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Significance tests for analyzing gene expression data with small sample sizes

Ullah, Insha
; 
Paul, Sudhir
; 
Hong, Zhenjie
; 
Wang, You-Gan
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Author
Ullah, Insha
Paul, Sudhir
Hong, Zhenjie
Wang, You-Gan
Abstract
Motivation: Under two biologically different conditions, we are often interested in identifying differentially expressed genes. It is usually the case that the assumption of equal variances on the two groups is violated for many genes where a large number of them are required to be filtered or ranked. In these cases, exact tests are unavailable and the Welch's approximate test is most reliable one. The Welch's test involves two layers of approximations: approximating the distribution of the statistic by a t-distribution, which in turn depends on approximate degrees of freedom. This study attempts to improve upon Welch's approximate test by avoiding one layer of approximation. Results: We introduce a new distribution that generalizes the t-distribution and propose a Monte Carlo based test that uses only one layer of approximation for statistical inferences. Experimental results based on extensive simulation studies show that the Monte Carol based tests enhance the statistical power and performs better than Welch's t-approximation, especially when the equal variance assumption is not met and the sample size of the sample with a larger variance is smaller. We analyzed two gene-expression datasets, namely the childhood acute lymphoblastic leukemia gene-expression dataset with 22 283 genes and Golden Spike dataset produced by a controlled experiment with 13 966 genes. The new test identified additional genes of interest in both datasets. Some of these genes have been proven to play important roles in medical literature. Availability and implementation: R scripts and the R package mcBFtest is available in CRAN and to reproduce all reported results are available at the GitHub repository, https://github.com/iullah1980/MCTcodes. Supplementary information: Supplementary data is available at Bioinformatics online.
Keywords
Biometry, Gene Expression, Monte Carlo Method, Sample Size, Statistical Distributions
Date
2019
Type
Journal article
Journal
Book
Volume
35
Issue
20
Page Range
3996-4003
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Collections
Institute for Learning Sciences and Teacher Education
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URI
https://hdl.handle.net/20.500.14802/14909
Relation URI
DOI
10.1093/bioinformatics/btz189
Source URL
https://academic.oup.com/bioinformatics/article/35/20/3996/5381541
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Open Access Status
License
All rights reserved
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Controlled
Notes
© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)