I am trying to estimate the p-value weight settings by using ChicagoTools
fitDistCurve.R using replicates from my own samples as an input. I want to compare two treatments (three replicates per treatment), however, I am not sure whether I should calculate a separate weight adjustment for each treatment.
For comparison, I include the p-weight values from the built-in
GM12878 humanMacrophage mESC weightAlpha 29.138483 34.115730 18.259997 weightBeta -2.342790 -2.586881 -1.547562 weightGamma -17.108579 -17.134780 -17.357088 weightDelta -7.688056 -7.076092 -7.216534
.settings files, calculated from my own replicates:
TreatmentA TreatmebtB weightAlpha 24.486049 23.696801 weightBeta -1.978451 -1.931931 weightGamma -17.135199 -18.066895 weightDelta -6.737233 -7.222880
You can see that the weights of the p-values differ when generated for each treatment separately. I don t know whether the treatment can affect the "preferred" interaction distance (I would actually like to test this).
Taking all this into consideration, I have two questions:
1) Does it make sense to "pool" the samples from different treatments for p-value weight calculation using
fitDistCurve.R when I dont know if treatment affects differentiation state/cell type?
2) What is the expected similarity between the p-value weights when comparing two samples of the same tissue, species or treatment?