A 'shape-orientated' algorithm employing an adapted Marr wavelet and shape matching index improves the performance of...

A new 'shape-orientated' continuous wavelet transform (CWT)-based algorithm employing an adapted Marr wavelet (AMW) with a shape matching index (SMI), defined as peak height normalized wavelet coefficient ( [Formula: see text] ) for feature filtering, was developed for chromatographic peak detection and quantification. Exploiting the chromatographic profile of a candidate peak, AMW-SMI algorithm emphasizes more on the matching of the overall chromatographic profile to a reference Gaussian shape, while partly alleviates the requirement on the signal intensity derived from single or several data points, thus it allows the detection of low-intensity features from metabolites at low abundance. AMW-SMI imposes maximum and minimum thresholds on the ridgeline width and length to define a valid ridgeline, which corresponds to a more stably shaped chromatographic profile. The maximum wavelet coefficient C_max^'(a₀,b₀) on the valid ridgeline determines the translation b₀ as the peak center. AMW-SMI detects the valley lines to define the peak boundaries, which is important to obtain accurate peak quantification. As a more 'shape-orientated' peak detection algorithm, various methods related to the 'shape' are introduced for feature filtering, out of which, the effective SNR (eSNR) is defined to evaluate if the shape is strong or good enough relative to the 'shape noise', and the SMI, which can quantitatively evaluate the shape quality regardless of the data intensities and peak width, is applied to filter out the poorly shaped false positives. AMW-SMI performs Gaussian fitting of all data points between the defined peak boundaries to refine the peak parameters, and the refined SMI, SNR and peak width can be applied for further feature filtering and reinforce the 'shape-quality' of final selected peaks. The performance of AMW-SMI is evaluated qualitatively (by recall, precision and F-score) and quantitatively (by ratio of isotopic features and triplicate RSD) on the LC-MS data of model mixtures of 21 human metabolite standards and 8 plant metabolite standards, and of serum sample spiked with the 21 human metabolite standards, and on the triplicate LC-MS data of the same sample of cell metabolomic extracts. Compared with XCMS (centWave) and MZmine 2 (ADAP), the proposed AMW-SMI algorithm can faithfully identify chromatographic peaks with significantly fewer false positives and demonstrated general superiority in terms of qualitative precision (robustness) and quantitative accuracy (by ratio of isotopic features), and comparable recall (sensitivity) and quantitative stability (by RSD of triplicate measurements).