3 main r packages of difference analysis : DESeq2, edgeR and limma

created at 07-11-2021 views: 4

Data needed for difference analysis: Expression Matrix and Grouping Information

TCGA data only needs to express the matrix, because the sample ID of TCGA is quite special. Whether the 14th and 15th digits of the sample ID are >=10 or <10, it represents whether the sample is a normal sample or a tumor sample.

The starting point of the three R packages for difference analysis is the count matrix (reads count matrix). The count matrix cannot be directly used for difference analysis, so the three R packages have their own processing methods for the count matrix.

If you can't get the count matrix

  • RSEM: available for all three packages
  • tpm: Use the limma package for difference analysis (as a last resort)
  • fpkm, rpkm: convert to tpm, use limma for difference analysis (as a last resort)

1. Use three major R packages for enrichment analysis

Ready to work


rm(list = ls())

1.1 DESeq2

Deseq2 analysis requires two steps:

  1. The DESeqDataSetFromMatrix() function is to generate the data type required by Deseq2 from the matrix;
  2. DESeq() for difference analysis

#Generate a data frame whose row name is the sample id and the grouping information is listed
colData <- data.frame(row.names =colnames(exp), 
dds <- DESeqDataSetFromMatrix(
    countData = exp,
    colData = colData, 

    design = ~ condition) 

dds <- DESeq(dds)  
save(dds,file = paste0(proj,"_dd.Rdata"))
  • countData = exp: Expression matrix
  • colData = colData: Express the correspondence between matrix column names and groupings
  • design = ~ condition): Experimental design, condition corresponds to the condition in colData, which is the grouping information
  • dds <- DESeq(dds): Difference analysis, although the generated dds is still a Deseq2 object, it can be easily converted into a data frame

Extract the difference expression matrix from the difference analysis result

load(file = paste0(proj,"_dd.Rdata"))

res <- results(dds, contrast = c("condition",rev(levels(Group))))
resOrdered <- res[order(res$pvalue),]
DEG <- as.data.frame(resOrdered) 
DEG = na.omit(DEG)
  • results(): function: Extract the differential expression results from dds, and the generated object is Deseq2, which can be converted into a data frame
  • The contrast parameter must be written in the following three-element vector format, and the order cannot be reversed (refer to the help document).
  • resOrdered <- res[order(res$pvalue),]: Sort by P value (only Deseq2 needs, limma and EdgeR will be sorted automatically)
  • DEG <- as.data.frame(resOrdered): Convert to data frame
  • DEG = na.omit(DEG): If this step is not present, NA will appear after calculation of some genes with very low expression levels, which will cause trouble for subsequent analysis and drawing

expression matrix

Add change column to mark genes up-regulated and down-regulated

#Set the threshold to mean+2sd
logFC_cutoff <- with(DEG,mean(abs(log2FoldChange)) + 2*sd(abs(log2FoldChange)) )

# [1] 3.909132
k1 = (DEG$pvalue < 0.05)&(DEG$log2FoldChange < -logFC_cutoff)
k2 = (DEG$pvalue < 0.05)&(DEG$log2FoldChange > logFC_cutoff)
DEG$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
# DOWN   NOT    UP 
#  783 28724   841 

DESeq2_DEG <- DEG #Backup Results
  • logFC_cutoff:The logFC_cutoff calculated by different R packages is different

1.2 edgeR

Perform differential expression analysis and extract differential expression matrix


#Enter expression matrix and grouping information data
dge <- DGEList(counts=exp,group=Group) 
dge$samples$lib.size <- colSums(dge$counts)
dge <- calcNormFactors(dge) 

#Writing or not writing 0+ is the same
design <- model.matrix(~0+Group) 

dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTrendedDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)

fit <- glmFit(dge, design)
fit2 <- glmLRT(fit, contrast=c(-1,1)) 
DEG=topTags(fit2, n=nrow(exp))
  • fit2 <- glmLRT(fit, contrast=c(-1,1)): There is a difference between contrast and DESeq2 here, here you only need to enter c(-1,1), -1 corresponds to normal, which is the control group, and 1 corresponds to tumor, which is the experimental group.

FDR is a kind of p.adj

Add change column to mark genes up-regulated and down-regulated

logFC_cutoff <- with(DEG,mean(abs(logFC)) + 2*sd(abs(logFC)) )
# [1] 4.290788
k1 = (DEG$PValue < 0.05)&(DEG$logFC < -logFC_cutoff)
k2 = (DEG$PValue < 0.05)&(DEG$logFC > logFC_cutoff)
DEG$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))

# DOWN   NOT    UP 
#  533 28643  1172 
edgeR_DEG <- DEG

1.3 limma


design <- model.matrix(~0+Group) # get data: Group

dge <- DGEList(counts=exp) #get data: exp
dge <- calcNormFactors(dge)

v <- voom(dge,design, normalize="quantile")
fit <- lmFit(v, design)
# Same as the above two packages, you need to explain who is better than whom
constrasts = paste(rev(levels(Group)),collapse = "-") 
# [1] "tumor-normal"
cont.matrix <- makeContrasts(contrasts=constrasts,levels = design) 

