Normalization regarding non-random missing values in high-throughput mass spectrometry data

Pac Symp Biocomput. 2006:315-26.


We propose a two-step normalization procedure for high-throughput mass spectrometry (MS) data, which is a necessary step in biomarker clustering or classification. First, a global normalization step is used to remove sources of systematic variation between MS profiles due to, for instance, varying amounts of sample degradation over time. A probability model is then used to investigate the intensity-dependent missing events and provides possible substitutions for the missing values. We illustrate the performance of the method with a LC-MS data set of synthetic protein mixtures.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromatography, Liquid
  • Computational Biology
  • Mass Spectrometry / statistics & numerical data*
  • Models, Statistical
  • Probability
  • Proteins / isolation & purification


  • Proteins