Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment

Y. Melodie Du, Ye Hu, Yu Xia, Zheng Ouyang

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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. (Graph Presented).

Original languageEnglish (US)
Pages (from-to)3156-3163
Number of pages8
JournalAnalytical Chemistry
Volume88
Issue number6
DOIs
StatePublished - Mar 15 2016
Externally publishedYes

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Mass spectrometry
Biomarkers
Sugars
Support vector machines
Bacteria

ASJC Scopus subject areas

  • Analytical Chemistry

Cite this

Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment. / Du, Y. Melodie; Hu, Ye; Xia, Yu; Ouyang, Zheng.

In: Analytical Chemistry, Vol. 88, No. 6, 15.03.2016, p. 3156-3163.

Research output: Contribution to journalArticle

Du, Y. Melodie ; Hu, Ye ; Xia, Yu ; Ouyang, Zheng. / Power Normalization for Mass Spectrometry Data Analysis and Analytical Method Assessment. In: Analytical Chemistry. 2016 ; Vol. 88, No. 6. pp. 3156-3163.
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