DSpace Repository

A Stochastic Segmentation Model for Categorical and Continuous Features of various biological sequential

Show simple item record

dc.contributor.advisor Zhang, Michael Q., Xing, Haipeng en_US
dc.contributor.author Mo, Yifan en_US
dc.contributor.other Department of Applied Mathematics and Statistics en_US
dc.date.accessioned 2013-05-22T17:35:17Z
dc.date.accessioned 2015-04-24T14:47:09Z
dc.date.available 2013-05-22T17:35:17Z
dc.date.available 2015-04-24T14:47:09Z
dc.date.issued 2012-12-01 en_US
dc.identifier Mo_grad.sunysb_0771E_11168 en_US
dc.identifier.uri http://hdl.handle.net/1951/59794 en_US
dc.identifier.uri http://hdl.handle.net/11401/71350 en_US
dc.description 107 pg. en_US
dc.description.abstract Nowadays, Hidden Markov Model (HMM) has been widely used in analysis of various biological data for both smoothing and clustering. However, characterizing each hidden state by a single distribution, the classical HMM might have some limitations on the data whose hidden state is composed by a mixture of distributions (Heng Lian et al., 2006). To address this issue, we proposed a new stochastic segmentation model and an associated estimation procedure that has attractive analytical and computational properties. We combined the forward and backward filter together based on Bayes' theorem to calculate the posterior mean and variance. Besides, we developed an expectation-maximization (EM) algorithm to estimate the hyper-parameters. Furthermore, we utilized a bounded complexity mixture (BCMIX) approximation whose computational complexity is linear in sequence length. Another important feature of this segmentation model is that it yields explicit formulas for posterior means and probability of categorical states, which can be used to make inference on both categorical and continuous aspects of the data. Other quantities relating to the posterior distribution that are useful for making confidence assessments of any given segmentation can also be estimated by using our method. We perform intensive simulation studies (1) to compare the Bayes and BCMIX estimates (2) to evaluate the BCMIX estimates in terms of sum square error, Kullback-Leibler divergence and the identification ratio of true segments. We also applied our model on two biological data sets: (1) reduced representation bisulfite sequencing (RRBS) data (A.Molaro et al., 2011) (2) ENCODE Nimblegen tilled arrays (Sabo et al., 2006). Our model shows good performance on segmentation of these two sequential data. In RRBS data it can further help identify differential methylation region (DMR) while in microarray data it can discover the DNAsel Hypersensitive Sites (DHSs). en_US
dc.description.sponsorship This work is sponsored by the Stony Brook University Graduate School in compliance with the requirements for completion of degree. en_US
dc.format Monograph en_US
dc.format.medium Electronic Resource en_US
dc.language.iso en_US en_US
dc.publisher The Graduate School, Stony Brook University: Stony Brook, NY. en_US
dc.subject.lcsh Statistics en_US
dc.title A Stochastic Segmentation Model for Categorical and Continuous Features of various biological sequential en_US
dc.type Dissertation en_US
dc.mimetype Application/PDF en_US
dc.contributor.committeemember Wu, Song en_US
dc.contributor.committeemember Fang, Yixin. en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account