Current technology advances have brought us massive biomedical data for statistical analysis, for example, the cancer microarray data. Typical of these data is the common feature that the number of observed samples is much smaller than the number of variables/predictors, which poses challenges for statistical analysis. Identifying differentially expressed genes and predicting sample phenotype based on the gene expressions data are two important research questions in analyzing these large-scale biomedical data. This project proposes to develop some new large-scale prediction and signifiance analysis statistical methods that are specially designed to address small sample size and potential sampe heterogeneity issues, incorporate existing biological information for improved inference, and can be applied very generally. The usefulness of these methods will be shown with the large-scale biomedical data originating from the leukemia cancer research projects. The cancer projects aimed to improve the cancer molecular diagnosis and prognosis by identifying molecular biomarkers for critical early treatment and rapid, noninvasive testing.
The specific aims are 1) Develop new statistical methods for significance testing of large-scale molecular markers. 2) Develop new statistical methods that appropriately model the sample heterogeneity for significance testing. 3) Develop new statistical methods that utilize the gene group information to improve cancer prediction. 4) Use the developed models and methods to answer research questions relevant to public health in the leukemia cancer projects;and implement and validate the proposed methods in user-friendly and well-documented software, and distribute them to the scientific community at no charge. Project

Public Health Relevance

It is very important to identify new biomarkers and study the molecular prediction of leukemia cancer patients. We propose to study novel statistical methods for analyzing the large-scale biomedical data to realize their full potential of molecular diagnosis and prognosis of leukemia cancer.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
5R01CA134848-02
Application #
7835748
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Tricoli, James
Project Start
2009-05-07
Project End
2012-04-30
Budget Start
2010-05-01
Budget End
2012-04-30
Support Year
2
Fiscal Year
2010
Total Cost
$152,039
Indirect Cost
Name
University of Minnesota Twin Cities
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
555917996
City
Minneapolis
State
MN
Country
United States
Zip Code
55455
Wu, Baolin; Pankow, James S (2017) Genome-wide association test of multiple continuous traits using imputed SNPs. Stat Interface 10:379-386
Guo, Bin; Wu, Baolin (2017) Reader reaction on the fast small-sample kernel independence test for microbiome community-level association analysis. Biometrics :
Wu, Baolin; Guan, Weihua; Pankow, James S (2016) On Efficient and Accurate Calculation of Significance P-Values for Sequence Kernel Association Testing of Variant Set. Ann Hum Genet 80:123-35
Wu, Baolin; Pankow, James S (2016) On Sample Size and Power Calculation for Variant Set-Based Association Tests. Ann Hum Genet 80:136-43
Wu, Baolin; Pankow, James S (2016) Sequence Kernel Association Test of Multiple Continuous Phenotypes. Genet Epidemiol 40:91-100
Wu, Baolin; Pankow, James S; Guan, Weihua (2015) Sequence Kernel Association Analysis of Rare Variant Set Based on the Marginal Regression Model for Binary Traits. Genet Epidemiol 39:399-405
Lee, Sang Mee; Wu, Baolin; Kersey, John H (2014) Likelihood-Based Approach to Gene Set Enrichment Analysis with a Finite Mixture Model. Stat Biosci 6:38-54
Cao, Xiting; Wu, Baolin; Hertz, Marshall I (2013) Empirical null distribution based modeling of multi-class differential gene expression detection. J Appl Stat 40:347-357
Zhang, Wei; Ota, Takayo; Shridhar, Viji et al. (2013) Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment. PLoS Comput Biol 9:e1002975
Wu, Baolin (2013) Sparse cluster analysis of large-scale discrete variables with application to single nucleotide polymorphism data. J Appl Stat 40:358-367

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