I'm guess that e.g. WA1 is replicate one of WA.  To get your analysis started, you'll at least need to separate out that using eg tidyr::extract(coldata, c("Tissue","Replicate", "(..)([123])")  . Then you'll probably need to clarify what exactly you mean by "between all tissue types" - do you want a genelist for each pair or tissues, or do you want a single one that lists genes that have at least one tissue that is different from the others; or comparisons against a common 'baseline' tissue.  (iii) is even more ambiguous: I think you'll only be able to use the common tissues, but do you want three lists of tissue-specific mutant vs wt, or genes that have a consistent mutant vs wt effect size across all tissues, or ...

You should also make your replication structure clear.  Is there a 'batch' effect in that WA1 is closely related to WB1 (e.g. from the same individual - possible), and MB1 (unlikely from same individual, but maybe from 'batch 1').  Currently there's not really enough detail in your question to allow us to answer it.

Michael Love

# I tried the following for (1):

dds4group <- dds
dds4group$group <- factor(paste0(dds4group$tissue, dds4group$condition))
design(dds4group) <- ~ group
dds4group_deseq <- DESeq(dds4group)

estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing

resultsNames(dds4group_deseq)
[1] "Intercept"  "group_Awild_vs_Amutant"   "group_Cmutant_vs_Amutant" "group_Cwild_vs_Amutant"  

[5] "group_Bmutant_vs_Amutant" "group_Bwild_vs_Amutant"   "group_Dwild_vs_Amutant"   "group_Ewild_vs_Amutant"

# It showed me the different combinations but the ones I want are not there:

"group_Awild_vs_Cwild"  
"group_Awild_vs_Bwild"
"group_Bwild_vs_Cwild"
"group_Dwild_vs_Ewild"

# Then I did:

dds4group2 <- dds
dds4group2$group2 <- factor(paste0(dds4group2$tissue, dds4group2$wild))
design(dds4group2) <- ~ group2
dds4group2_deseq <- DESeq(dds4group2)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
-- replacing outliers and refitting for 29 genes
-- DESeq argument 'minReplicatesForReplace' = 7 
-- original counts are preserved in counts(dds)
estimating dispersions
fitting model and testing
resultsNames(dds4group2_deseq)
[1] "Intercept"     "group2_C_vs_A" "group2_B_vs_A" "group2_D_vs_A" "group2_E_vs_A"

# Still  "group2_B_vs_C" and "group2_D_vs_E" are absent.

So I can use:

dds$condition <- factor(dds$condition, levels = c("wild","mutant"))

How can I keep replicate numbers also during analysis and heatmap so that I can see if there is any variation between replicates?

In (iii) I want to compare between different tissue types from mutant.

Thanks Michael Love. I cleaned my data as per your suggestions and ran DESeq2. Its awesome. :-)

I did:

dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~tissue + condition)
dds$condition <- factor(dds$condition, levels = c("wild","mutant"))
dds_deseq <- DESeq(dds)
res <- results(dds_deseq)

Now, I would like to do comparison between

1) "A" vs."B" tissues under "wild" condition

I tried the following:

res_ad <- results(dds_deseq, contrast=c("tissue","A","B"))
res_ad_0.05 <- subset(res_ad, res_ad$padj < 0.05)

But, it takes "A","B" from both "wild" and "mutant" while I want only for "wild".

2) Together AC vs. B ("AC" vs. "B"  tissues under "wild" condition)

I have already annotated my dataframe for all samples with ensembl and is there any possibility to use that dataframe for comparisons (e.g. to find list of differentially expressed genes for some samples?

Please guide. 

Thanks.