1 / 18

Stitching the Tutorials Together

This tutorial introduces a systematic approach to studying the correlation between microRNA (miRNA) expression and copy number variations (CNVs) related to glioma. Focusing on hsa-miR-26a, we explore its amplification and the implications for target gene expression, while highlighting rigorous statistical methods for initial screening and integrating clinical phenotypes into the analysis. The session emphasizes the importance of linear regression, biological filtering, and the quest for causal relationships in miRNA-target gene interactions.

kristy
Télécharger la présentation

Stitching the Tutorials Together

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. Stitching the Tutorials Together Introduction to Systems Biology Course Chris Plaisier Institute for Systems Biology

  2. Impetus for Tutorial

  3. Searching for Amplified miRNAs • Region on chromosome 12 containing hsa-miR-26a is amplified in glioma • This region also contains a few other genes including the genes encapsulating hsa-miR-26a

  4. Screen for miRNAs Correlated with CNV • Which miRNA’s expression is modified by CNVs? • hsa-miR-26a = Amplified • hsa-miR-491 = Deleted • hsa-miR-339 = Amplified and deleted in sub-clonalpopulations

  5. Correlation of miRNA Expression and Copy Number Variation

  6. Comparison of Approaches • We identified other miRNA not reported by Kim, et al. 2010 • Our screening approach yielded more putatively dysregulatedmiRNAs • Cherry picking can be useful • Typically need to follow rigorous statistical protocols for initial screen • After initial screen multiple paths to publication • Up to expertise of researcher

  7. How could we make it better? • User linear regression and add covariates: • Sex • Age • ... • Others?

  8. Gene Affected by miRNA Perturbation • Applied biologically motivated filtering of putative genes targeted by miRNA to those predicted by PITA • Complementarity of miRNA to 3’ UTR • … • Tested for correlation between miRNA to putative target genes

  9. Correlation of miRNA Expression with Putative Target Genes

  10. Correlation with CNV • We took it one step further and tested for a correlation between the CNV and miRNA target genes • Only one gene for hsa-miR-26a that was associated with the miRNA expression was not associated with hsa-miR-26a CNV

  11. How could we make it better? • Use linear regression and integrate strength of PITA match • Others?

  12. Causality? • Purpose of correlation with CNV is that we wanted to make sure the effect of the miRNA on the target genes was through the CNV • Can we really say for sure the direction of the association? • Can we imply causality?

  13. Consistent with Causality • While not a test for causality the fact that conditioning miRNA target gene expression on hsa-miR-26a expression significantly reduces the association is consistent with a causal model • There are ways we could model causal information flow • http://www.genetics.ucla.edu/labs/horvath/aten/NEO • http://www.nature.com/ng/journal/v37/n7/abs/ng1589.html

  14. Integration with Clinical Phenotypes • Case / Control • Student’s T-Test • Correlation / Regression • Survival analysis • Need time to event and status at end of study

  15. Glioma miRNAs Case/Control

  16. hsa-miR-26a Amplification Significantly Associated with Lower Survival

  17. Summary • We repeated analysis done by Kim, et al. • You learned how to calculate a correlation and Student’s T-test in R • You learned how to correct for multiple testing • You learned how to go from a global study to specific hypotheses for testing

More Related