Statistics for proteomics: A review of tools for analyzing experimental data

Wolfgang Urfer*, Marco Grzegorczyk, Klaus Jung

*Corresponding author for this work

Research output: Contribution to journalReview articleAcademicpeer-review

43 Citations (Scopus)

Abstract

Most proteomics experiments make use of 'high throughput' technologies such a's 2-DE, MS or protein arrays to measure simultaneously the expression levels of thousands of proteins. Such experiments yield, large, high-dimensional data sets which usually reflect not only the biological but also technical and experimental factors. Statistical tools are essential for evaluating these data and preventing false conclusions. Here, an overview is given of some, typical statistical tools for proteomics experiments.

In particular, we present methods for data preprocessing (e.g. calibration, missing values estimation and outlier detection), comparison of protein expression in different groups (e.g. detection of differentially expressed proteins or classification of new observations) as well as the detection of dependencies between proteins (e.g. protein clusters or networks). We also discuss questions of sample size planning for some of these methods.

Original languageEnglish
Pages (from-to)48-55
Number of pages8
JournalProteomics
DOIs
Publication statusPublished - Sep-2006

Keywords

  • DIFFERENCE GEL-ELECTROPHORESIS
  • MASS-SPECTROMETRY
  • MICROARRAY EXPERIMENTS
  • CLASSIFICATION
  • SERUM

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