Open AccessPower Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment
Author(s) -
Yi Du,
Ye Hu,
Yu Xia,
Zheng Ouyang
Publication year - 2016
Publication title -
analytical chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.117
H-Index - 332
eISSN - 1520-6882
pISSN - 0003-2700
DOI - 10.1021/acs.analchem.5b04418
Subject(s) - normalization (sociology) , chemistry , mass spectrometry , support vector machine , database normalization , analytical chemistry (journal) , chromatography , data mining , pattern recognition (psychology) , artificial intelligence , computer science , sociology , anthropology
Biomarker profiling using mass spectrometry plays an essential role in biological studies and is highly dependent on the data analysis for sample classification. In this study, we introduced power nomination of the mass spectra as a method for systematically altering the weights of peaks at different intensity levels. In combination with the use of support vector machine method (SVM), the impact on the sample classification has been characterized using data in four studies previously reported, including the distinctions of anomeric configurations of sugars, types of bacteria, stages of melanoma, and the types of breast cancer. Comprehensive analysis of the data with normalization at different power normalization index (PNI) was developed and analysis tools, including error-PNI plots, reference profiles, and error source profiles, were used to assess the potential of the analytical methods as well as to find the proper approaches to classify the samples.