Simple regression for correcting ΔCt bias in RT-qPCR low-density array data normalization

Background: Reverse 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 C-t values of the internal reference genes from the C-t values of the target genes to obtain Delta C-t Delta C-t values are then used to derive Delta Delta C-t when compared to a control group or to conduct further statistical analysis. Results: We 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 C-t values of the target genes onto those of the reference genes to obtain regression coefficients, which are then used to adjust the reference gene C-t values before calculating Delta C-t. Conclusions: The per-gene regression method effectively removes the Delta C-t bias. This method can be applied to both low density RT-qPCR arrays and individual RT-qPCR assays.