Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2 - Liu, Shiyi; Wang, Zitao; Zhu, Ronghui; Wang, Feiyan; Cheng, Yanxiang; Liu, Yeqiang (Journal of visualized experiments, 2021)
RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in diagnostics, prognostics, and therapeutics of tumors. Differential analysis of RNA-seq data is crucial to identify aberrant transcriptions, and limma, EdgeR and DESeq2 are efficient tools for differential analysis. However, RNA-seq differential analysis requires certain skills with R language and the ability to choose an appropriate method, which is lacking in the curriculum of medical education. Herein, we provide the detailed protocol to identify differentially expressed genes (DEGs) between cholangiocarcinoma (CHOL) and normal tissues through limma, DESeq2 and EdgeR, respectively, and the results are shown in volcano plots and Venn diagrams. The three protocols of limma, DESeq2 and EdgeR are similar but have different steps among the processes of the analysis. For example, a linear model is used for statistics in limma, while the negative binomial distribution is used in edgeR and DESeq2. Additionally, the normalized RNA-seq count data is necessary for EdgeR and limma but is not necessary for DESeq2. Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. The results of the three methods are partly overlapping. All three methods have their own advantages, and the choice of method only depends on the data.RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration and complex biological processes and has great value in diagnostics, prognostics, and therapeutics of tumors. Differential analysis of RNA-seq data is crucial to identify aberrant transcriptions, and limma, EdgeR and DESeq2 are efficient tools for differential analysis. However, RNA-seq differential analysis requires certain skills with R language and the ability to choose an appropriate method, which is lacking in the curriculum of medical education. Herein, we provide the detailed protocol to identify differentially expressed genes (DEGs) between cholangiocarcinoma (CHOL) and normal tissues through limma, DESeq2 and EdgeR, respectively, and the results are shown in volcano plots and Venn diagrams. The three protocols of limma, DESeq2 and EdgeR are similar but have different steps among the processes of the analysis. For example, a linear model is used for statistics in limma, while the negative binomial distribution is used in edgeR and DESeq2. Additionally, the normalized RNA-seq count data is necessary for EdgeR and limma but is not necessary for DESeq2. Here, we provide a detailed protocol for three differential analysis methods: limma, EdgeR and DESeq2. The results of the three methods are partly overlapping. All three methods have their own advantages, and the choice of method only depends on the data.