My RNA-seq read count table consists of samples fro two tissues (root and shoot), different time-points (1hr, 3hr, 12hr, 1day etc). and stress and no-stress conditions all in one table.
For example: 

`root_ctrl_rep1     root_ctrl_rep2       root_1hr_rep1    root_1hr_rep2   root_3hr_rep1    root_3hr_rep2     shoot_ctrl_rep1      shoot_ctrl_rep2      shoot_1hr_rep1      shoot_1hr_rep2     shoot_3hr_rep1    shoot_3hr_rep2 ` 

As of now, I am interested only in a basic DEG analysis between the following pairs

`root_ctrl vs root_1hr`

`root_ctrl vs root_3hr`

`shoot_ ctrl vs shoot_1hr`

`shoot_ctrl vs shoot_3hr`

----and so on

How do go about this ?.

> library("DESeq2") 

 >count_table<-read.table("counts.txt", header=T, row.names=1, sep="\t")

>exp_design<-data.frame(row.names=colnames(count_table), condition = c("root_ctrl","root_1hr","root_6hr","root_12hr","shoot_ctrl","shoot_1hr","shoot_6hr","shoot_12hr","shoot_day1","root_ctrl","root_3hr","root_6hr","root_12hr","root_day1","shoot_ctrl","shoot_1hr","shoot_3hr","shoot_6hr","shoot_12hr","shoot_12hr","shoot_day1","root_1hr","root_3hr","root_day1","shoot_3hr","shoot_3hr"))

>conditions=exp_design$condition

> ds <- DESeqDataSetFromMatrix(countData=count_table, exp_design, formula(~condition))

I am not sure if the above is right and what to do next. Any suggestion is greatly appreciated. Thanks
You'll probably want `~0+condition`, since I'm not sure if DESeq2's extended model matrix will be used in your model or not (I presume not).

Then for each comparison: `res = results(ds, contrast=c("condition", "root_1hr", "root_ctrl"))`. Note that you'll want to include `lfcShrink()`, since that's not automatically done anymore.