Analysis of cancer gene expression data with an assisted robust marker identification approach

Hao Chai; Xingjie Shi; Qingzhao Zhang; Qing Zhao; Yuan Huang; Shuangge Ma

doi:10.1002/gepi.22066

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Analysis of cancer gene expression data with an assisted robust marker identification approach

Journal article

Peer reviewed

Analysis of cancer gene expression data with an assisted robust marker identification approach

Hao Chai, Xingjie Shi, Qingzhao Zhang, Qing Zhao, Yuan Huang and Shuangge Ma

Genetic epidemiology, Vol.41(8), pp.779-789

12/2017

DOI: 10.1002/gepi.22066

PMCID: PMC5912176

PMID: 28913902

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Abstract

Gene expression (GE) studies have been playing a critical role in cancer research. Despite tremendous effort, the analysis results are still often unsatisfactory, because of the weak signals and high data dimensionality. Analysis is often further challenged by the long-tailed distributions of the outcome variables. In recent multidimensional studies, data have been collected on GEs as well as their regulators (e.g., copy number alterations (CNAs), methylation, and microRNAs), which can provide additional information on the associations between GEs and cancer outcomes. In this study, we develop an ARMI (assisted robust marker identification) approach for analyzing cancer studies with measurements on GEs as well as regulators. The proposed approach borrows information from regulators and can be more effective than analyzing GE data alone. A robust objective function is adopted to accommodate long-tailed distributions. Marker identification is effectively realized using penalization. The proposed approach has an intuitive formulation and is computationally much affordable. Simulation shows its satisfactory performance under a variety of settings. TCGA (The Cancer Genome Atlas) data on melanoma and lung cancer are analyzed, which leads to biologically plausible marker identification and superior prediction.

Biomarkers, Tumor - genetics

Biomarkers, Tumor - metabolism

Gene Expression Regulation, Neoplastic

Genes, Neoplasm

Humans

Melanoma - genetics

Melanoma - metabolism

Melanoma - pathology

Models, Genetic

Neoplasms - genetics

Neoplasms - metabolism

Neoplasms - pathology

Phenotype

Skin Neoplasms - genetics

Skin Neoplasms - metabolism

Skin Neoplasms - pathology

Details

Title: Subtitle: Analysis of cancer gene expression data with an assisted robust marker identification approach
Creators: Hao Chai - Yale University
Xingjie Shi - Nanjing University of Finance and Economics
Qingzhao Zhang - Xiamen University
Qing Zhao - Merck & Co., Inc., Rahway, NJ, USA (United States)
Yuan Huang - Yale University
Shuangge Ma - Yale University
Resource Type: Journal article
Publication Details: Genetic epidemiology, Vol.41(8), pp.779-789
DOI: 10.1002/gepi.22066
PMID: 28913902
PMCID: PMC5912176
NLM abbreviation: Genet Epidemiol
ISSN: 0741-0395
eISSN: 1098-2272
Grant note: DOI: 10.13039/100009376, name: National Bureau of Statistics of China, award: 2016LD01; DOI: 10.13039/100000054, name: National Cancer Institute, award: R01CA204120, R03CA182984, R21CA191383
Language: English
Date published: 12/2017
Academic Unit: Biostatistics
Record Identifier: 9984364433802771

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