Statistical Modeling of OMICS data

Min Zhang, M.D., Ph.D.

Department of StatisticsPurdue University

OMICS Data

Genomics (SNP)

Glycoproteomics

Lipdomics

Metabolomics

Outline

Statistical Methods for Identifying Biomarkers

Metabolomics Align GCxGC-MS Data

Other Projects

Statistical Methods for Identifying Biomarkers

Classical Methods

Bayesian Variable Selection

Regularized Variable Selection

Regularized Variable Selection

Feasible

Easy to implement

Incorporate a large number of factors

Regularized Variable Selection

Fast

Do not need to calculate inverse of any matrix

As fast as repeating an univariate association study serveral times

Regularized Variable Selection

Fruitful Effective and efficient for variable

selection OMICS data in CCE Genome-wide association study Epistasis Gene-gene interactions eQTL mapping

Regularized Variable Selection

More Details

Will be presented by Yanzhu Lin in the future

Alignment of GCxGC-MS Data

The Two-Dimensional Correlation Optimized Warping (2D-COW) Algorithm

The 2-D COW Algorithm

The 2-D COW Algorithm

The 2-D COW Algorithm Applying the 1-D alignment parameters

simultaneously to warp the chromatogram

A Toy Example

Align Homogeneous Images (TIC)

Align Homogeneous Images (SIC)

Align Heterogeneous Images (SIC)

Align Heterogeneous Images (TIC)

Align Chromatograms from Serum Samples

Align Chromatograms from Serum Samples

Other Projects

Identify Differentially Expressed Features in GCxGC-MS Data

Integration of OMICS data

Other Clinical Data

More …

Summary Regularized Variable Selection Method

for Identifying Biomarkers The 2D-COW Algorithm for Aligning

GCxGC-MS Data It can also be used to align LCxLC, LCxGC,

GCxGC, LCxCE, and CExCE data

In Progress Identify Differentially Expressed Features in

GCxGC-MS Data

Acknowledgements

Dabao Zhang

Yanzhu Lin

Fred Regnier

Xiaodong Huang

Dan Raftery