deseq2

setwd('G:/博士/project_qu_chiip8/')
df <- read.table("rna_8.count",header = TRUE,row.names = 1) #读入数据文件


colnames(df)<-c('142','143','145','146','149','150')


#df_fh_x=df[,2:5]
#df_fh_y=df[, c(2,3,6,7)]


condition <- factor(c("control","control","control","case","case",'case'))
metadata <- data.frame(sample_id = colnames(df),
                       condition = condition
)


dds <- DESeqDataSetFromMatrix(df,  metadata, design= ~ condition )
head(dds)  #查
dds <- DESeq(dds)
resultsNames(dds)
res <- results(dds,contrast = c("condition","case","control"))
# summary一下，看一下结果的概要信息
summary(res)
# 获取padj（p值经过多重校验校正后的值）小于0.05，表达倍数取以2为对数后大于1或者小于-1的差异表达基因。
table(res$padj<0.05) #取P值小于0.05的结果
res <- res[order(res$padj),]
diff_df <-subset(res,padj < 0.05 & (log2FoldChange > 1 | log2FoldChange < -1))
diff_gene <- row.names(diff_df)

write.csv(diff_df,file= "diff_df_fdr005_fc2.csv",quote = FALSE)
write.csv(diff_gene,file= "diff_gene_fdr005_fc2.csv",quote = FALSE)


#heatmap of degene
name <- read.csv("fh_x_def_padj0.01_fc2.csv") 
rn=name[,1]

pheatmap::pheatmap(df[rn,],
                   fontsize = 8,
                   show_colnames =T,
                   show_rownames = F,
                   cluster_cols = T,
                
                   width = 8,
                   height = 6,
                   angle_col=45)
#----------pca