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An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects

We are motivated by biological studies intended to understand global gene expression fold change. Biologists have generally adopted a fixed cutoff to determine the significance of fold changes in gene expression studies (e.g. by using an observed fold change equal to two as a fixed threshold). Scien...

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Detalles Bibliográficos
Autores principales: Guo, Zhenxing, Cui, Ying, Shi, Xiaowen, Birchler, James A, Albizua, Igor, Sherman, Stephanie L, Qin, Zhaohui S, Ji, Tieming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671412/
https://www.ncbi.nlm.nih.gov/pubmed/33575620
http://dx.doi.org/10.1093/nargab/lqaa072
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author Guo, Zhenxing
Cui, Ying
Shi, Xiaowen
Birchler, James A
Albizua, Igor
Sherman, Stephanie L
Qin, Zhaohui S
Ji, Tieming
author_facet Guo, Zhenxing
Cui, Ying
Shi, Xiaowen
Birchler, James A
Albizua, Igor
Sherman, Stephanie L
Qin, Zhaohui S
Ji, Tieming
author_sort Guo, Zhenxing
collection PubMed
description We are motivated by biological studies intended to understand global gene expression fold change. Biologists have generally adopted a fixed cutoff to determine the significance of fold changes in gene expression studies (e.g. by using an observed fold change equal to two as a fixed threshold). Scientists can also use a t-test or a modified differential expression test to assess the significance of fold changes. However, these methods either fail to take advantage of the high dimensionality of gene expression data or fail to test fold change directly. Our research develops a new empirical Bayesian approach to substantially improve the power and accuracy of fold-change detection. Specifically, we more accurately estimate gene-wise error variation in the log of fold change. We then adopt a t-test with adjusted degrees of freedom for significance assessment. We apply our method to a dosage study in Arabidopsis and a Down syndrome study in humans to illustrate the utility of our approach. We also present a simulation study based on real datasets to demonstrate the accuracy of our method relative to error variance estimation and power in fold-change detection. Our developed R package with a detailed user manual is publicly available on GitHub at https://github.com/cuiyingbeicheng/Foldseq.
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spelling pubmed-76714122021-02-10 An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects Guo, Zhenxing Cui, Ying Shi, Xiaowen Birchler, James A Albizua, Igor Sherman, Stephanie L Qin, Zhaohui S Ji, Tieming NAR Genom Bioinform Methods Article We are motivated by biological studies intended to understand global gene expression fold change. Biologists have generally adopted a fixed cutoff to determine the significance of fold changes in gene expression studies (e.g. by using an observed fold change equal to two as a fixed threshold). Scientists can also use a t-test or a modified differential expression test to assess the significance of fold changes. However, these methods either fail to take advantage of the high dimensionality of gene expression data or fail to test fold change directly. Our research develops a new empirical Bayesian approach to substantially improve the power and accuracy of fold-change detection. Specifically, we more accurately estimate gene-wise error variation in the log of fold change. We then adopt a t-test with adjusted degrees of freedom for significance assessment. We apply our method to a dosage study in Arabidopsis and a Down syndrome study in humans to illustrate the utility of our approach. We also present a simulation study based on real datasets to demonstrate the accuracy of our method relative to error variance estimation and power in fold-change detection. Our developed R package with a detailed user manual is publicly available on GitHub at https://github.com/cuiyingbeicheng/Foldseq. Oxford University Press 2020-09-18 /pmc/articles/PMC7671412/ /pubmed/33575620 http://dx.doi.org/10.1093/nargab/lqaa072 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Methods Article
Guo, Zhenxing
Cui, Ying
Shi, Xiaowen
Birchler, James A
Albizua, Igor
Sherman, Stephanie L
Qin, Zhaohui S
Ji, Tieming
An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
title An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
title_full An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
title_fullStr An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
title_full_unstemmed An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
title_short An empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
title_sort empirical bayesian approach for testing gene expression fold change and its application in detecting global dosage effects
topic Methods Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7671412/
https://www.ncbi.nlm.nih.gov/pubmed/33575620
http://dx.doi.org/10.1093/nargab/lqaa072
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