Jump to ContentJump to Main Navigation
Show Summary Details
In This Section

Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Stumpf, Michael P.H.

6 Issues per year


IMPACT FACTOR 2016: 0.646
5-year IMPACT FACTOR: 1.191

CiteScore 2016: 0.94

SCImago Journal Rank (SJR) 2015: 0.954
Source Normalized Impact per Paper (SNIP) 2015: 0.554

Mathematical Citation Quotient (MCQ) 2015: 0.06

Online
ISSN
1544-6115
See all formats and pricing
In This Section
Volume 9, Issue 1 (Jan 2010)

Issues

A Bayesian Hierarchical Model for Quantitative Real-Time PCR Data

Turid Follestad
  • Norwegian University of Science and Technology
/ Tommy S Jørstad
  • Norwegian University of Science and Technology
/ Sten E Erlandsen
  • Norwegian University of Science and Technology
/ Arne K Sandvik
  • Norwegian University of Science and Technology & St. Olav’s University Hospital
/ Atle M Bones
  • Norwegian University of Science and Technology
/ Mette Langaas
  • Norwegian University of Science and Technology
Published Online: 2010-01-06 | DOI: https://doi.org/10.2202/1544-6115.1427

We present a Bayesian hierarchical model for quantitative real-time polymerase chain reaction (PCR) data, aiming at relative quantification of DNA copy number in different biological samples. The model is specified in terms of a hidden Markov model for fluorescence intensities measured at successive cycles of the polymerase chain reaction. The efficiency of the reaction is assumed to depend on the abundance of the target DNA through fluorescence intensities, and the relationship is specified based on the kinetics of the reaction. The model incorporates the intrinsic random nature of the process as well as measurement error. Taking a Bayesian inferential approach, marginal posterior distributions of the quantities of interest are estimated using Markov chain Monte Carlo. The method is applied to simulated data and an experimental data set.

Keywords: Bayesian model; Markov chain Monte Carlo; polymerase chain reaction; real-time PCR

About the article

Published Online: 2010-01-06



Citation Information: Statistical Applications in Genetics and Molecular Biology, ISSN (Online) 1544-6115, DOI: https://doi.org/10.2202/1544-6115.1427. Export Citation

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Bret Hanlon and Anand N. Vidyashankar
Journal of the American Statistical Association, 2011, Volume 106, Number 494, Page 525

Comments (0)

Please log in or register to comment.
Log in