Research Repository

Analysis of ChIP-seq data via Bayesian finite mixture models with a non-parametric component

Alhaji, BB and Dai, H and Hayashi, Y and Vinciotti, V and Harrison, A and Lausen, B (2016) Analysis of ChIP-seq data via Bayesian finite mixture models with a non-parametric component. In: UNSPECIFIED, ? - ?.

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© Springer International Publishing Switzerland 2016. In large discrete data sets which requires classification into signal and noise components, the distribution of the signal is often very bumpy and does not follow a standard distribution. Therefore the signal distribution is further modelled as a mixture of component distributions. However, when the signal component is modelled as a mixture of distributions, we are faced with the challenges of justifying the number of components and the label switching problem (caused by multimodality of the likelihood function). To circumvent these challenges, we propose a non-parametric structure for the signal component. This new method is more efficient in terms of precise estimates and better classifications. We demonstrated the efficacy of the methodology using a ChIP-sequencing data set.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Additional Information: Published proceedings: Studies in Classification, Data Analysis, and Knowledge Organization
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Mathematical Sciences, Department of
Depositing User: Jim Jamieson
Date Deposited: 05 Dec 2016 21:12
Last Modified: 22 Jan 2019 20:15

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