A novel peak detection approach with chemical noise removal using short‐time FFT for prOTOF MS data

Abstract

Peak detection is a pivotal first step in biomarker discovery from mass spectrometry (MS) data and can significantly influence the results of downstream data analysis steps. We developed a novel automatic peak detection method for prOTOF MS data which does not require a priori knowledge of protein masses. Random noise is removed by an undecimated wavelet transform and chemical noise is attenuated by an adaptive short-time discrete Fourier transform. Isotopic peaks corresponding to a single protein are combined by extracting an envelope over them. Depending on the signal-to-noise ratio (SNR), the desired peaks in each individual spectrum are detected and those with the highest intensity among their peak clusters are recorded. The common peaks among all the spectra are identified by choosing an appropriate cut-off threshold in the complete linkage hierarchical clustering. To remove the 1Da shifting of the peaks, the peak corresponding to the same protein is determined as the detected peak with the largest number among its neighborhood. We validated this method using a dataset of serial peptide and protein calibration standards. Compared with MoverZ program, our new method detects more peaks and significantly enhances SNR of the peak after the chemical noise removal. We then successfully applied this method to a dataset from prOTOF MS spectra of albumin and albumin-bound proteins from serum samples of 59 patients with carotid artery disease to detect peaks with SNR ≥2. Our method is easily implemented and is highly effective to define peaks that will be used for disease classification or to highlight potential biomarkers.

A Comprehensive Analysis of MALDI-TOF Spectrometry Data

“…Peaks may be also considered a continuous range of points where intensities are high enough (Eidhammer et al, 2007). Another approach is using peak clusters to find peaks of the highest intensities (Zhang et al, 2007). There are also methods which try to distinguish true peaks from noise and contaminants.…”

“…al., 2004) usually compare local maxima with noise level. There are also methods (Zhang et al, 2007) considering the signal to noise ratio. This ratio needs to be high enough to identify a true peak with a local maximum.…”

“…Some methods consider the mass spectrometer resolution. There are also methods turning spectrum decomposition into the sum of their constituent functions (Randolph et al, 2005) or the sum of the Levy processes (Zhang et al, 2007).…”

O-glycan profiling of serum glycan for potential renal cancer biomarkers

“…Peak intensity exhibited some variation locally, and decreased with increasing m/z. Spectrum features were similar to Q-TOF data [19] and prOTOF data [20].…”

Gaussian mixture decomposition in the analysis of MALDI‐TOF spectra

“…There are methods (Yasui et al, 2003;Tibshirani et al, 2004;Morris et al, 2005) that compare local maxima to the noise level. Local maxima are classified as peaks only when they have signal to noise ratio (SNR) high enough (Zhang et al, 2007). This technique facilitates the choice of only peaks with the highest intensities.…”