Cancer genomic studies have been extensively conducted using high-throughput profiling techniques. Molecular signatures identified from these studies have been used to assist clinical practice including diagnosis, prognosis prediction, and selection of treatment regimens. Despite promising successes, these signatures often suffer from a lack of reproducibility and reliability. A major cause of this problem is the relatively small sample sizes and hence lack of power of individual studies. A cost-effective remedy is to pool and analyze data from multiple studies. Available methods for analyzing multiple datasets have serious drawbacks. There is an urgent need for novel statistical methodologies that can effectively analyze and extract useful information from multiple cancer genomic studies. This project will be among the first to systematically develop and implement integrative analysis methodologies. The proposed methods will be able to effectively analyze heterogeneous high-dimensional datasets from multiple cancer genomic studies. They will be able to account for the joint effects of multiple genomic measurements and the pathway structure in modeling cancer development, and be able to properly adjust for clinical and environmental risk factors. Dissemination through the development of R package and public website will make our research accessible to the general biomedical community. Analysis of data on multiple cancer clinical outcomes will lead to identification of clinically useful markers. Specifically, we plan to (1) Develop penalized marginal screening methods for integrative analysis of multiple heterogeneous cancer genomic datasets;(2) Develop individual-marker based penalization methods for integrative analysis of multiple heterogeneous cancer genomic datasets;(3) Develop pathway based penalization methods for integrative analysis of multiple heterogeneous cancer genomic datasets;(4) Develop integrative analysis methods that can properly accommodate partially linear clinical and environmental covariate effects;(5) Disseminate the proposed methods, analyze data on multiple cancers, and identify cancer markers. The proposed study will emphasize equally development of novel methodologies and their practical applications. It will make significant contributions to methodologies for integrative analysis of multiple heterogeneous datasets, and enable researchers to more efficiently extract useful information from cancer genomic studies.
This study will be among the first to systematically develop and implement novel integrative analysis methods, which can effectively analyze multiple heterogeneous and high-dimensional cancer genomic studies. It will enrich the family of methodologies for integrative analysis, enable researchers to more efficiently extract useful information from existing data, and lead to a better understanding of cancer genomics. Applications of the proposed methods will lead to identification of clinically useful cancer markers.
|Huang, Yuan; Liu, Jin; Yi, Huangdi et al. (2017) Promoting similarity of model sparsity structures in integrative analysis of cancer genetic data. Stat Med 36:509-559|
|Jiang, Yu; Shi, Xingjie; Zhao, Qing et al. (2016) Integrated analysis of multidimensional omics data on cutaneous melanoma prognosis. Genomics 107:223-30|
|Liu, Jin; Yang, Can; Shi, Xingjie et al. (2016) Analyzing Association Mapping in Pedigree-Based GWAS Using a Penalized Multitrait Mixed Model. Genet Epidemiol 40:382-93|
|Shi, Xingjie; Zhao, Qing; Huang, Jian et al. (2015) Deciphering the associations between gene expression and copy number alteration using a sparse double Laplacian shrinkage approach. Bioinformatics 31:3977-83|
|Breheny, Patrick; Huang, Jian (2015) Group descent algorithms for nonconvex penalized linear and logistic regression models with grouped predictors. Stat Comput 25:173-187|
|Zhao, Qing; Shi, Xingjie; Xie, Yang et al. (2015) Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA. Brief Bioinform 16:291-303|
|Jiang, Dingfeng; Huang, Jian (2015) Concave 1-norm group selection. Biostatistics 16:252-67|
|Zhao, Qing; Shi, Xingjie; Huang, Jian et al. (2015) Integrative Analysis of ""-Omics"" Data Using Penalty Functions. Wiley Interdiscip Rev Comput Stat 7:99-108|
|Shi, Xingjie; Shen, Shihao; Liu, Jin et al. (2014) Similarity of markers identified from cancer gene expression studies: observations from GEO. Brief Bioinform 15:671-84|
|Liu, Jin; Huang, Jian; Zhang, Yawei et al. (2014) Integrative analysis of prognosis data on multiple cancer subtypes. Biometrics 70:480-8|
Showing the most recent 10 out of 50 publications