Thank you very much for the previous email correspondence (see below) on FourCSeq package.
>Question: How do I bypass the counting procedure and precede the downstream statistical analysis in detecting interactions and differences?
>Your answer: You have to create the FourC Seq object as described in the vingette with colData and exptData. Then you skip all steps as digesting the reference and couning read. But you have to generate your GRanges object of the fragments yourself.
I have further questions regarding to the data analysis.
(1). In “getZScores” function: Prior to obtain VST count, the raw count is normalised by the sum of filtered read count (such as, minCount=40) but not total sequenced reads. Is it correct?
(2). How do we evaluate whether “distFitMonotoneSymmetric or distFitMonotone” is the choice of fitting? In addition, could you please advise me on how to perform the plotFit on “distFitMonotone” data (no issue with distFitMonotoneSymmetric using “plotFits(fcf[,1]" command)?
(3). In “getDifferences” function, it is not clear to me whether the calculation of normalisation factors is simply based on the raw count (back-transformed trend fitted value) or filtered normalised count (see 1). If the answer is raw count, shouldn’t we obtain differential interactions based on count normalised with both distance and sequencing depth?
Thank you very much for your help