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.