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.

Analysis of ChIP-seq data via Bayesian finite mixture models with a non-parametric component / Alhaji, B. B.; Dai, H.; Hayashi, Y.; Vinciotti, V.; Harrison, A.; Lausen, B.. - (2016), pp. 507-517. (Intervento presentato al convegno 2nd European Conference on Data Analysis, ECDA 2014 tenutosi a Bremen nel 2014) [10.1007/978-3-319-25226-1_43].

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

Vinciotti V.;
2016-01-01

Abstract

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.
2016
Studies in Classification, Data Analysis, and Knowledge Organization
HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
Kluwer Academic Publishers
978-3-319-25224-7
978-3-319-25226-1
Alhaji, B. B.; Dai, H.; Hayashi, Y.; Vinciotti, V.; Harrison, A.; Lausen, B.
Analysis of ChIP-seq data via Bayesian finite mixture models with a non-parametric component / Alhaji, B. B.; Dai, H.; Hayashi, Y.; Vinciotti, V.; Harrison, A.; Lausen, B.. - (2016), pp. 507-517. (Intervento presentato al convegno 2nd European Conference on Data Analysis, ECDA 2014 tenutosi a Bremen nel 2014) [10.1007/978-3-319-25226-1_43].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/285039
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