Systems and methods for determining the
cycle threshold (Ct) value in a kinetic PCR amplification curve. The PCR
data set may be visualized in a two-dimensional plot of
fluorescence intensity (y-axis) vs. cycle number (x-axis). The
data set is transformed to produce a partition table of data points with one column including the
fluorescence at cycle (n) and a second column including the
fluorescence at cycle (n+i), where i is typically 1 or greater. A
cluster analysis process is applied to the partition table
data set to determine a plurality of clusters in the partition table data set. In one aspect, the clustering process used includes a k-means clustering
algorithm, where the number of identified clusters, k, is greater than or equal to three. In another aspect, a Partitioning Around Medoids (PAM)
algorithm is used to identify three or more clusters. Using the identified clusters, a linear slope of each of the clusters is determined based on y(n+1) vs. n, and for each cluster, a ratio of the slope of that cluster with the slope of an adjacent cluster is determined. The ratios are then compared. An end point of a cluster having the largest or smallest ratio represents a specific point of interest in the data curve. The
data point representing the
elbow or Ct value of the PCR curve is identified as an end point of one of the identified clusters, and the cycle number corresponding to this
data point is returned or displayed.