Data-driven detection of subtype-specific differentially expressed genes

Lulu Chen, Yingzhou Lu, Chiung Ting Wu, Robert Clarke, Guoqiang Yu, Jennifer E. Van Eyk, David M. Herrington, Yue Wang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Among multiple subtypes of tissue or cell, subtype-specific differentially-expressed genes (SDEGs) are defined as being most-upregulated in only one subtype but not in any other. Detecting SDEGs plays a critical role in the molecular characterization and deconvolution of multicellular complex tissues. Classic differential analysis assumes a null hypothesis whose test statistic is not subtype-specific, thus can produce a high false positive rate and/or lower detection power. Here we first introduce a One-Versus-Everyone Fold Change (OVE-FC) test for detecting SDEGs. We then propose a scaled test statistic (OVE-sFC) for assessing the statistical significance of SDEGs that applies a mixture null distribution model and a tailored permutation test. The OVE-FC/sFC test was validated on both type 1 error rate and detection power using extensive simulation data sets generated from real gene expression profiles of purified subtype samples. The OVE-FC/sFC test was then applied to two benchmark gene expression data sets of purified subtype samples and detected many known or previously unknown SDEGs. Subsequent supervised deconvolution results on synthesized bulk expression data, obtained using the SDEGs detected from the independent purified expression data by the OVE-FC/sFC test, showed superior performance in deconvolution accuracy when compared with popular peer methods.

Original languageEnglish (US)
Article number332
JournalScientific reports
Issue number1
StatePublished - Dec 2021
Externally publishedYes

Bibliographical note

Funding Information:
This work was funded in part by the National Institutes of Health under Grants HL111362-05A1, HL133932, W81XWH-18-1-0723 (BC171885P1), and U01NS115658-01.

Publisher Copyright:
© 2021, The Author(s).


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