差异分析我一般只用DESeq2，所以写了个函数，把这个打包了

    需要的包

    1. library(DESeq2)
    2. library(ggplot2)
    3. library(ggrepel)
    4. library(pheatmap)

    打包函数如下 ↓

    1. DEG_DESeq2 = function(exp, group){
    2.   colData <- data.frame(row.names =colnames(exp), 
    3.                         condition=group)
    4.   dds <- DESeqDataSetFromMatrix(countData = exp,
    5.                                 colData = colData,
    6.                                 design = ~ condition)
    7.   dds <- DESeq(dds) #这一步比较慢
    8.   res <- results(dds, contrast = c("condition",rev(levels(group))))
    9.   resOrdered <- res[order(res$pvalue),]
    10.   DEG <- as.data.frame(resOrdered)
    11.   DEG = na.omit(DEG)
    13.   logFC_cutoff <- with(DEG,mean(abs(log2FoldChange)) + 2*sd(abs(log2FoldChange)) ) # logFC 可以改
    14.   k1 = (DEG$pvalue < 0.05)&(DEG$log2FoldChange < -logFC_cutoff)
    15.   k2 = (DEG$pvalue < 0.05)&(DEG$log2FoldChange > logFC_cutoff)
    16.   DEG$change = ifelse(k1,"DOWN",ifelse(k2,"UP","NOT"))
    17.   write.csv(DEG, file = 'outport/DEG_DESeq2.csv') # 保存路径，也可以改
    19. }

    需要准备俩东西

    • 表达矩阵 exp（counts）
    • 分组信息 group

    开跑 ↓

    1. DEG_DESeq2(exp, group)
    2. DEG = read.csv('outport/DEG_DESeq2.csv')
    3. table(DEG$change)

    代码说明：DEG_DESeq2函数里有俩地方可以改（代码里注释了）

    • logFC_cutoff <- with(DEG,mean(abs(log2FoldChange)) + 2*sd(abs(log2FoldChange)) )
      这一句是log_FC的值，可以改
    • write.csv(DEG, file = ‘outport/DEG_DESeq2.csv’) # 保存路径，也可以改
      这一步是保存位置，我建了outport这个文件夹，如果没有建，可能会报错

    箱图可视化（一些代码要根据数据改）

    1. logFC_cutoff <- with(DEG,mean(abs(log2FoldChange)) + 2*sd(abs(log2FoldChange)) )
    2. DEG$label = ifelse(DEG$X == 'HMOX1', 'HMOX1', '') # 这里是你要研究的基因
    4. p1 = ggplot(DEG, aes(x=log2FoldChange, y=-log10(pvalue),color=change))+ 
    5.   geom_point(alpha=0.5, size=1.8)+
    6.   theme_set(theme_set(theme_bw(base_size=20)))+ 
    7.   xlab("log2FC") + ylab("-log10(pvalue)")+
    8.   scale_colour_manual(values = c("#377EB8",'grey', "#E41A1C"))+
    9.   theme_classic()+
    10.   theme(panel.border = element_rect(fill=NA,color="black", size=1, linetype="solid"))+
    11.   geom_hline(aes(yintercept = -log10(0.05)), linetype = 'dashed', size = 1)+
    12.   geom_vline(aes(xintercept =  logFC_cutoff), linetype = 'dashed',size = 1)+
    13.   geom_vline(aes(xintercept =  -logFC_cutoff), linetype = 'dashed', size = 1)+
    14.   geom_label_repel(aes(label = label))
    15. p1

    热图可视化（一些代码要根据数据改）

    1. gene = c(DEG[DEG$change == 'UP','X'], DEG[DEG$change == 'DOWN','X'])
    2. data = log2(edgeR::cpm(exp)+1)[gene,]
    4. bk = c(seq(-1,-0.1,by=0.01),seq(0,1,by=0.01))
    5. annotation_col = data.frame(risk = rep(c('HighRisk','LowRisk'), c(80,80)))
    6. rownames(annotation_col) = rownames(cl)
    7. ann_colors = list(
    8.   risk = c(HighRisk = "#E41A1C", LowRisk = "#377EB8")
    9. )
    11. p2 = pheatmap(data,
    12.              scale = "row",
    13.              show_colnames =F,
    14.              show_rownames = F, 
    15.              cluster_cols = F,
    16.              cluster_rows = F,
    17.              color = c(colorRampPalette(colors = c("#377EB8","white"))(length(bk)/2),
    18.                        colorRampPalette(colors = c("white","#E41A1C"))(length(bk)/2)),
    19.              breaks=bk,
    20.              annotation_col = annotation_col,
    21.              annotation_colors = ann_colors,
    22.              gaps_row = 620,
    23.              gaps_col = 80)