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

Ranciati, S., Viroli, C. & Wit, E. C. 30-Jun-2017 In : BIOMETRICAL JOURNAL.

Research output: Scientific - peer-reviewArticle

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

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DOI

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 autoregressive 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.

Original languageEnglish
JournalBIOMETRICAL JOURNAL
StateE-pub ahead of print - 30-Jun-2017

    Keywords

  • Journal Article

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