Name: three differential expression analysis methods for rna sequencing: limma, edger, deseq2
Uploaded: 2021-09-18T14:00:00
Description: Watch this Scientific Journal Video about Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2 at JoVE.com

This work was supported by the&#8194;National&#8194;Natural&#8194;Science&#8194;Foundation&#8194;of&#8194;China&#8194;(Grant&#8194;No. 81860276) and Key Special Fund Projects of National Key R&#38;D Program (Grant&#8194;No. 2018YFC1003200).

NOTE: Open the R-studio program and load R file &#34;DEGs.R&#34;, the file can be acquired from Supplementary files/Scripts.
1. Downloading and pre-processing of data
<ol>
	<li>Download the high-throughput sequencing (HTSeq) count data of cholangiocarcinoma (CHOL) from The Cancer Genome Atlas (TCGA). This step can be easily achieved by the following R code.
	<ol>
		<li>Click Run to install R packages.</li>
		<li>Click Run to load R packages. 
		if(!requ...

<ol>
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K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=edgeR:+a+Bioconductor+package+for+differential+expression+analysis+of+digital+gene+expression+data.">edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.</a> Bioinformatics. 26, 139-140 (2010).</li><li>Love, M. I., Huber, W., Anders, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Moderated+estimation+of+fold+change+and+dispersion+for+RNA-seq+data+with+DESeq2.">Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.</a> Genome Biology. 15, 550 (2014).</li><li>Gentleman, R. 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Abundant aberrate transcripts in cancers can be easily identified by RNA-seq differential analysis5. However, the application of RNA-seq differential expression analysis is often restricted as it requires certain skills with R language and the capacity to choose appropriate methods. To address this problem, we provide a detailed introduction to the three most known methods (limma, EdgeR and DESeq2) and tutorials for applying the RNA-seq differential expression analysis. This will facilitate the un...

RNA-sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics with many advantages (e.g., high data reproducibility), and has dramatically increased our understanding of the functions and dynamics of complex biological processes1,2. Identification of aberrate transcripts under different biological context, which are also known as differentially expressed genes (DEGs), is a key step in RNA-seq analysis. RNA-seq makes it possible to get a deep understanding of pathogenesis related molecular mechanisms and biological functions. Therefore, differential analysis has been regarded as valuable for diagnostics, prognostics and therapeutics of tumors3,4,5. Currently, more open-source R/Bioconductor packages have been developed for RNA-seq differential expression analysis, particularly limma, DESeq2 and EdgeR1,6,7. However, differential analysis requires certain skills with R language and the ability to choose the appropriate method, which is lacking in the curriculum of medical education.
In this protocol, based on the cholangiocarcinoma (CHOL) RNA-seq count data extracted from The Cancer Genome Atlas (TCGA), three of the most known methods (limma8, EdgeR9 and DESeq210) were carried out, respectively, by the R program11 to identify the DEGs between CHOL and normal tissues. The three protocols of limma, EdgeR and DESeq2 are similar but have different steps among the processes of the analysis. For example, the normalized RNA-seq count data is necessary for EdgeR and limma8, 9, whereas DESeq2 uses its own library discrepancies to correct data instead of normalization10. Furthermore, edgeR is specifically suitable for RNA-seq data, while the limma is used for microarrays and RNA-seq. A linear model is adopted by limma to assess the DEGs12, while the statistics in edgeR are based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests9.
In summary, we provide the detailed protocols of RNA-seq differential expression analysis by using limma, DESeq2 and EdgeR, respectively. By referring to this article, users can easily perform the RNA-seq differential analysis and choose the appropriate differential analysis methods for their data.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>R</td>
			<td></td>
			<td>version 3.6.2</td>
			<td>free software</td>
		</tr>
		<tr>
			<td>Rstudio</td>
			<td></td>
			<td></td>
			<td>free software</td>
		</tr>
	</tbody>
</table>

three differential expression analysis methods for rna sequencing: limma, edger, deseq2

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.

There are various approaches to visualize the result of differential expression analysis, among which the volcano plot and Venn diagram are particularly used. limma identified 3323 DEGs between the CHOL and normal tissues with the |logFC|&#8805;2 and adj.P.Val &#60;0.05 as thresholds, among which 1880 were down-regulated in CHOL tissues and 1443 were up-regulated (Figure 1a). Meanwhile, edgeR identified the 1578 down-regulated DEGs and 3121 up-regulated DEGs (Figure 1b</...

Watch this Scientific Journal Video about Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2 at JoVE.com

Three Differential Expression Analysis Methods for RNA Sequencing: limma, EdgeR, DESeq2

A detailed protocol of differential expression analysis methods for RNA sequencing was provided: limma, EdgeR, DESeq2.

RNA sequencing (RNA-seq) is one of the most widely used technologies in transcriptomics as it can reveal the relationship between the genetic alteration ...

