Analysis of ChIP-seq Data Via Bayesian Finite Mixture Models with a Non-parametric Component [chapter]

Baba B. Alhaji, Hongsheng Dai, Yoshiko Hayashi, Veronica Vinciotti, Andrew Harrison, Berthold Lausen
2016 Studies in Classification, Data Analysis, and Knowledge Organization  
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 multi-modality of the
more » ... kelihood 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 demonstrate the efficacy of the methodology using a ChIPsequencing data set.
doi:10.1007/978-3-319-25226-1_43 fatcat:gdv56hy2frhu3l4q5hl5c45i3m