This is regarding the single factor design For example if I have Age or other continuous numerical variable how to provide that into the design formula.

For this post do i need to 'You could dichotomise your continuous variables into meaningful groups' or it can go without it? grouping numerical variable is needed prior to running it in deseq2 because here each age becomes a factor if I get it

Here in case of metadata/coldata I m giving a single numerical value which is Blast percentage. In case of sample I have like 5 sub-types from M0 to M5.

Now for the interpretation part How do I interpret the result?

Would it be as such

the expression differences between the my subtypes(samples) due to 'Age' or what would be my statistical way to convey the result

I'm bit confused since in my coldata I'm not providing any information regarding my subtypes.

So if I would like to know if there is a difference between which sub-type due to this Age variable how do i get that information?

To know the differences in sub-type I have do which is providing the FAB which are basically my sub-types information where I have tested pairwise.

I would like to know if I give any numerical variable to my design how do I interpret output? the gene expression?

Any suggestion or help would be really appreciated.

My design

  dds <- DESeqDataSetFromMatrix(countData=data, colData=coldata, design= ~ Age)

    dds <- estimateSizeFactors(dds)

    dds <- estimateDispersions(dds)

    dds  <- DESeq(dds, parallel = TRUE)
 resultsNames(dds)  I get this 


    [1] "Intercept"     "Age"

sessionInfo( )

From what you have said, it's likely you are not specifying your model correctly. Right now you are simply testing for genes that change expression as a function of age (in which case you would interpret the beta for any significant genes as the log fold change for each unit change in age. As an example, if you used age in years and you had a beta of say 1, that would mean the gene expression doubles for every yearly increase in age). But you talk about a blast percentage, which I take to be the phenotype or treatment of interest.

If you are actually trying to find genes that change due to the blast percentage, you need to add that to the design. You can keep age, which will then adjust for changes due to age (a nusiance parameter that you think might affect the gene expression, but you don't really care about). If what I am saying is confusing, then you should either read (and re-read) the DESeq2 vignette, and probably the workflow as well. Or maybe find somebody local with experience and have them help or do the analysis for you.