Systems and methods for step discontinuity removal in real-time PCR fluorescence data

Systems and methods for removing jump discontinuities in PCR or growth data. A first approximation to a curve that fits a received data set is determined by applying a non-linear regression process to a non-linear function that models the data set to determine parameters, including a step discontinuity parameter, of the non-linear function. One example of a non-linear function is a double sigmoid equation. A second approximation to a curve that fits the data set is also determined by applying a regression process to a second non-linear function to determine parameters, including a step discontinuity parameter, of the second function. One of the first or second approximations is then selected based on an information coefficient determined for each of the first and second approximations. If a confidence interval calculated for the step discontinuity parameter includes the value zero, no step correction is made. If the confidence interval does not include the value zero, then a step correction is made. If a step correction is made, the portion of the data curve prior to the step change is replaced with appropriate portion of the selected approximation to produce a shift-corrected data set. In certain aspects, the portion of the data curve up to the first point after the step change is corrected. In certain aspects, if the approximation does not satisfy a goodness of fit criterion, no step correction is made. The shift-corrected data set is returned and may be displayed or otherwise used for further processing.