The ΔΔct method estimates fold change in gene expression data from RT-PCR assay. The ΔΔct estimate aggregates replicates using mean and standard deviation (sd) and is not robust to outliers which are in practice often removed before the non-outlying replicates are aggregated. The alternative of using robust statistics such as median and median absolute deviation (MAD) to aggregate the replicates is not done in practice perhaps because the distribution of a robust ΔΔct estimate based on median and MAD is not straightforward to deduce.

We introduce a robust ΔΔct estimate and deduce an approximate distribution for it. Simulations show that when data has outliers, the robust ΔΔct estimate compared to the non-robust ΔΔct estimate leads to significantly reduced confidence interval length and a coverage close to the nominal coverage. The analysis of an RT-PCR data from a Novartis clinical trial demonstrates benefit of a robust ΔΔct estimate.

ΔΔct 方法估计来自 RT-PCR 测定的基因表达数据的倍数变化。ΔΔct 估计使用平均值和标准偏差 (sd) 聚合复制，并且对于在实践中经常在非异常复制被聚合之前移除的异常值不稳健。使用稳健统计数据（如中值和中值绝对偏差 (MAD) 来聚合复制品）的替代方法在实践中并未完成，这可能是因为基于中值和 MAD 的稳健 ΔΔct 估计值的分布并不容易推断。

我们引入了一个稳健的 ΔΔct 估计并推导出它的近似分布。模拟表明，当数据具有异常值时，与非稳健 ΔΔct 估计相比，稳健的 ΔΔct 估计会导致置信区间长度显着缩短，并且覆盖范围接近标称覆盖范围。对来自诺华临床试验的 RT-PCR 数据的分析证明了稳健的 ΔΔct 估计值的好处。