Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/80508
Title: Stability of methods for differential expression analysis of RNA-seq data
Authors: Lin, BQ
Pang, Z 
Keywords: Stability
DE analysis
RNA-seq data
Issue Date: 2019
Publisher: BioMed Central
Source: BMC genomics, 40544 2019, v. 20, 35, p. 1-13 How to cite?
Journal: BMC genomics 
Abstract: Background: As RNA-seq becomes the assay of choice for measuring gene expression levels, differential expression analysis has received extensive attentions of researchers. To date, for the evaluation of DE Methods, most attention has been paid on validity. Yet another important aspect of DE Methods, stability, is overlooked and has not been studied to the best of our knowledge.
Results: In this study, we empirically show the need of assessing stability of DE methods and propose a stability metric, called Area Under the Correlation curve (AUCOR), that generates the perturbed datasets by a mixture distribution and combines the information of similarities between sets of selected features from these perturbed datasets and the original dataset.
Conclusion: Empirical results support that AUCOR can effectively rank the DE methods in terms of stability for given RNA-seq datasets. In addition, we explore how biological or technical factors from experiments and data analysis affect the stability of DE methods. AUCOR is implemented in the open-source R package AUCOR, with source code freely available at https://github.com/linbingqing/stableDE.
URI: http://hdl.handle.net/10397/80508
EISSN: 1471-2164
DOI: 10.1186/s12864-018-5390-6
Rights: © The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to theCreative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
The following publication Lin, B. Q., & Pang, Z. (2019). Stability of methods for differential expression analysis of RNA-seq data. BMC Genomics, 20, 35, 1-13 is available at https://dx.doi.org/10.1186/s12864-018-5390-6
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