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

Analytical Biochemistry
Daijun LingPaul M Salvaterra

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...Continue Reading

References

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Citations

Jul 2, 2015·Comparative Biochemistry and Physiology. Part B, Biochemistry & Molecular Biology·Zi-Xia ZhaoXiao-Wen Sun
May 12, 2019·FEBS Letters·Stefan MereiterCelso A Reis

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