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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2020
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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. |
format | Online Article Text |
id | pubmed-7671412 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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