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.
dds <- DESeqDataSetFromMatrix(countData=data, colData=coldata, design= ~ Age) dds <- estimateSizeFactors(dds) dds <- estimateDispersions(dds) dds <- DESeq(dds, parallel = TRUE) resultsNames(dds) I get this  "Intercept" "Age" sessionInfo( )