DEG = topTable(fit2, coef=constrasts, n=Inf)
DEG = na.omit(DEG)


logFC_cutoff <- with(DEG,mean(abs(logFC)) + 2*sd(abs(logFC)) )
k1 = (DEG$P.Value < 0.05)&(DEG$logFC < -logFC_cutoff)
k2 = (DEG$P.Value < 0.05)&(DEG$logFC > logFC_cutoff)
DEG$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
# DOWN   NOT    UP 
# 1060 28915   373 
limma_voom_DEG <- DEG
tj = data.frame(deseq2 = as.integer(table(DESeq2_DEG$change)),
           edgeR = as.integer(table(edgeR_DEG$change)),
           limma_voom = as.integer(table(limma_voom_DEG$change)),
           row.names = c("down","not","up")
save(DESeq2_DEG,edgeR_DEG,limma_voom_DEG,Group,tj,file = paste0(proj,"_DEG.Rdata"))

1.4 compare

Check how many up-regulated and down-regulated genes are obtained in each of the 3 R packages

tj = data.frame(deseq2 = as.integer(table(DESeq2_DEG$change)),
                edgeR = as.integer(table(edgeR_DEG$change)),
                limma_voom = as.integer(table(limma_voom_DEG$change)),
                row.names = c("down","not","up")
#      deseq2 edgeR limma_voom
# down    783   533       1060
# not   28724 28643      28915
# up      841  1172        373

The results are still quite different, but there is no right or wrong, but the algorithm is different.

2. Visualization of difference analysis results

rm(list = ls())
# install tinyarray
if(!require(tinyarray))devtools::install_local("tinyarray-master.zip",upgrade = F) 

2.1 PCA diagram

The matrix here is still the count number, and the count matrix is only used for difference analysis

# cpm Remove the influence of library size
dat = log2(cpm(exp)+1) # Get the matrix used for drawing
pca.plot = draw_pca(dat,Group);pca.plot
save(pca.plot,file = paste0(proj,"_pcaplot.Rdata"))

pca graph made by cpm data

2.2 Heat map

Select the difference genes made by three R packages separately and draw a heat map

cg1 = rownames(DESeq2_DEG)[DESeq2_DEG$change !="NOT"]
cg2 = rownames(edgeR_DEG)[edgeR_DEG$change !="NOT"]
cg3 = rownames(limma_voom_DEG)[limma_voom_DEG$change !="NOT"]

h1 = draw_heatmap(dat[cg1,],Group,n_cutoff = 2) 
h2 = draw_heatmap(dat[cg2,],Group,n_cutoff = 2)
h3 = draw_heatmap(dat[cg3,],Group,n_cutoff = 2)
  • n_cutoff=2, the color distribution is -2 to 2, and the color beyond this range is the same as -2 or 2 to eliminate the influence of extreme values

2.3 Volcano map

Calculate the mean+2sd threshold (the first three R packages calculated are all called logFC\_cutoff, and they are not saved, recalculate here)

m2d = function(x){

v1 = draw_volcano(DESeq2_DEG,pkg = 1,logFC_cutoff = m2d(DESeq2_DEG$log2FoldChange))
v2 = draw_volcano(edgeR_DEG,pkg = 2,logFC_cutoff = m2d(edgeR_DEG$logFC))
v3 = draw_volcano(limma_voom_DEG,pkg = 3,logFC_cutoff = m2d(limma_voom_DEG$logFC))
  • pkg parameter:a integer ,means which Differential analysis packages you used,we support three packages by now, 1,2,3,4 respectively means DESeq2,edgeR,limma(voom),limma.

Image stitching

(h1 + h2 + h3) / (v1 + v2 + v3) +plot_layout(guides = 'collect') &theme(legend.position = "none")
ggsave(paste0(proj,"_heat_vo.png"),width = 15,height = 10)

Volcano map

3. Comparison of the difference genes of the three major R packages

rm(list = ls())
} #Select to increase ENSEMBLID
} # Select to downgrade ENSEMBLID

# Take the intersection of upregulated genes
up = intersect(intersect(UP(DESeq2_DEG),UP(edgeR_DEG)),UP(limma_voom_DEG)) 
# Remove the intersection of down-regulated genes
down = intersect(intersect(DOWN(DESeq2_DEG),DOWN(edgeR_DEG)),DOWN(limma_voom_DEG))

Draw a heat map of common difference genes

dat = log2(cpm(exp)+1)
hp = draw_heatmap(dat[c(up,down),],Group,n_cutoff = 2)

Draw Venn diagrams for up-regulated and down-regulated genes

The list accepted by the drawing function here must be a named list, and the name is the name of each category that will appear on the graph

up_genes = list(Deseq2 = UP(DESeq2_DEG),
          edgeR = UP(edgeR_DEG),
          limma = UP(limma_voom_DEG))
down_genes = list(Deseq2 = DOWN(DESeq2_DEG),
          edgeR = DOWN(edgeR_DEG),
          limma = DOWN(limma_voom_DEG))

# Quotation mark is the name of the figure
up.plot <- draw_venn(up_genes,"UPgene") 
down.plot <- draw_venn(down_genes,"DOWNgene")

Image stitching

#up.plot + down.plot
# Image stitching
pca.plot + hp+up.plot +down.plot+ plot_layout(guides = "collect")
ggsave(paste0(proj,"_heat_ve_pca.png"),width = 15,height = 10)

Image stitching

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