2016/02/09 by Alexia Kakourou, Werner Vach, Kakourou, Alexia +8
Biochemistry, Genetics and Molecular Biology · Chemistry · Mathematics · #Advanced Proteomics Techniques and Applications #Applications (stat.AP) #FOS: Computer and information sciences #Mass Spectrometry Techniques and Applications #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #stat.AP #stat.ME
paper · pdf · doi:10.48550/arxiv.1602.02908
arxiv created 2016/02/09 · openalex publication_date 2016/02/09 · arxiv updated 2016/02/10 · openalex created_date 2022/08/29 · openalex updated_date 2026/07/28
Mass spectrometry based clinical proteomics has emerged as a powerful tool for highthroughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.