background and Sparse-foreground Decomposition (CSD) model and the Maximum Block Improvement (MBI) co-clustering technique. SPARCoC has clear advantages compared with widely-used alternative approaches: hierarchical clustering (Hclust) and nonnegative matrix factorization (NMF). We apply SPARCoC to the study of lung adenocarcinoma (ADCA), an extremely heterogeneous histological type, and a significant challenge for molecular subtyping. For testing and verification, we use high quality gene expression profiling data of lung ADCA patients, and identify prognostic gene signatures which could cluster patients into subgroups that are significantly different in their overall survival (with p-values < 0.05). Our results are only based on gene expression profiling data analysis, without incorporating any other feature selection or clinical information; we are able to replicate our findings with completely independent datasets. SPARCoC is broadly applicable to large-scale genomic data to empower pattern discovery and cancer gene identification.
authors.affiliation: Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, N.T. Hong Kong.
authors.forename: Shiqian
authors.initials: S
authors.lastname: Ma
authors.affiliation: Molecular Biosciences Program, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.
authors.forename: Daniel
authors.initials: D
authors.lastname: Johnson
authors.affiliation: Molecular Biosciences Program, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.
authors.forename: Cody
authors.initials: C
authors.lastname: Ashby
authors.affiliation: Department of Pharmacology and Toxicology and the Cancer Center, Medical College of Wisconsin, Milwaukee, Wisconsin 53226, United States of America.
authors.forename: Donghai
authors.initials: D
authors.lastname: Xiong
authors.affiliation: Arkansas Bioscience Institute and Department of Biological Sciences, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.
authors.forename: Carole L
authors.initials: CL
authors.lastname: Cramer
authors.affiliation: The Geisel School of Medicine, Dartmouth College, Lebanon, New Hampshire 03756, United States of America.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.affiliation: Department of Industrial and Systems Engineering, University of Minnesota, Minneapolis, Minnesota 55455, United States of America.
authors.forename: Shuzhong
authors.initials: S
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, Arkansas State University, Jonesboro, Arkansas 72467, United States of America.
authors.forename: Xiuzhen
authors.initials: X
authors.lastname: Huang
mesh_terms: D000230:Adenocarcinoma / Q000145:classification / Q000235:genetics* / Q000473:pathology; D000077192:Adenocarcinoma of Lung; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D016000:Cluster Analysis; D030541:Databases, Genetic; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods; D052138:Genes, Neoplasm; D005820:Genetic Testing / Q000379:methods; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000235:genetics* / Q000473:pathology; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D014408:Biomarkers, Tumor