<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We compared two of the most common methods for differential expression analysis in the RNA-seq field: edgeR and DESeq2. We evaluated these methods based on four real RNA-seq plant datasets. The results indicate that there is a large number of joint differentially expressed genes between the two methods. However, depending on the research goal and the preparation of an experiment, different approaches to statistical analysis and interpretation of the results can be suggested. We focus on answering the question: what workflow should be used in the statistical analysis of the datasets under consideration to minimize the number of falsely identified differentially expressed genes?</p>
</abstract>

We compared two of the most common methods for differential expression analysis in the RNA-seq field: edgeR and DESeq2. We evaluated these methods based on four real RNA-seq plant datasets. The results indicate that there is a large number of joint differentially expressed genes between the two methods. However, depending on the research goal and the preparation of an experiment, different approaches to statistical analysis and interpretation of the results can be suggested. We focus on answering the question: what workflow should be used in the statistical analysis of the datasets under consideration to minimize the number of falsely identified differentially expressed genes?

Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology

Department of Mathematical and Statistical Methods

Biometrical Letters

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

We compared two of the most common methods for differential expression analysis in the RNA-seq field: edgeR and DESeq2. We evaluated these methods based on...

Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology

Online veröffentlicht: 30. Dez. 2022

Seitenbereich: 127 - 139

DOI: https://doi.org/10.2478/bile-2022-0009

Schlüsselwörter
edgeR, DESeq2, RNA-seq, statistical methods for differential analysis

© 2022 Grażyna Niedziela et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Application of edgeR and DESeq2 methods in plant experiments based on RNA-seq technology

Online veröffentlicht: 30. Dez. 2022

Seitenbereich: 127 - 139

DOI: https://doi.org/10.2478/bile-2022-0009

SchlüsselwörteredgeR, DESeq2, RNA-seq, statistical methods for differential analysis

© 2022 Grażyna Niedziela et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
edgeR, DESeq2, RNA-seq, statistical methods for differential analysis