COT: an efficient and accurate method for detecting marker genes among many subtypes

Yingzhou Lu, Chiung Ting Wu, Sarah J. Parker, Zuolin Cheng, Georgia Saylor, Jennifer E. Van Eyk, Guoqiang Yu, Robert Clarke, David M. Herrington, Yue Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Motivation: Ideally, a molecularly distinct subtype would be composed of molecular features that are expressed uniquely in the subtype of interest but in no others - so-called marker genes (MGs). MG plays a critical role in the characterization, classification or deconvolution of tissue or cell subtypes. We and others have recognized that the test statistics used by most methods do not exactly satisfy the MG definition and often identify inaccurate MG. Results: We report an efficient and accurate data-driven method, formulated as a Cosine-based One-sample Test (COT) in scatter space, to detect MG among many subtypes using subtype expression profiles. Fundamentally different from existing approaches, the test statistic in COT precisely matches the mathematical definition of an ideal MG. We demonstrate the performance and utility of COT on both simulated and real gene expression and proteomics data. The open source Python/R tool will allow biologists to efficiently detect MG and perform a more comprehensive and unbiased molecular characterization of tissue or cell subtypes in many biomedical contexts. Nevertheless, COT complements not replaces existing methods.

Original languageEnglish (US)
Article numbervbac037
JournalBioinformatics Advances
Issue number1
StatePublished - 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s). Published by Oxford University Press.

PubMed: MeSH publication types

  • Journal Article


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