alexa A Stochastic Segmentation Model for Recurrent Copy Number Alteration Analysis
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

Like us on:
OMICS International organises 3000+ Global Conferenceseries Events every year across USA, Europe & Asia with support from 1000 more scientific Societies and Publishes 700+ Open Access Journals which contains over 50000 eminent personalities, reputed scientists as editorial board members.

Open Access Journals gaining more Readers and Citations

700 Journals and 15,000,000 Readers Each Journal is getting 25,000+ Readers

This Readership is 10 times more when compared to other Subscription Journals (Source: Google Analytics)

Research Article

A Stochastic Segmentation Model for Recurrent Copy Number Alteration Analysis

Haipeng Xing* and Ying Cai

Applied Mathematics and Statistics, State University of New York, Stony Brook, NY 11794, USA

*Corresponding Author:
Haipeng Xing
Applied Mathematics and Statistics
State University of New York
Stony Brook, NY 11794, USA
E-mail: [email protected]

Received date: February 06, 2015; Accepted date: May 11, 2015; Published date: May 18, 2015

Citation: Xing H, Cai Y (2015) A Stochastic Segmentation Model for Recurrent Copy Number Alteration Analysis. J Biom Biostat 6:221. doi: 10.4172/2155-6180.1000221

Copyright: © 2015 Xing H, et al. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are are credited.

Abstract

Recurrent DNA copy number alterations (CNAs) are key genetic events in the study of human genetics and disease. Analysis of recurrent DNA CNA data often involves the inference of individual samples’ true signal levels and the crosssample recurrent regions at each location. We propose for the analysis of multiple samples CNA data a new stochastic segmentation model and an associated inference procedure that has attractive statistical and computational properties. An important feature of our model is that it yields explicit formulas for posterior probabilities of recurrence at each location, which can be used to estimate the recurrent regions directly. We propose an approximation method whose computational complexity is only linear in sequence length, which makes our model applicable to data of higher density. Simulation studies and analysis of an ovarian cancer dataset with 15 samples and a lung cancer dataset with 10 samples are conducted to illustrate the advantage of the proposed model.

Keywords

Share This Page

Additional Info

Loading
Loading Please wait..
 
Peer Reviewed Journals
 
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
 
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

Agri & Aquaculture Journals

Dr. Krish

[email protected]

1-702-714-7001Extn: 9040

Biochemistry Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Business & Management Journals

Ronald

[email protected]

1-702-714-7001Extn: 9042

Chemistry Journals

Gabriel Shaw

[email protected]

1-702-714-7001Extn: 9040

Clinical Journals

Datta A

[email protected]

1-702-714-7001Extn: 9037

Engineering Journals

James Franklin

[email protected]

1-702-714-7001Extn: 9042

Food & Nutrition Journals

Katie Wilson

[email protected]

1-702-714-7001Extn: 9042

General Science

Andrea Jason

[email protected]

1-702-714-7001Extn: 9043

Genetics & Molecular Biology Journals

Anna Melissa

[email protected]

1-702-714-7001Extn: 9006

Immunology & Microbiology Journals

David Gorantl

[email protected]

1-702-714-7001Extn: 9014

Materials Science Journals

Rachle Green

[email protected]

1-702-714-7001Extn: 9039

Nursing & Health Care Journals

Stephanie Skinner

[email protected]

1-702-714-7001Extn: 9039

Medical Journals

Nimmi Anna

[email protected]

1-702-714-7001Extn: 9038

Neuroscience & Psychology Journals

Nathan T

[email protected]

1-702-714-7001Extn: 9041

Pharmaceutical Sciences Journals

Ann Jose

[email protected]

1-702-714-7001Extn: 9007

Social & Political Science Journals

Steve Harry

[email protected]

1-702-714-7001Extn: 9042

 
© 2008- 2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version
adwords