Statistical algorithm for assuring similar efficiency in standards and samples for absolute quantification by real-time reverse transcription polymerase chain reaction

Reverse transcription (RT) followed by the polymerase chain reaction (PCR) is the method of choice for quantifying rare transcripts in biological samples. A key assumption underlying the absolute quantification of transcripts is similar amplification efficiencies of all external standards and samples. However, efficiencies can vary between individual reactions, a problem that can be magnified when quantifying transcripts of low abundance. Here, an algorithm to assure that calibration standards and samples meet the assumption of similar amplification efficiencies underlying absolute quantification is presented. Individual reaction efficiency is estimated by fitting an exponential growth model to the fluorescence data in the exponential phase of the reaction. Next, reactions of standards with outlying estimates of amplification rates are eliminated using the boxplot outlier detection rule. Then, estimates of amplification rates of outlier-free standards are employed to define exact tolerance intervals, which are used to eliminate kinetic outliers from test samples. This algorithm was employed to eliminate kinetic outliers prior to defining the baseline expression of guanylyl cyclase C mRNA, a marker for colorectal cancer, in blood of healthy volunteers. These studies demonstrate that elimination of kinetic outliers from calibration standards and test samples improves the accuracy of absolute transcript quantification by RT-PCR.