TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples

Article

DOI: 10.1007/s12561-016-9163-y

Cite this article as:
Li, W.V., Chen, Y. & Li, J.J. Stat Biosci (2016). doi:10.1007/s12561-016-9163-y

Abstract

Comparative transcriptomics has gained increasing popularity in genomic research thanks to the development of high-throughput technologies including microarray and next-generation RNA sequencing that have generated numerous transcriptomic data. An important question is to understand the conservation and divergence of biological processes in different species. We propose a testing-based method TROM (Transcriptome Overlap Measure) for comparing transcriptomes within or between different species, and provide a different perspective, in contrast to traditional correlation analyses, about capturing transcriptomic similarity. Specifically, the TROM method focuses on identifying associated genes that capture molecular characteristics of biological samples, and subsequently comparing the biological samples by testing the overlap of their associated genes. We use simulation and real data studies to demonstrate that TROM is more powerful in identifying similar transcriptomes and more robust to stochastic gene expression noise than Pearson and Spearman correlations. We apply TROM to compare the developmental stages of six Drosophila species, C. elegans, S. purpuratus, D. rerio and mouse liver, and find interesting correspondence patterns that imply conserved gene expression programs in the development of these species. The TROM method is available as an R package on CRAN (https://cran.r-project.org/package=TROM) with manuals and source codes available at http://www.stat.ucla.edu/~jingyi.li/software-and-data/trom.html.

Keywords

Transcriptomic similarity measure Multi-species developmental stages Robustness to platform differences Comparative transcriptomics Microarray vs. RNA-seq Pearson correlation coefficient Spearman correlation coefficient overlap test 

Funding information

Funder NameGrant NumberFunding Note
Hellman Foundation
  • Hellman Fellowship 2015

Copyright information

© International Chinese Statistical Association 2016

Authors and Affiliations

  1. 1.Department of StatisticsUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of Human GeneticsUniversity of CaliforniaLos AngelesUSA

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