DEseq2 均一化多个处理的样本

我要做一个分析，需要用均一化的数据，学生说之前是需要两两去做DEseq2，然后再做下游分析，我觉得太麻烦。于是摸索了下，DEseq2可以均一化多个样本。
代码如下：我这里是8个条件下的24个样本，大家根据自己的情况去修改：

1. 加载需要的相关包

  rm(list=ls())
  if(!require(DESeq2))BiocManager::install("DESeq2")
  library(rio)
  library(dplyr)

2. 导入数据

  data_all=import("exprpac.csv",header = T)

3.选取需要均一化的样本列，构建database

  database=data_all[,c(5:28)]
  head(database)
  database=database[complete.cases(database), ]

4. 构建factor,切忌名称中间不能添加如“_“ 等符号，否则会报错Error in DESeqDataSet(se, design = design, ignoreRank) : variables in design formula cannot contain NA: condition

 condition = factor(c(rep("56AA", 3), rep("56CK", 3), rep("col0AA", 3), rep("col0CK", 3), rep("30AA", 3), rep("30CK", 3), rep("30mycAA", 3), rep("30mycCK", 3)), levels = c("56AA","56CK","col0AA","col0CK","30AA","30CK","30mycAA","30mycCK"))

5. DEseq2均一化

  countdata = round(as.matrix(database))
  coldata = data.frame(row.names = colnames(countdata), condition)
  dds = DESeqDataSetFromMatrix(countdata, colData = coldata, design = ~ condition)
  dds = DESeq(dds)
  sizeFactors(dds)
  head(dds)
  res <- results(dds)
  resdata <- merge(as.data.frame(res), as.data.frame(counts(dds, 
  normalized=TRUE)),by="row.names",sort=FALSE)
  head(resdata)
  merge_list <- data.frame(data_all,resdata)
  head(merge_list)
  resdata <- merge_list
  head(resdata)

6.数据保存，因为我是用均一化的数据替代了原始的数据，所以，表格结构重新构造了下

 nor <- select(resdata,c(1:4,59:82),c(29:51))
 write.csv(nor,file = "DEseq2_tot_normalized.csv",row.names = FALSE)

*注，目前我的水平看起来后续做差异分析的时候只能两两做，不能够一次生成。如果有高手的有办法的话，欢迎指教！