**0**wrote:

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?

**110**• written 5 weeks ago by adrija.kalvisa •

**0**

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.

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