TCGA分析-数据整理2
原创
title: "三大R包差异分析"
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1.三大R包差异分析
rm(list = ls())
load("GSE106899.Rdata")
table(group)
#> group
#> control tumor
#> 22 44
#deseq2----
library(DESeq2)
colData <- data.frame(row.names =colnames(exp6),
condition=group)
if(!file.exists(paste0(proj,"_dd.Rdata"))){
dds <- DESeqDataSetFromMatrix(
countData = exp6,
colData = colData,
design = ~ condition)
dds <- DESeq(dds)
save(dds,file = paste0(proj,"_dd.Rdata"))
}
load(file = paste0(proj,"_dd.Rdata"))
class(dds)
#> [1] "DESeqDataSet"
#> attr(,"package")
#> [1] "DESeq2"
res <- results(dds, contrast = c("condition",rev(levels(group))))
#constrast
c("condition",rev(levels(group)))
#> [1] "condition" "tumor" "control"
class(res)
#> [1] "DESeqResults"
#> attr(,"package")
#> [1] "DESeq2"
DEG1 <- as.data.frame(res)
library(dplyr)
DEG1 <- arrange(DEG1,pvalue)
DEG1 = na.omit(DEG1)
head(DEG1)
#> baseMean log2FoldChange lfcSE stat pvalue
#> Pfn2 57.60350 2.684809 0.4787181 5.608330 2.042883e-08
#> Slc2a1 49.24526 2.384014 0.4511164 5.284698 1.259126e-07
#> Sfrp2 27.72403 2.623599 0.5023184 5.222981 1.760653e-07
#> Marcksl1 10.95315 2.349047 0.4500747 5.219238 1.796607e-07
#> Ddit4 12.19504 1.611006 0.3203432 5.029000 4.930432e-07
#> Bcl11a 71.45106 2.362877 0.4746990 4.977632 6.436673e-07
#> padj
#> Pfn2 9.979483e-05
#> Slc2a1 2.194106e-04
#> Sfrp2 2.194106e-04
#> Marcksl1 2.194106e-04
#> Ddit4 4.817032e-04
#> Bcl11a 5.240524e-04
#添加change列标记基因上调下调
logFC_t = 0.585
pvalue_t = 0.05
k1 = (DEG1$pvalue < pvalue_t)&(DEG1$log2FoldChange < -logFC_t);table(k1)
#> k1
#> FALSE TRUE
#> 4759 126
k2 = (DEG1$pvalue < pvalue_t)&(DEG1$log2FoldChange > logFC_t);table(k2)
#> k2
#> FALSE TRUE
#> 4256 629
DEG1$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
table(DEG1$change)
#>
#> DOWN NOT UP
#> 126 4130 629
head(DEG1)
#> baseMean log2FoldChange lfcSE stat pvalue
#> Pfn2 57.60350 2.684809 0.4787181 5.608330 2.042883e-08
#> Slc2a1 49.24526 2.384014 0.4511164 5.284698 1.259126e-07
#> Sfrp2 27.72403 2.623599 0.5023184 5.222981 1.760653e-07
#> Marcksl1 10.95315 2.349047 0.4500747 5.219238 1.796607e-07
#> Ddit4 12.19504 1.611006 0.3203432 5.029000 4.930432e-07
#> Bcl11a 71.45106 2.362877 0.4746990 4.977632 6.436673e-07
#> padj change
#> Pfn2 9.979483e-05 UP
#> Slc2a1 2.194106e-04 UP
#> Sfrp2 2.194106e-04 UP
#> Marcksl1 2.194106e-04 UP
#> Ddit4 4.817032e-04 UP
#> Bcl11a 5.240524e-04 UP
#edgeR----
library(edgeR)
dge <- DGEList(counts=exp6,group=group)
dge$samples$lib.size <- colSums(dge$counts)
dge <- calcNormFactors(dge)
design <- model.matrix(~group)
dge <- estimateGLMCommonDisp(dge, design)
