After collection, the images were aligned to correct for any thermally induced movement via an open-source automated program, AIR (33). The remainder of the analysis was performed with a custom-developed MATLAB script (fig. S4). The user selects the four corners of the chip to generate a virtual mask outlining the expected well locations. Once generated, the virtual mask was mapped to the acquired image via a homography transformation to correct for the nonparallel relationship between the signal and imaging surfaces. Finally, well-by-well transformations were performed by locally optimizing the signal over a 5 × 5 pixel neighborhood. The overall image contained 4240 × 2832 pixels, yielding approximately 200 pixels per well.

Once well locations were determined, the virtual mask was propagated throughout all of the collected images and captured the signal intensity of the 200 pixels in each well. Fluorescence intensity values within a well at each time point were checked for outliers and then averaged. Time points were then sorted by temperature, and each signal within 0.3°C was further averaged. Finally, a low-pass and Savitzky-Golay filter was performed on each well to produce a melt curve. The negative derivative of this signal was taken, and then corresponding peak(s) determined the melt temperature of the amplicon in each well. Only the right-most peak is used for discernment in each well to avoid any potential signal from heteroduplex formation. Simple thermal calibration was performed by taking advantage of the DREAMing assay principles (21), namely, a known background population of unmethylated targets, which had a consistent melt temperature and could thus be used for interexperimental alignment.

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