# Posterior inference of Hi-C contact frequency through sampling

## TL;DR

HiCSampler improves Hi-C data by estimating the uncertainty in chromosomal interaction frequencies, enabling more reliable downstream analysis.

## Contribution

HiCSampler introduces a method to infer the posterior distribution of Hi-C interaction frequencies with uncertainty quantification.

## Key findings

- HiCSampler accurately predicts chromosomal interaction frequencies through posterior inference.
- Summary statistics from HiCSampler measure uncertainty in Hi-C experiments.
- HiCSampler samples are compatible with existing downstream analysis tools.

## Abstract

Hi-C is one of the most widely used approaches to study three-dimensional genome conformations. Contacts captured by a Hi-C experiment are represented in a contact frequency matrix. Due to the limited sequencing depth and other factors, Hi-C contact frequency matrices are only approximations of the true interaction frequencies and are further reported without any quantification of uncertainty. Hence, downstream analyses based on Hi-C contact maps (e.g., TAD and loop annotation) are themselves point estimations. Here, we present the Hi-C interaction frequency sampler (HiCSampler) that reliably infers the posterior distribution of the interaction frequency for a given Hi-C contact map by exploiting dependencies between neighboring loci. Posterior predictive checks demonstrate that HiCSampler can infer highly predictive chromosomal interaction frequency. Summary statistics calculated by HiCSampler provide a measurement of the uncertainty for Hi-C experiments, and samples inferred by HiCSampler are ready for use by most downstream analysis tools off the shelf and permit uncertainty measurements in these analyses without modifications.