This proposal develops novel statistical methods to select a small group of molecules from high-throughput data such as microarray and proteomic data from cancer research. The challenge of the study is the ultrahigh dimensionality inherited in these studies, particular when gene-gene interactions are introduced. The ultrahigh dimensionality has large impact on statistical computation, methodological developments, and theoretical studies. The challenge will be dealt by using the proposed novel independence screening methods, which also addresses the computational demand and stability, and the issues of stochastic error accumulation in ultra-high dimensional statistical inferences. An iterative independence screening method is introduced to find hidden signature genes that are marginally unimportant but jointly extremely important to the clinical outcomes. It also enables us to eliminate redundant molecules that are marginally highly but jointly weakly associated with clinical outcomes. With number of features surely reduced to a manageable level, penalized pseudo-likelihood methods will be introduced to further select relevant genes. In addition, methods for finding synergetic groups of molecules are introduced. The idea of independence screening and its iterated version will be applied to various statistical problems from the analysis of high throughput data, ranging from ultrahigh dimensional regression and classification to the analysis of survival time, estimation of genewide variance, and normalization of microarrays. The efficacy of the proposed methods will be evaluated via asymptotic theory and simulation studies. Data sets from on-going biomedical studies on cancer such as breast cancer, multiple myeloma, neuroblastoma, lung tumor, and liver carcigogen will be critically analyzed using the newly developed statistical and bioinformatic tools.

Public Health Relevance

Statistical Methods for Ultrahigh-dimensional Biomedical Data PI: Jianqing Fan This proposal develops novel statistical and bioinformatic tools for finding genes and proteins that are associated with clinical outcomes. Data sets from on-going biomedical studies on cancer such as breast cancer, multiple myeloma, neuroblastoma, lung tumor, and liver carcinogen will be critically analyzed using the newly developed statistical and bioinformatic tools. The research findings will have strong impact on understanding molecular mechanisms of cancer and developing therapeutic targets.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
2R01GM072611-05
Application #
7714616
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Remington, Karin A
Project Start
2006-02-01
Project End
2014-01-31
Budget Start
2010-02-01
Budget End
2011-01-31
Support Year
5
Fiscal Year
2010
Total Cost
$266,820
Indirect Cost
Name
Princeton University
Department
Miscellaneous
Type
Schools of Arts and Sciences
DUNS #
002484665
City
Princeton
State
NJ
Country
United States
Zip Code
08544
Wang, Weichen; Fan, Jianqing (2017) Asymptotics of empirical eigenstructure for high dimensional spiked covariance. Ann Stat 45:1342-1374
Cheng, Zhijian; Bosco, Dale B; Sun, Li et al. (2017) Neural Stem Cell-Conditioned Medium Suppresses Inflammation and Promotes Spinal Cord Injury Recovery. Cell Transplant 26:469-482
Fan, Jianqing; Han, Xu (2017) Estimation of the false discovery proportion with unknown dependence. J R Stat Soc Series B Stat Methodol 79:1143-1164
Fan, Jianqing; Li, Quefeng; Wang, Yuyan (2017) Estimation of high dimensional mean regression in the absence of symmetry and light tail assumptions. J R Stat Soc Series B Stat Methodol 79:247-265
Fan, Jianqing; Liao, Yuan; Wang, Weichen (2016) PROJECTED PRINCIPAL COMPONENT ANALYSIS IN FACTOR MODELS. Ann Stat 44:219-254
Rolfe, Alyssa J; Bosco, Dale B; Wang, Jingying et al. (2016) Bioinformatic analysis reveals the expression of unique transcriptomic signatures in Zika virus infected human neural stem cells. Cell Biosci 6:42
Dobriban, Edgar; Fan, Jianqing (2016) Regularity Properties for Sparse Regression. Commun Math Stat 4:1-19
Fan, Jianqing; Han, Fang; Liu, Han et al. (2016) Robust Inference of Risks of Large Portfolios. J Econom 194:298-308
Fan, Jianqing; Zhou, Wen-Xin (2016) Guarding against Spurious Discoveries in High Dimensions. J Mach Learn Res 17:
Jones, Zachary B; Ren, Yi (2016) Sphingolipids in spinal cord injury. Int J Physiol Pathophysiol Pharmacol 8:52-69

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