Normalization Regarding Non-Random Missing Values in High-Throughput Mass Spectrometry Data

@article{Wang2006NormalizationRN,
  title={Normalization Regarding Non-Random Missing Values in High-Throughput Mass Spectrometry Data},
  author={Pei Wang and Hua Tang and Heidi Zhang and Jeffrey R Whiteaker and Amanda G. Paulovich and Martin McIntosh},
  journal={Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
  year={2006},
  pages={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… CONTINUE READING