Deconvolution of the confounding variations for reverse transcription quantitative real-time polymerase chain reaction by separate analysis of biological replicate data

doi:10.1016/j.ab.2012.04.029

Analytical Biochemistry

Volume 427, Issue 1, 1 August 2012, Pages 21-25

https://doi.org/10.1016/j.ab.2012.04.029 Get rights and content

Abstract

Reverse transcription quantitative real-time polymerase chain reaction (RT–qPCR) uses threshold cycles (Ct values) for measuring relative gene expression. Ct values are signal-to-noise data composed of target gene expression and multiple sources of confounding variations. Data analysis is to minimize technical noises, evaluate biological variances, and estimate treatment-attributable expression changes of particular genes. However, this function is not sufficiently fulfilled in current analytic methods. An important but unrecognizable problem is that Ct values from all biological replicates and technical repeats are pooled across genes and treatment types. This violates the sample-specific association between target and reference genes, leading to inefficient removal of technical noises. To resolve this problem, here we propose to separate Ct values into replicate-specific data subsets and iteratively analyze expression ratios for individual data subsets. The individual expression ratios, rather than the raw Ct values, are pooled to determine the final expression change. The variances of all biological replicates and technical repeats across all target and reference genes are summed up. Our results from example data demonstrate that this separated method can substantially minimize RT–qPCR variance compared with the traditional methods using pooled Ct profiles. This analytic strategy is more effective in control of technical noises and improves the fidelity of RT–qPCR quantification.

Section snippets

Variance components and setup for RT–qPCR replicates

Biological individuals have endogenous variation in gene expression and may also exhibit variable responses to particular biomedical treatments with respect to gene expression. These differences primarily contribute to the biological variance. Furthermore, RT–qPCR quantification through RNA extraction, reverse transcription, and real-time PCR will inevitably introduce technical variance. Usually multiple biological replicates and technical repeats are set up for assessment of the variance [2],

Distribution of raw Ct values among independent replicates

To visualize the fluctuation of raw Ct among independent replicates, Ct values of unknown cDNA samples are plotted across genes and labeled differently for distinguishing independent replicates (Fig. 2). Even without separating treatment types, the Ct values exhibit an apparent stratification between independent replicates. The group of replicate 1 has constantly lower Ct values relative to replicate 3, suggesting the sample-specific association of Ct variations among measured genes; that is,

Acknowledgments

The authors received no outside funding for this work. Our labs are currently supported by the Sidell–Kagan Foundation (to P.M.S., City of Hope) and the National Institutes of Health (AG26572 to C.J.P., University of Southern California).

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Deconvolution of the confounding variations for reverse transcription quantitative real-time polymerase chain reaction by separate analysis of biological replicate data

Abstract

Section snippets

Variance components and setup for RT–qPCR replicates

Distribution of raw Ct values among independent replicates

Acknowledgments

Methods

Anal. Biochem.

Comput. Biol. Chem.

Methods

Methods

Methods

Design and optimization of reverse-transcription quantitative PCR experiments

Clin. Chem.

Importance of replication in analyzing time-series gene expression data: corticosteroid dynamics and circadian patterns in rat liver

BMC Bioinformatics

Data analysis software

An analysis of quantitative PCR reliability through replicates using the C method

J. Biomed. Sci. Eng.