Statistical development and assessment of summary measures to account for isotopic clustering of Fourier transform mass spectrometry data in clinical diagnostic studies

TL;DR

This paper develops and assesses statistical methods for summarizing high-throughput mass spectrometry data in clinical studies, leveraging isotopic distribution properties to improve disease classification accuracy.

Contribution

It introduces simple, effective preprocessing and summarization techniques that incorporate isotopic cluster information for better disease outcome prediction in proteomics.

Findings

Both isotopic cluster shape and intensity are informative for disease classification.

The proposed methods improve the accuracy of disease status prediction.

Preprocessing based on isotopic properties enhances data analysis in proteomics.

Abstract

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.