No Control Genes Required: Bayesian Analysis of qRT-PCR Data

Abstract

BackgroundModel-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.ResultsIn 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.ConclusionsOur 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.

Expression of pluripotency factors in echinoderm regeneration

“…Statistical analysis of qPCR data was performed using the MCMC.qpcr R package (Matz et al, 2013; R Core Team, 2014) in the ”classic” mode, which uses a normalization procedure relative to ”control” genes. The following control genes were used to normalize the qPCR expression values in the radial nerve cord dataset: elongation factor 2 (EF2) , ribosomal protein rpL18a , Mn-superoxide dismutase (Sod) , and V-type proton AT-Pase 16 kDa proteolipid subunit (ATP6L).…”

“…∗ P < 0.05, ∗∗∗ P < 0.001. The full output of the MCMC.qpcr R package (Matz et al, 2013) containing relative expression values and corresponding p-values can be found in Electronic Supplementary Material, Text S3, and Text S4…”

An improved method for RNA extraction from common bean seeds and validation of reference genes for qPCR

“…There is a premise that reference genes cannot be regulated by the experimental conditions of the sample set (Robledo et al 2014). The geNorm and BestKeeper algorithms are based on the assumption that none of the analyzed genes are co-regulated (Matz et al 2013). For this reason, the use of more than one algorithm for the validation of reference genes is suggested to give more reliable results (Zyzynska-Granica and Koziak 2012).…”

Sexually dimorphic effects of gestational endocrine-disrupting chemicals on microRNA expression in the developing rat hypothalamus

“…All raw CT microRNA and mRNA data were normalized within sex to the median of the DMSO P15 group in an R statistical package for qPCR analysis which utilized a generalized linear mixed model with Poisson-lognormal errors and a Bayesian Marco Chain Monte Carlo sampling scheme (47). All data were normally distributed and homoscedastic.…”