Hello,

I am new to mRNAseq data analysis and I would like to know how to run deseq2 for my data. (I googled it and I saw some posts like DESEq2 Paired samples Before and after treatment , but I didn't understand it. I am sorry to ask a basic question and take your time...)

Here is my data. (6 patient data and they are paired. They have the same disease and took the same drug. I would like to know different gene expression between before and after condition.)

> coldata
                           samples condition
1_3_12_clamp_0_buffy        1_3_12    before
1_3_12_clamp_200_buffy      1_3_12     after
9_clamp_0_buffy                  9    before
9_clamp_200_buffy                9     after
010_0_buffy                     10    before
010_200_buffy                   10     after
11_15_006_0m_buffy       11_15_006    before
11_15_006_200m_buffy     11_15_006     after
12_27_11_clamp_buffy_0    12_27_11    before
12_27_11_clamp_buffy_200  12_27_11     after
013_clamp_0m_buffy             013    before
013_clamp_200m_buffy           013     after

> head(txi.rsem$counts) # I removed some of columns for a better look.

I used the rsem output for tximport and ran deseq2 and I got this result. 

>ddsTxi <- DESeqDataSetFromTximport(txi.rsem, colData = coldata, design = ~samples + condition)
>dds <- ddsTxi[ rowSums(counts(ddsTxi)) > 1, ]
>dds <- DESeq(dds)
>res <- results(dds, pAdjustMethod = "fdr")
> res
log2 fold change (MLE): condition before vs after 
Wald test p-value: condition before vs after 
DataFrame with 26959 rows and 6 columns
                    baseMean log2FoldChange     lfcSE        stat    pvalue      padj
                   <numeric>      <numeric> <numeric>   <numeric> <numeric> <numeric>
ENSG00000000003    1.5229213     0.29551456 3.0266631  0.09763709 0.9222205        NA
ENSG00000000005    0.9538215     1.13808315 3.0745585  0.37016149 0.7112622        NA
ENSG00000000419  265.9276159     0.10175892 0.6761213  0.15050394 0.8803670 0.9987058
ENSG00000000457  230.5449617    -0.09454739 0.3944541 -0.23969175 0.8105692 0.9987058

So, I would like to know what I did is a right way of analysis my data?

Sorry for my English and thank you in advance.

JK

Yes, this is the recommendation in the FAQ in the DESeq2 vignette:

vignette(“DESeq2”)

Except you need to take care of factor levels of condition. See the Note on Factor Levels in the vignette.