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( )
```

If I understood correctly, you want to see the results, right?

And then just view the genes on a volcano plot or something like that.

I know the result but i was not clear about the interpretation because if i use that numerical variable which is turned into factor for example 20.2 and 20.3 would be different factors although they are still 20. So if i have a like a categorical variable say disease vs non disease there the interpretation is straight forward what would be the here ?that was my question

You can import age and not make it a factor.

yes in my metadata I checked its not turned into factor.. simple question what is the

`intercept`

in case of when I using age in my design ? is it the lowest age that is turned into reference levelIf you converted to a factor, the intercept is the reference level (you have to look at

`levels(age)`

to know what the reference is). If you use age as a continuous value, then the intercept is gene expression at birth.