dge <- estimateGLMTrendedDisp(dge, design)
dge <- estimateGLMTagwiseDisp(dge, design)
fit <- glmFit(dge, design)
fit <- glmLRT(fit)
DEG2=topTags(fit, n=Inf)
class(DEG2)
#> [1] "TopTags"
#> attr(,"package")
#> [1] "edgeR"
DEG2=as.data.frame(DEG2)
head(DEG2)
#> logFC logCPM LR PValue FDR
#> Errfi1 -1.978325 0.7286484 27.51085 1.562158e-07 0.0008635609
#> Tiparp -1.866452 0.2415403 24.49273 7.459089e-07 0.0015882179
#> Ddit4 1.520452 1.3282727 24.17949 8.776197e-07 0.0015882179
#> Bcl11a 2.363640 3.7313587 23.11293 1.527598e-06 0.0015882179
#> Sfrp2 2.595633 2.4405642 22.21239 2.440938e-06 0.0015882179
#> Mcm2 4.686831 1.2813389 22.13340 2.543453e-06 0.0015882179
k1 = (DEG2$PValue < pvalue_t)&(DEG2$logFC < -logFC_t)
k2 = (DEG2$PValue < pvalue_t)&(DEG2$logFC > logFC_t)
DEG2$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
head(DEG2)
#> logFC logCPM LR PValue FDR
#> Errfi1 -1.978325 0.7286484 27.51085 1.562158e-07 0.0008635609
#> Tiparp -1.866452 0.2415403 24.49273 7.459089e-07 0.0015882179
#> Ddit4 1.520452 1.3282727 24.17949 8.776197e-07 0.0015882179
#> Bcl11a 2.363640 3.7313587 23.11293 1.527598e-06 0.0015882179
#> Sfrp2 2.595633 2.4405642 22.21239 2.440938e-06 0.0015882179
#> Mcm2 4.686831 1.2813389 22.13340 2.543453e-06 0.0015882179
#> change
#> Errfi1 DOWN
#> Tiparp DOWN
#> Ddit4 UP
#> Bcl11a UP
#> Sfrp2 UP
#> Mcm2 UP
table(DEG2$change)
#>
#> DOWN NOT UP
#> 217 4735 576
#limma----
library(limma)
dge <- edgeR::DGEList(counts=exp6)
dge <- edgeR::calcNormFactors(dge)
design <- model.matrix(~group)
v <- voom(dge,design, normalize="quantile")
fit <- lmFit(v, design)
fit= eBayes(fit)
DEG3 = topTable(fit, coef=2, n=Inf)
DEG3 = na.omit(DEG3)
k1 = (DEG3$P.Value < pvalue_t)&(DEG3$logFC < -logFC_t)
k2 = (DEG3$P.Value < pvalue_t)&(DEG3$logFC > logFC_t)
DEG3$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
table(DEG3$change)
#>
#> DOWN NOT UP
#> 217 5019 292
head(DEG3)
#> logFC AveExpr t P.Value adj.P.Val
#> Ddit4 1.486366 0.7589110 5.020394 2.727289e-06 0.01507645
#> Six1 1.934545 1.0338629 4.768701 7.426821e-06 0.01759464
#> Col1a2 1.865652 2.0734033 4.704515 9.548468e-06 0.01759464
#> Klf6 -1.236666 4.9984036 -4.621729 1.316885e-05 0.01795344
#> Trp53inp2 -1.298168 -1.1309887 -4.567344 1.623864e-05 0.01795344
#> Trib2 1.265511 -0.2644945 4.351003 3.687606e-05 0.03397515
#> B change
#> Ddit4 4.321273 UP
#> Six1 3.450022 UP
#> Col1a2 3.208490 UP
#> Klf6 2.972140 DOWN
#> Trp53inp2 2.789282 DOWN
#> Trib2 2.067142 UP
tj = data.frame(deseq2 = as.integer(table(DEG1$change)),
edgeR = as.integer(table(DEG2$change)),
limma_voom = as.integer(table(DEG3$change)),
