Mixture model with multiple allocations for clustering spatially correlated observations in the analysis of ChIP-Seq data

TL;DR

This paper introduces a novel mixture model for clustering ChIP-Seq data that allows for multiple group memberships per observation, incorporates zero-inflation correction, and accounts for spatial dependencies, improving analysis of complex biological signals.

Contribution

It proposes a new mixture model with multiple allocations, zero-inflation correction, and spatial dependency modeling specifically designed for ChIP-Seq data analysis.

Findings

Model effectively captures multiple group memberships.

Incorporates zero-inflation to handle excess zeros.

Demonstrates improved clustering performance on real data.

Abstract

Model-based clustering is a technique widely used to group a collection of units into mutually exclusive groups. There are, however, situations in which an observation could in principle belong to more than one cluster. In the context of Next-Generation Sequencing (NGS) experiments, for example, the signal observed in the data might be produced by two (or more) different biological processes operating together and a gene could participate in both (or all) of them. We propose a novel approach to cluster NGS discrete data, coming from a ChIP-Seq experiment, with a mixture model, allowing each unit to belong potentially to more than one group: these multiple allocation clusters can be flexibly defined via a function combining the features of the original groups without introducing new parameters. The formulation naturally gives rise to a `zero-inflation group' in which values close to zero can be allocated, acting as a correction for the abundance of zeros that manifest in this type of data. We take into account the spatial dependency between observations, which is described through a latent Conditional Auto-Regressive process that can reflect different dependency patterns. We assess the performance of our model within a simulation environment and then we apply it to ChIP-seq real data.