This is kind of a continuation from my previous question about using both spike-in normalization and input normalization in differential binding analysis (https://www.biostars.org/p/203724/).
I am also posting this question at the Bioconductor forum (https://support.bioconductor.org/p/85810/).
I read through the other posts on the Bioconductor forum about using RNA-seq packages to analyze Chip-seq data (https://support.bioconductor.org/p/72098/). It seems like the general consensus is to just ignore the input control samples or build a black-list and only look at differential binding between IP samples.
If I do want to incorporate input control into my differential binding, is it valid to include that data as another factor in the design matrix and perform a difference of difference type analysis?
So for every library, I would have a "IP" factor with two levels (IP/Input) and also a "sample" factor with two levels for treatment and control.
I've been trying to learn more about R and this series of tutorials seems to talk about building difference of difference type contrast (http://genomicsclass.github.io/book/pages/interactions_and_contrasts.html ). I am not very well versed in R, would it be possible to make this kind of contrast? And would this type of contrast even be valid?