Abstract
BackgroundReverse transcription quantitative PCR (RT-qPCR) is considered the gold standard for quantifying relative gene expression. Normalization of RT-qPCR data is commonly achieved by subtracting the Ct values of the internal reference genes from the Ct values of the target genes to obtain ΔCt. ΔCt values are then used to derive ΔΔCt when compared to a control group or to conduct further statistical analysis.ResultsWe examined two rheumatoid arthritis RT-qPCR low density array datasets and found that this normalization method introduces substantial bias due to differences in PCR amplification efficiency among genes. This bias results in undesirable correlations between target genes and reference genes, which affect the estimation of fold changes and the tests for differentially expressed genes. Similar biases were also found in multiple public mRNA and miRNA RT-qPCR array datasets we analysed. We propose to regress the Ct values of the target genes onto those of the reference genes to obtain regression coefficients, which are then used to adjust the reference gene Ct values before calculating ΔCt.ConclusionsThe per-gene regression method effectively removes the ΔCt bias. This method can be applied to both low density RT-qPCR arrays and individual RT-qPCR assays.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-1274-1) contains supplementary material, which is available to authorized users.
Highlights
Reverse transcription quantitative PCR (RT-qPCR) is considered the gold standard for quantifying relative gene expression
ΔCt normalization introduces bias The commonly used normalization method for RT-qPCR data is subtracting the Ct values of the internal reference genes from those of the target genes to obtain the difference in the Ct (ΔCt)
If there were no bias, there would be no significant correlation between the mean ΔCt values of the target genes and the reference gene Ct values
Summary
Reverse transcription quantitative PCR (RT-qPCR) is considered the gold standard for quantifying relative gene expression. Conventional normalization of RT-qPCR data entails first identifying the appropriate reference genes, subtracting the Ct (threshold cycle) values of the best reference gene or the Ct mean of several reference genes from all the target genes to obtain the normalized (calibrated) ΔCt for further comparison [17,18]. This type of normalization is based on the assumption that the Ct values of the target genes have a linear relationship with those of the reference genes and that the regression coefficient is 1. We show, with RT-qPCR array data collected from rheumatoid arthritis patients, that the relationship is linear but the
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.