Joint modelling of ChIP-seq data via a Markov random field model

TL;DR

This paper introduces a Markov random field model for joint analysis of multiple ChIP-seq experiments, effectively capturing spatial dependencies and experimental design factors, leading to improved detection accuracy of protein binding sites.

Contribution

The proposed model uniquely integrates multiple experiments, replicates, and antibody differences, advancing ChIP-seq data analysis beyond existing methods.

Findings

Lower false non-discovery rate compared to existing methods

Effective modeling of spatial dependencies and zero-inflation

Successful application to real histone modification data

Abstract

Chromatin ImmunoPrecipitation-sequencing (ChIP-seq) experiments have now become routine in biology for the detection of protein binding sites. In this paper, we present a Markov random field model for the joint analysis of multiple ChIP-seq experiments. The proposed model naturally accounts for spatial dependencies in the data, by assuming first order Markov properties, and for the large proportion of zero counts, by using zero-inflated mixture distributions. In contrast to all other available implementations, the model allows for the joint modelling of multiple experiments, by incorporating key aspects of the experimental design. In particular, the model uses the information about replicates and about the different antibodies used in the experiments. An extensive simulation study shows a lower false non-discovery rate for the proposed method, compared to existing methods, at the same false discovery rate. Finally, we present an analysis on real data for the detection of histone modifications of two chromatin modifiers from eight ChIP-seq experiments, including technical replicates with different IP efficiencies.