Peak Tree: A New Tool for Multiscale Hierarchical Representation and Peak Detection of Mass Spectrometry Data

Abstract

Peak detection is one of the most important steps in mass spectrometry (MS) analysis. However, the detection result is greatly affected by severe spectrum variations. Unfortunately, most current peak detection methods are neither flexible enough to revise false detection results nor robust enough to resist spectrum variations. To improve flexibility, we introduce peak tree to represent the peak information in MS spectra. Each tree node is a peak judgment on a range of scales, and each tree decomposition, as a set of nodes, is a candidate peak detection result. To improve robustness, we combine peak detection and common peak alignment into a closed-loop framework, which finds the optimal decomposition via both peak intensity and common peak information. The common peak information is derived and loopily refined from the density clustering of the latest peak detection result. Finally, we present an improved ant colony optimization biomarker selection method to build a whole MS analysis system. Experiment shows that our peak detection method can better resist spectrum variations and provide higher sensitivity and lower false detection rates than conventional methods. The benefits from our peak-tree-based system for MS disease analysis are also proved on real SELDI data.

Analysis of the correlation between infrared thermal sequence images of nostril area and respiratory rate

“…However, most of the many previous studies were applied to specific applications. These included, the detection of R‐peaks in ECG signals [41, 42] and peak detection in mass spectrometry analysis [43]. Of course, there are also other more universal tools, such as scale‐space peak picking [44], Matlab findpeak [45], and others.…”

A novel self-adaptive, multi-peak detection algorithm for blade tip clearance measurement based on a capacitive probe

“…According to the pattern matching theory, when the template has the same shape as the signal to be matched, a large matching coefficient can be obtained; as the noise is random, the matching between noise and template contributes a very small coefficient [24,25]. Therefore, the SNR of the matching coefficients is greatly improved by pattern matching.…”

Preprocessing and Analysis of LC-MS-Based Proteomic Data

“…Also, peak detection is usually performed for each LC-MS run individually, without leveraging the information from other runs in the same experiment. Utilization of multi-scale information from multiple runs has been proposed for analysis of MALDI-TOF data [20]. This idea could potentially be applied to LC-MS data and lead to a more reliable peak detection result, where the peak matching step to be introduced later plays an important role.…”