Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data.
In profiling studies, the analysis of a single dataset often leads to unsatisfactory results because of the small sample size. Multi-dataset analysis utilizes information of multiple independent datasets and outperforms single-dataset analysis. Among the available multi-dataset analysis methods, integrative analysis methods aggregate and analyze raw data and outperform meta-analysis methods, which analyze multiple datasets separately and then pool summary statistics. In this study, we conduct integrative analysis and marker selection under the heterogeneity structure, which allows different datasets to have overlapping but not necessarily identical sets of markers. Under certain scenarios, it is reasonable to expect some similarity of identified marker sets - or equivalently, similarity of model sparsity structures - across multiple datasets. However, the existing methods do not have a mechanism to explicitly promote such similarity. To tackle this problem, we develop a sparse boosting method. This method uses a BIC/HDBIC criterion to select weak learners in boosting and encourages sparsity. A new penalty is introduced to promote the similarity of model sparsity structures across datasets. The proposed method has a intuitive formulation and is broadly applicable and computationally affordable. In numerical studies, we analyze right censored survival data under the accelerated failure time model. Simulation shows that the proposed method outperforms alternative boosting and penalization methods with more accurate marker identification. The analysis of three breast cancer prognosis datasets shows that the proposed method can identify marker sets with increased similarity across datasets and improved prediction performance. Copyright © 2016 John Wiley & Sons, Ltd.
VA Cooperative Studies Program Coordinating Center, West Haven, CT; Department of Biostatistics, Yale University, New Haven, CT, U.S.A.
Yuan
Y
Huang
Center of Quantitative Medicine, Duke-NUS Medical School, Singapore.
Jin
J
Liu
Department of Biostatistics, Yale University, New Haven, CT, U.S.A.
Huangdi
H
Yi
School of Health Care Administration, Big Data Research Center & School of Management, Taipei Medical University, Taipei, Taiwan.
Ben-Chang
BC
Shia
VA Cooperative Studies Program Coordinating Center, West Haven, CT; Department of Biostatistics, Yale University, New Haven, CT, U.S.A.
Shuangge
S
Ma
D014408:Biomarkers, Tumor / Q000235:genetics; D001943:Breast Neoplasms / Q000175:diagnosis / Q000235:genetics; D003627:Data Interpretation, Statistical; D005260:Female; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000453:epidemiology / Q000235:genetics; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*; D011379:Prognosis
D014408:Biomarkers, Tumor; D005819:Genetic Markers
heterogeneity structure; integrative analysis; marker identification; model sparsity structure; sparse boosting
Genet Res (Camb). 2013 Jun;95(2-3):68-77
23938111
Biostatistics. 2012 Jul;13(3):509-22
22045909
Biostatistics. 2011 Oct;12(4):710-22
21525421
Nature. 2002 Jan 31;415(6871):530-6
11823860
Genet Res (Camb). 2012 Aug;94(4):205-21
22950901
Proc Natl Acad Sci U S A. 2003 Sep 2;100(18):10393-8
12917485
Stat Med. 2011 Dec 10;30(28):3361-71
22105693
Nucleic Acids Res. 2012 Jan;40(2):e15
22116060
Scand Stat Theory Appl. 2014 Mar 1;41(1):87-103
24578589
Proc Natl Acad Sci U S A. 2004 Jun 22;101(25):9309-14
15184677
Lancet. 2003 May 10;361(9369):1590-6
12747878
J Natl Cancer Inst. 2006 Feb 15;98(4):262-72
16478745
Stat Med. 1997 May 15;16(9):981-91
9160493
Nucleic Acids Res. 2012 May;40(9):3785-99
22262733
Stat Med. 2001 Jul 30;20(14):2115-30
11439425