Chapter 18. Mass Spectrometry for Microbial Proteomics: Issues in Data Analysis with Electrophoretic or Mass Spectrometric Expression Proteomic Data

  1. Haroun N. Shah and
  2. Saheer E. Gharbia
  1. Natasha A. Karp

Published Online: 15 JUN 2010

DOI: 10.1002/9780470665497.ch18

Mass Spectrometry for Microbial Proteomics

Mass Spectrometry for Microbial Proteomics

How to Cite

Karp, N. A. (2010) Mass Spectrometry for Microbial Proteomics: Issues in Data Analysis with Electrophoretic or Mass Spectrometric Expression Proteomic Data, in Mass Spectrometry for Microbial Proteomics (eds H. N. Shah and S. E. Gharbia), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470665497.ch18

Editor Information

  1. Department for Bioanalysis and Horizon Technologies, Health Protection Agency Centre for Infections, 61 Colindale Avenue, London NW9 5EQ, UK

Author Information

  1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SA, UK

Publication History

  1. Published Online: 15 JUN 2010
  2. Published Print: 23 JUL 2010

ISBN Information

Print ISBN: 9780470681992

Online ISBN: 9780470665497

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Keywords:

  • mass spectrometry for microbial proteomics-data analysis issues with electrophoretic or mass spectrometric expression proteomic data;
  • expression proteomics, comparison of distinct proteomes to identify protein species;
  • data analysis, divided into univariate and multivariate approaches;
  • principal component analysis (PCA), partial least squares (PLS), hierarchical cluster analysis (HCA) and linear discriminate analysis (LDA);
  • repeat measurement types;
  • randomization within experimental designs;
  • understanding statistical tests-whether statistically significant;
  • understanding statistical tests and multiple testing problems;
  • downstream validation-confirming findings from expression studies

Summary

Expression or quantitative proteomics is the comparison of distinct proteomes which enables the identification of protein species which exhibit changes in expression or post-translational state in response to a given stimulus. Expression proteomics, as with the other – omics, suffers from experiments with many variables but few observations. Many different quantitative techniques are being utilized but many of the data analysis issues are independent of the technique used. Approaches to address the problems that arise with these large lean datasets are discussed to give insight into the types of statistical analyses of data appropriate for the various experimental strategies that can be employed. This review also highlights the importance of employing a robust experimental design and discusses the issues that need consideration. The concepts and examples discussed within will show how robust design and analysis will lead to confident results that will ensure expression proteomics delivers.