With the advent of high-throughput molecular assay technologies, biologists are having to deal with the? analysis of high-dimensional genomic datasets. While statistical methods have been proposed for issues? such as differential expression with these data, relatively little work has been done in terms of? incorporating biological knowledge in the statistical analysis of high-throughput biological data in? human disease settings.? In this grant, we propose the development of statistical procedures for modeling of complex highdimensional? biological data with an emphasis towards incorporating functional biological knowledge.? The methods we propose will be implemented and distributed in software available to biologists. While the? major biological data example in this grant is from a microarray experiment in cancer, the methods? proposed here are general and can be developed for studying high-dimensional genotype-phenotype? associations in other contexts. Given this, we propose the following aims:? 1. Development of hierarchical models for modelling of high-dimensional data in complex cell systems.? 2. Development of statistical methodology for the identification of disease progressor genes.? 3. Development of statistical methodology for assessing the role of functional pathways based on? integration of gene expression and pathway data.? 4. Development of statistical methodology for determining regions of overexpression and underexpression? based on integration of gene expression and chromosomal location data.? 5. Dissemination of these results in user-friendly statistical software.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
5R01GM072007-05
Application #
7493391
Study Section
Special Emphasis Panel (ZGM1-CMB-0 (MB))
Program Officer
Remington, Karin A
Project Start
2004-09-01
Project End
2011-08-31
Budget Start
2008-09-01
Budget End
2011-08-31
Support Year
5
Fiscal Year
2008
Total Cost
$203,275
Indirect Cost
Name
Pennsylvania State University
Department
Biostatistics & Other Math Sci
Type
Schools of Arts and Sciences
DUNS #
003403953
City
University Park
State
PA
Country
United States
Zip Code
16802
Ghosh, Debashis (2013) Genomic outlier detection in high-throughput data analysis. Methods Mol Biol 972:141-53
Liu, Xuhang; Jin, Dong-Yan; McManus, Michael T et al. (2012) Precursor microRNA-programmed silencing complex assembly pathways in mammals. Mol Cell 46:507-17
Tseng, George C; Ghosh, Debashis; Feingold, Eleanor (2012) Comprehensive literature review and statistical considerations for microarray meta-analysis. Nucleic Acids Res 40:3785-99
Ghosh, Debashis (2012) Incorporating the empirical null hypothesis into the Benjamini-Hochberg procedure. Stat Appl Genet Mol Biol 11:
Poisson, Laila M; Sreekumar, Arun; Chinnaiyan, Arul M et al. (2012) Pathway-directed weighted testing procedures for the integrative analysis of gene expression and metabolomic data. Genomics 99:265-74
Begum, Ferdouse; Ghosh, Debashis; Tseng, George C et al. (2012) Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res 40:3777-84
Li, Yihan; Ghosh, Debashis (2012) Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data. Bioinformatics 28:807-14
Mortensen, Amanda H; MacDonald, James W; Ghosh, Debashis et al. (2011) Candidate genes for panhypopituitarism identified by gene expression profiling. Physiol Genomics 43:1105-16
Poisson, Laila M; Taylor, Jeremy M; Ghosh, Debashis (2011) Integrative set enrichment testing for multiple omics platforms. BMC Bioinformatics 12:459
Hu, Ming; Yu, Jindan; Taylor, Jeremy M G et al. (2010) On the detection and refinement of transcription factor binding sites using ChIP-Seq data. Nucleic Acids Res 38:2154-67

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