Statistical Modeling of OMICS data
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Statistical Modeling of OMICS data
Min Zhang, M.D., Ph.D.
Department of StatisticsPurdue University
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OMICS Data
Genomics (SNP)
Glycoproteomics
Lipdomics
Metabolomics
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Outline
Statistical Methods for Identifying Biomarkers
Metabolomics Align GCxGC-MS Data
Other Projects
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Statistical Methods for Identifying Biomarkers
Classical Methods
Bayesian Variable Selection
Regularized Variable Selection
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Regularized Variable Selection
Feasible
Easy to implement
Incorporate a large number of factors
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Regularized Variable Selection
Fast
Do not need to calculate inverse of any matrix
As fast as repeating an univariate association study serveral times
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Regularized Variable Selection
Fruitful Effective and efficient for variable
selection OMICS data in CCE Genome-wide association study Epistasis Gene-gene interactions eQTL mapping
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Regularized Variable Selection
More Details
Will be presented by Yanzhu Lin in the future
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Alignment of GCxGC-MS Data
The Two-Dimensional Correlation Optimized Warping (2D-COW) Algorithm
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The 2-D COW Algorithm
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The 2-D COW Algorithm
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The 2-D COW Algorithm Applying the 1-D alignment parameters
simultaneously to warp the chromatogram
A Toy Example
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Align Homogeneous Images (TIC)
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Align Homogeneous Images (SIC)
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Align Heterogeneous Images (SIC)
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Align Heterogeneous Images (TIC)
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Align Chromatograms from Serum Samples
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Align Chromatograms from Serum Samples
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Other Projects
Identify Differentially Expressed Features in GCxGC-MS Data
Integration of OMICS data
Other Clinical Data
More …
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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
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Acknowledgements
Dabao Zhang
Yanzhu Lin
Fred Regnier
Xiaodong Huang
Dan Raftery