Three differential expression analysis methods for RNA sequencing:limma, EdgeR, and DESeq2. Open the RStudio program and then load R file, DEGs. The file can be acquired from supplementary files.One.Downloading and pre-processing of data.1.1. Download the high-throughput sequencing count data of Cholangiocarcinoma from the Cancer Genome Atlas. This tab can easily achieved by the following code.Click run to install the R package. Click run to load R package. Set working directory.Choose the cancer type. Run R code from the GDCquery file to download the data. File GDCquery can be acquired from supplementary files/scripts.After execution, the Cholangiocarcinoma RNA sequencing count data can be downloaded and named CNT, where rows represent ensemble gene IDs and columns represent symbols IDs. Please notice the numbers at position 14 to 15 in the symbols IDs. Numbers range from 01 to 09 indicate tumors and 10 to 19 indicate normal tissues.1.2.Conversation of ensemble gene IDs to gene symbols. Import the annotation file into R, according to its'storage path. The annotation file can be acquired from supplementary files.Run the R code from the gtf v22 file. Which can be acquired from supplementary files/scripts. Apply inn"function and to convert the ensemble gene IDs to gene symbols.1.3.Filter low-expressed genes. Click run to install package edgeR"Click run to load the R package edgeR"Run following R code to keep genes with counts per million values greater than one in at least two samples.Two. Differential expression analysis through limma"Click Run to install R package limma"Click Run to load R package limma"edgeR"Run the following R code to create design matrix.Extract group information. Set 01"as tumor tissue. Set 11"as normal tissue.Create design matrix. Create the DGEList object. Normalize the data.Run the following R code to perform the limma-trend method based differential expression analysis. Calculate the CPM value. Click Run to fit a linear model to predict the data or infer the relationship between variables.Calculate T value, F value and the log-odds based on Bayesian. Extract the results table. The results of differential expression analysis is saved in res_limma"which includes the log2 fold change value.The average log2 expression level of the gene in the experiment. The modified T statistic, P value, the false discovery rate corrected p value and the log-odds of differentially expressed genes. Identify the differentially expressed genes.So the adjusted P value less than 0.05, and absolute value of log false change greater than or equal to two are thresholds to screen the differentially expressed genes. The results res limma shows that comparing with the normal tissues, 1, 443 genes are up-regulated, and 1, 880 genes are down-regulated in Cholangiocarcinoma tissues. Output the result table to a file.Click Run to install R package ggplot2"Click Run to load R package ggplot2"Run R code from the volcano file to create the volcano plot and the file volcano can be acquired from supplementary files. Genes can be mapped to different positions according to their log2 fold change and adjusted P values. So up-regulated differentially expressed genes are colored in red.and the down-regulated differentially expressed genes are colored in green. Click export"to save the volcano plot.Three. Differential expression analysis through edgeR"Click Run to load R package edgeR"Run the following R code to create design matrix.Click Run to create the DGEList object and normalize the data. Click Run to estimate the dispersion of gene expression value. Click Run to fit model to count data.Conduct statistical test. Extract the result table. The result is saved in res edgeR"which includes the log fold change value, logCPM, F, p value and the false discovery rate corrected p value.Identify the differentially expressed genes. The result res edgeR"shows that comparing with the normal tissues, 3, 121 genes are up-regulated, and 1, 578 genes are down-regulated in Cholangiocarcinoma tissues. Output the result table to a file.Create the volcano plot. Click export to save the volcano plot.Four. Differential expression analysis through DESeq2.Click Run to install R package DESeq2"Click Run to load R package DESeq2"Run the following R code to determine the groping factor. Create the DESeq2 data set object. Perform analysis.Generate the result table. The result is saved in res DESeq2, which includes the mean of the normalized read count, log fold change value, log fold change standard arrow, the weld statistic, original P value and the corrected P value. Identify DEGs.The result res DESeq2 shows that comparing with the normal tissues, two thousand nine hundred and thirty eight genes are up-regulated, and one thousand six hundred and sixteen genes are down-regulated in Cholangiocarcinoma tissues. Output the result table to a file. Create the volcano plot.Click export to save the volcano plot.Five. Venn diagram. Click Run to install the R package venn diagram.Click Run to load the R package venn diagram. Make a venn diagram of up-regulated differentially expressed genes. Click export to save the van diagram, Make a venn diagram of down-regulated differentially expressed genes.Click export to save the venn diagram.Six. Representative results. Figure one shows the volcano plots of all genes acquired by limma, edgeR and DESeq2.Negative log p value is plotted against the log fold change. Red points represent the up-regulated differentially expressed genes, and the green points represent the down-regulated differentially expressed genes. Limma identifies the one thousand eight hundred and eighty down-regulated differentially expressed the genes, and the one thousand four hundred and forty three up-regulated differentially expressed genes in Cholangiocarcinoma tissues.EdgeR identify the one thousand five hundred seventy eight down-regulated differentially expressed genes, and three thousand one hundred and twenty one up-regulated differentially expressed genes. DESeq2 identify one thousand six hundred and sixteen down-regulated differentially expressed genes, and two thousand nine hundred and thirty eight up-regulated differentially expressed genes. Figure two, venn diagrams show overlap among the results divide from limma edgeR and DESeq2.Compare the results of these three methods, One thousand four hundred and thirty one up-regulated differentially expressed genes, and one thousand five hundred and thirty one down-regulated differentially expressed genes are overlapping.Seven.Conclusion. In this protocol, we provided here a detailed protocol of different types of measure analysis for a high sequence of count data by using R packages, limma, edgeR, and DESeq2. Three methods have similar and staffs among their process of their analysis.And then their from those three medicines are partly overlapping. All three medicines have their own advantages. And the choice is just depends on time of your data.If there is my current data, limma should be given with priority, but generation sequencing data, in edgeR, and DESeq2 are preferred.

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