Abstract: Systems and methods for removing jump discontinuities in growth data are provided. A first approximation to 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. A second approximation to 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 approximations is selected based on an information coefficient determined for each of the approximations. If a confidence interval for the step discontinuity parameter includes zero, no correction is made, and if includes zero, then a correction is made. For a correction, 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.

Abstract: 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.

Abstract: 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.