Alignment Results

We have used the continuous alignment method presented in [1] to align the second cycle to the first cycle using the interpolated and deconvolved results. This method can compute an alignment error value which is the average square integral between the two aligned curve. We fixed the alignment parameters so that the two cycles are assumed to align perfectly well (stretch of 1 and shift of 0). Next, we compute the average alignment error for all cycling genes using these two datasets.
Both datasets resulted in similar alignment error (0.16). However, spline interpolation tends to flatten the expression profile. This leads to low alignment errors (for example, a completely flat curve will have an alignment error of 0). We thus normalized the alignment error for each gene by its average absolut expression values. Note that this procedure will still result in large errors for the deconvolved data if the two cycles do not agree. The normzalied error was 25% higher for the inmterpolated results, inidicatinmg that the deconvolved values achieve better agreement betnween the first and second cycles.