Figure 1

Procedure for generating correlation plots. Each spectrum is transformed to a row vector where the chemical shifts for both ¹H and ¹³C are encoded, forming a matrix with dimensions n x K (step 1). By plotting one of these vectors, real signals are easily discerned from noise and an appropriate noise threshold may be selected. Data points are removed from the matrix only when all values in the column (from all HSQC spectra) are lower than the selected threshold. This noise exclusion step results in a final matrix X of a more manageable size that still contains all relevant information (step 2). Any of the rows in X can be transformed to a matrix of the original format and plotted as a noise-free HSQC-spectrum. From this plot, a cross-peak (coordinate) of interest may be selected, corresponding to the column vector v _peak (step 3) in X. At this point, X (and v _peak) is auto-scaled and a correlation vector c _peak is calculated according to equation 2. This vector will contain values between −1 and 1, i.e. correlation coefficients, and can be visualized as a 2D spectrum after re-introducing zeros to the data points omitted in the noise exclusion step, followed by transformation to a matrix with the same dimensions as the original data (step 4). A cutoff for the correlation is then chosen for the visualization, for example 0.9, to only show peaks highly correlated (>0.9) with the chosen peak.