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Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

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

Wei Vivian Li¹, Yiling Chen¹, Jingyi Jessica Li^1,2

¹Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA.

Statistics in Biosciences

|August 8, 2017

Summary

A new method, Transcriptome Overlap Measure (TROM), identifies gene expression similarities across species. TROM is more powerful and robust than traditional correlation analyses for comparative transcriptomics research.

Keywords:

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

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Area of Science:

Genomics
Bioinformatics
Developmental Biology

Background:

High-throughput technologies like RNA sequencing generate vast transcriptomic data.
Understanding conserved and divergent biological processes across species is crucial.
Traditional correlation analyses have limitations in capturing transcriptomic similarity.

Purpose of the Study:

To introduce a novel testing-based method, Transcriptome Overlap Measure (TROM), for comparing transcriptomes.
To offer an alternative perspective to correlation analyses for assessing transcriptomic similarity.
To identify associated genes that characterize biological samples.

Main Methods:

Developed a testing-based method (TROM) to compare transcriptomes by assessing the overlap of associated genes.
Utilized simulation studies to evaluate TROM's performance.
Applied TROM to real transcriptomic data from multiple species and developmental stages.

Main Results:

TROM demonstrated higher power in identifying similar transcriptomes compared to Pearson and Spearman correlations.
TROM showed increased robustness against stochastic gene expression noise.
Analysis of developmental stages across species revealed conserved gene expression programs.

Conclusions:

TROM provides a more powerful and robust approach for comparative transcriptomics.
The method facilitates the discovery of conserved gene expression programs across diverse species.
TROM is available as an R package for broader scientific application.