Quality Assessment of Tandem Mass Spectra by Using a Weighted K-Means

Clinical Proteomics

Jiarui Ding²  Jinhong Shi¹  Fang-Xiang Wu³ 

[1] Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, CanadaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, CanadaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada;Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, CanadaDepartment of Mechanical Engineering, University of Saskatchewan, Saskatoon, CanadaDepartment of Mechanical Engineering, University of Saskatchewan, Saskatoon, Canada;Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, CanadaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, CanadaDepartment of Mechanical Engineering, University of Saskatchewan, Saskatoon, CanadaDepartment of Mechanical Engineering, University of Saskatchewan, Saskatoon, CanadaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, CanadaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, CanadaDepartment of Mechanical Engineering, University of Saskatchewan, Saskatoon, CanadaDivision of Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada

关键词: Tandem mass spectrum;    Quality assessment;    Weighted k-means;    Feature vector;    Peptide;    Protein;   

DOI  :  10.1007/s12014-009-9025-4

来源: Humana Press Inc

【 摘 要 】

Abstract

Introduction

The tandem mass spectrometer is a powerful tool with which to generate peptide (tandem) mass spectrum data for the analysis of complex biological protein mixtures in genomic-related disease cell lines. However, the majority of experimental tandem mass spectra cannot be interpreted by any database search engines. One of the main reasons this happens is that majority of experimental spectra are of quality too poor to be interpretable. Interpreting these “un-interpretable” spectra is a waste of time. Therefore, it is worthwhile to determine the quality of mass spectra before any interpretation.

Objectives

This paper proposes an approach to classifying tandem spectra into two groups: one with high quality and one with poor quality.

Methods

The proposed approach has two steps. First, each spectrum is mapped to a feature vector which describes the quality of the spectrum. Then, a weighted K-means clustering method is applied in order to classify the tandem mass spectra.

Results and Conclusion

Computational experiments illustrate that one cluster contains the majority of the high-quality spectra, while the other contains the majority of the poor-quality spectra. This result indicates that if we just search the spectra in the high-quality cluster, we can save the time for searching the majority of poor-quality spectra while losing a minimal amount of high-quality spectra. The software created for this work is available upon request.

【 授权许可】

Unknown