Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis

Wei Tang; Zhijun Liao; Quan Zou

doi:10.18632/oncotarget.12828

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Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis

Wei Tang _, Zhijun Liao and Quan Zou

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Oncotarget. 2016; 7:85613-85623. https://doi.org/10.18632/oncotarget.12828

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Abstract

Wei Tang1,*, Zhijun Liao2,3,* and Quan Zou3,4

1 Department of Biological Engineering, School of Chemical Engineering, Tianjin University, Tianjin, China

2 Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China

3 School of Computer Science and Technology, Tianjin University, Tianjin, China

4 State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China

* These authors have contributed equally to this work

Correspondence to:

Quan Zou, email:

Keywords: microRNA; differential expression; statistical significance test; MARS; oncomiRNA

Received: September 24, 2016 Accepted: October 14, 2016 Published: October 23, 2016

Abstract

MicroRNAs(miRNAs) often exert their oncogenic and tumor suppressor functions by suppressing protein-coding genes expressions in cancers and thus have a strong association with cancers’ generation, development and metastasis. Through comprehensively understanding differentially expressed miRNAs (oncomiRNA) in tumor tissues, we can elucidate the underlying molecular mechanisms in tumorigenesis and develop novel strategies for cancer diagnosis and treatment. The differential expression of miRNAs can now be analyzed through numerous statistical significance tests based on different principles, which are also available in various R packages. However, the results can be notably different. In this study, we compared miRNAs obtained from 6 common significance tests/R packages (t-test, Limma, DESeq, edgeR, LRT and MARS) with the miRNAs archived in two databases; HMDD 2.0 database, which collects experimentally validated differentially expressed miRNAs, and Infer microRNA-disease association database, which contains the potential disease-associated miRNAs by network forecasting. Finally, we sought the MARS method in DEGseq package more effectively searched out differentially expressed miRNAs than other common methods.

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Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis

Abstract