No control genes required: Bayesian analysis of qRT-PCR data.

ABSTRACT:

Background

Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process.

Results

In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expr

SUBMITTER: Matz MV 

PROVIDER: S-EPMC3747227 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature