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The number of clusters was chosen using the cubic clustering criterion [ 12].
The number of classes to be considered was established using the cubic clustering criterion [ 29] implemented in SAS 9.2.
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To choose the optimal number of clusters, we used the cubic clustering criterion (CCC), in which the possible numbers of clusters are compared with each other in terms of their within-cluster sum of squares [ 33].
BeadStation 5003 data were extracted using the cubic spline normalization option of BeadStudio Version 1.5.1.3 (Illumina).
Fluorescent intensities were background-subtracted and then normalized using the cubic spline algorithm.
Raw data for each sample were background-subtracted and normalized using the "cubic spline" algorithm.
From these diameters, approximate volumes were calculated using the cubic and ellipsoid formula.
Figure 5 gives the cubic clustering criterion and the R-values for different numbers of clusters (1 to 25).
The "cubic clustering criterion" of the model was 14, higher than the cut-off of 2, validating the classification.
The number of clusters was selected based on a study of the observed overall R-squared and the cubic clustering criterion.
To determine the most appropriate number of clusters, R, the cubic clustering criteria and pseudo- F statistic were all examined (SAS Institute, release 9.1, Cary, NC, USA).
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