Testing-based Measures for Finding Correspondence of Genomic Samples

  • Jingyi Li, UCLA
  • A Genomics@JHU Seminar
  • When: November 17, 2015, 10:00
  • Where: The Barber Conference Room at Charles Commons
    10 E 33rd Street
    Baltimore, MD 21218
  • Light refreshments served at 9:30am


Comparative genomics has gained increasing popularity in genomic research thanks to the development of high-throughput technologies including microarray and next-generation sequencing that have generated numerous genomic data. Many important scientific questions are related to understanding the conservation and differentiation of biological processes in different species. In this talk, I will introduce two testing based measures: TROM (Transcriptome Overlap Measure) and EPOM (Epigenome Overlap Measure), for comparing transcriptomes and epigenomes within or between different species. In contrast to classical correlation analyses, these two measures provide a different perspective to interpret the similarity of transcriptomes and epigenomes. Specifically, they are based on 1) identified associated genes or intergenic regions that capture transcriptomic or epigenomic characteristics of biological samples and 2) an overlap test. We use simulation and real data studies to demonstrate that these testing based measures are more powerful in identifying similar transcriptomes or epigenomes and more robust to data noise than Pearson and Spearman correlations. We apply TROM to compare the developmental stages of six Drosophila species, C. elegans, zebrafish, sea urchin and mouse liver, and find interesting correspondence patterns that imply conserved gene expression programs in their development. We also apply TROM to study the conservation of stem cell differentiation in four mammalian species: human, chimpanzee, bonobo and mouse. Moreover, we use EPOM to study the similarity of human tissues and cell types based on Roadmap Epigenomic data.


About Jingyi Li.

Genomics @ JHU Seminar Series

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