1 / 21

Statistical Modeling of OMICS data

Statistical Modeling of OMICS data. Min Zhang, M.D., Ph.D. Department of Statistics Purdue University. OMICS Data. Genomics (SNP) Glycoproteomics Lipdomics Metabolomics. Outline. Statistical Methods for Identifying Biomarkers Metabolomics Align GCxGC-MS Data Other Projects.

winola
Download Presentation

Statistical Modeling of OMICS data

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Statistical Modeling of OMICS data Min Zhang, M.D., Ph.D. Department of Statistics Purdue University

  2. OMICS Data • Genomics (SNP) • Glycoproteomics • Lipdomics • Metabolomics

  3. Outline • Statistical Methods for Identifying Biomarkers • Metabolomics • Align GCxGC-MS Data • Other Projects

  4. Statistical Methods for Identifying Biomarkers • Classical Methods • Bayesian Variable Selection • Regularized Variable Selection

  5. Regularized Variable Selection • Feasible • Easy to implement • Incorporate a large number of factors

  6. Regularized Variable Selection • Fast • Do not need to calculate inverse of any matrix • As fast as repeating an univariate association study serveral times

  7. Regularized Variable Selection • Fruitful • Effective and efficient for variable selection • OMICS data in CCE • Genome-wide association study • Epistasis • Gene-gene interactions • eQTL mapping

  8. Regularized Variable Selection • More Details • Will be presented by Yanzhu Lin in the future

  9. Alignment of GCxGC-MS Data • The Two-Dimensional Correlation Optimized Warping (2D-COW) Algorithm

  10. The 2-D COW Algorithm

  11. The 2-D COW Algorithm

  12. The 2-D COW Algorithm • Applying the 1-D alignment parameters simultaneously to warp the chromatogram A Toy Example

  13. Align Homogeneous Images (TIC)

  14. Align Homogeneous Images (SIC)

  15. Align Heterogeneous Images (SIC)

  16. Align Heterogeneous Images (TIC)

  17. Align Chromatograms from Serum Samples

  18. Align Chromatograms from Serum Samples

  19. Other Projects • Identify Differentially Expressed Features in GCxGC-MS Data • Integration of OMICS data • Other Clinical Data • More …

  20. 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

  21. Acknowledgements • Dabao Zhang • Yanzhu Lin • Fred Regnier • Xiaodong Huang • Dan Raftery

More Related