row.names = c("down","not","up")
);tj
#> deseq2 edgeR limma_voom
#> down 126 217 217
#> not 4130 4735 5019
#> up 629 576 292
save(DEG1,DEG2,DEG3,group,tj,file = paste0(proj,"_DEG.Rdata"))2.可视化
library(ggplot2)
library(tinyarray)
exp6[1:4,1:4]
#> SVA492 SVA493 SVA494 SVA495
#> Itm2a 0 3 0 0
#> Dhx9 0 43 0 7
#> Ssu72 7 49 6 1
#> Mks1 0 3 0 0
# cpm 去除文库大小的影响
dat = log2(cpm(exp6)+1)
pca.plot = draw_pca(dat,group);pca.plot
save(pca.plot,file = paste0(proj,"_pcaplot.Rdata"))
cg1 = rownames(DEG1)[DEG1$change !="NOT"]
cg2 = rownames(DEG2)[DEG2$change !="NOT"]
cg3 = rownames(DEG3)[DEG3$change !="NOT"]
h1 = draw_heatmap(dat[cg1,],group)
h2 = draw_heatmap(dat[cg2,],group)
h3 = draw_heatmap(dat[cg3,],group)
v1 = draw_volcano(DEG1,pkg = 1,logFC_cutoff = logFC_t)
v2 = draw_volcano(DEG2,pkg = 2,logFC_cutoff = logFC_t)
v3 = draw_volcano(DEG3,pkg = 3,logFC_cutoff = logFC_t)
library(patchwork)
(h1 + h2 + h3) / (v1 + v2 + v3) +plot_layout(guides = 'collect') &theme(legend.position = "none")
ggsave(paste0(proj,"_heat_vo.png"),width = 15,height = 10)3.三大R包差异基因对比
UP=function(df){
rownames(df)[df$change=="UP"]
}
DOWN=function(df){
rownames(df)[df$change=="DOWN"]
}
up = intersect(intersect(UP(DEG1),UP(DEG2)),UP(DEG3))
down = intersect(intersect(DOWN(DEG1),DOWN(DEG2)),DOWN(DEG3))
dat = log2(cpm(exp6)+1)
dat=as.data.frame(dat)
hp = draw_heatmap(dat[c(up,down),],group)
#上调、下调基因分别画维恩图
up_genes = list(Deseq2 = UP(DEG1),
edgeR = UP(DEG2),
limma = UP(DEG3))
down_genes = list(Deseq2 = DOWN(DEG1),
edgeR = DOWN(DEG2),
limma = DOWN(DEG3))
up.plot <- draw_venn(up_genes,"UPgene")
down.plot <- draw_venn(down_genes,"DOWNgene")
#维恩图拼图，终于搞定
library(patchwork)
#up.plot + down.plot
# 拼图
pca.plot + hp+up.plot +down.plot+ plot_layout(guides = "collect")
ggsave(paste0(proj,"_heat_ve_pca.png"),width = 15,height = 10)
分组聚类的热图
library(ComplexHeatmap)
library(circlize)
col_fun = colorRamp2(c(-2, 0, 2), c("#2fa1dd", "white", "#f87669"))
top_annotation = HeatmapAnnotation(
cluster = anno_block(gp = gpar(fill = c("#f87669","#2fa1dd")),
labels = levels(group),
labels_gp = gpar(col = "white", fontsize = 12)))
m = Heatmap(t(scale(t(exp6[c(up,down),]))),name = " ",
col = col_fun,
top_annotation = top_annotation,
column_split = group,
show_heatmap_legend = T,
border = F,
show_column_names = F,
show_row_names = F,
use_raster = F,
cluster_column_slices = F,
column_title = NULL)
m
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原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
原创声明：本文系作者授权腾讯云开发者社区发表，未经许可，不得转载。
如有侵权，请联系 cloudcommunity@tencent.com 删除。
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