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 .settings files:
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
My own .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?
•
written
by
adrija.kalvisa •
1 answer
It's a great question, and I'm not sure there's just one "correct" answer.
Weights are not required for the actual differential analysis package - Chicdiff - which only uses expected mean estimates from Chicago, and computes its own p-value weights (because the hypothesis tested by Chicdiff is not the same as in Chicago).
What the weights affect is the set of Chicago regions to be tested by Chicdiff, which are called separately for each condition and then combined to define the "peak file" for Chicdiff.
So one way to approach this is call these regions with the most optimal settings for each condition (i.e., potentially different weights).
It must be said though that in practice we often use the default weights, because they are trained on a very high-powered dataset, and it may be that they are simply more precise than those computed on custom data with fewer replicates.
Log in to answer this question.
Since Chicago is at BioC I suggest you post this over at their support page: https://support.bioconductor.org/
pcHiC is not super common and the user base here is therefore probably small.