ArticleStatistics in biosciences2017
TROM: A Testing-Based Method for Finding Transcriptomic Similarity of Biological Samples.
Article in Statistics in biosciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Pathway-Based Similarity Measurement to Quantify Transcriptomics Similarity Between Human Tissues and Preclinical Models.Clinical pharmacology and therapeutics · 2025Article
- A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels.Acta neuropathologica communications · 2024Article
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- Liver cancer heterogeneity modeled by in situ genome editing of hepatocytes.Science advances · 2022Article
- Learning a genome-wide score of human-mouse conservation at the functional genomics level.Nature communications · 2021Article
- An information-theoretic approach for measuring the distance of organ tissue samples using their transcriptomic signatures.Bioinformatics (Oxford, England) · 2021Article
- Modeling and analysis of RNA-seq data: a review from a statistical perspective.Quantitative biology (Beijing, China) · 2018Article
- Neyman-Pearson classification algorithms and NP receiver operating characteristics.Science advances · 2018Article
- Correspondence of D. melanogaster and C. elegans developmental stages revealed by alternative splicing characteristics of conserved exons.BMC genomics · 2017Article
- Large-scale mapping of mammalian transcriptomes identifies conserved genes associated with different cell states.Nucleic acids research · 2017Article
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Authors and funding
3 authors.
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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
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Registered trials
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