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

<h4>Background</h4>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-abund...

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Autores principales:	Mikhail V Matz, Rachel M Wright, James G Scott
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/00f79762ad434425a236453166f4ddbf
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spelling	oai:doaj.org-article:00f79762ad434425a236453166f4ddbf2021-11-18T08:59:09ZNo control genes required: Bayesian analysis of qRT-PCR data.1932-620310.1371/journal.pone.0071448https://doaj.org/article/00f79762ad434425a236453166f4ddbf2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23977043/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4>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.<h4>Results</h4>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 expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests.<h4>Conclusions</h4>Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.Mikhail V MatzRachel M WrightJames G ScottPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 8, p e71448 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mikhail V Matz Rachel M Wright James G Scott No control genes required: Bayesian analysis of qRT-PCR data.
description	<h4>Background</h4>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.<h4>Results</h4>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 expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the "classic" analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests.<h4>Conclusions</h4>Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.
format	article
author	Mikhail V Matz Rachel M Wright James G Scott
author_facet	Mikhail V Matz Rachel M Wright James G Scott
author_sort	Mikhail V Matz
title	No control genes required: Bayesian analysis of qRT-PCR data.
title_short	No control genes required: Bayesian analysis of qRT-PCR data.
title_full	No control genes required: Bayesian analysis of qRT-PCR data.
title_fullStr	No control genes required: Bayesian analysis of qRT-PCR data.
title_full_unstemmed	No control genes required: Bayesian analysis of qRT-PCR data.
title_sort	no control genes required: bayesian analysis of qrt-pcr data.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/00f79762ad434425a236453166f4ddbf
work_keys_str_mv	AT mikhailvmatz nocontrolgenesrequiredbayesiananalysisofqrtpcrdata AT rachelmwright nocontrolgenesrequiredbayesiananalysisofqrtpcrdata AT jamesgscott nocontrolgenesrequiredbayesiananalysisofqrtpcrdata
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No control genes required: Bayesian analysis of qRT-PCR data.

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