[Formula: see text]法から[Formula: see text]法へ：リアルタイム定量的ポリメラーゼ連鎖反応データ解析のためのより厳密なアプローチ

To optimize the real-time quantitative polymerase chain reaction (RT-qPCR) data analysis process through mathematical principles by replacing the biased [Formula: see text] method with a more rigorous [Formula: see text] method, thereby improving the accuracy of gene expression quantification analysis.

Essentially, the C _T value serves as the exponent in a base-2 exponential equation within the logic of comparative C _T method. In the traditional [Formula: see text] method, the arithmetic means of raw C _T and ΔC _T values are directly calculated and the exponential nature of C _T data is overlooked, which may introduce systematic bias to the calculation results. We propose a new method, entitled the [Formula: see text] method, in which all calculations are based on the transformation of C _T values into [Formula: see text]. This includes computing the relative initial expression levels of target and reference genes within each sample, the relative abundance of the target gene, and its fold change across groups. Statistical comparisons are then performed based on fold change values. By strictly adhering to the exponential nature of of C _T values, the biases introduced by arithmetic averaging at the C _T or ΔC _T level are avoided. We applied this method to multiple RT-qPCR datasets to evaluate the differences between the traditional [Formula: see text] and the proposed [Formula: see text] methods in gene expression quantification, as well as the effect of the differences.

In the original dataset from LIVAK and SCHMITTGEN, the two methods produced similar results. However, in the cadmium exposure experiment, findings from the [Formula: see text] method indicated that 8-hour cadmium exposure caused an increase of irg-6 gene expression in Caenorhabditis elegans from 1.314-fold to 7.125-fold (P = 0.0002). In contrast, findings from the [Formula: see text]method showed a fold change from 1.0 to 4.124 (P = 0.0015), a 70% difference between the two methods.

The [Formula: see text] method provides a mathematically more rigorous approach that more accurately reflects gene expression changes, particularly in experiments with high C _T variability. It offers a more reliable computational paradigm for quantitative gene expression analysis.