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Molecular & Cellular Proteomics 8:2285-2295, 2009.
© 2009 by The American Society for Biochemistry and Molecular Biology, Inc.

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Research

Development and Evaluation of Normalization Methods for Label-free Relative Quantification of Endogenous Peptides^*,

Kim Kultima^,,¶, Anna Nilsson, Birger Scholz^||,**, Uwe L. Rossbach, Maria Fälth and Per E. Andrén

From Medical Mass Spectrometry,
||Division of Toxicology, and
Division of Biological Research on Drug Dependence, Department of Pharmaceutical Biosciences, Uppsala University, The Biomedical Center, Husargatan 3, Box 583, SE-75123 Uppsala, Sweden and
Department of Medical Sciences, Clinical Pharmacology, Uppsala University Hospital, 751 85 Uppsala, Sweden

The performances of 10 different normalization methods on data of endogenous brain peptides produced with label-free nano-LC-MS were evaluated. Data sets originating from three different species (mouse, rat, and Japanese quail), each consisting of 35–45 individual LC-MS analyses, were used in the study. Each sample set contained both technical and biological replicates, and the LC-MS analyses were performed in a randomized block fashion. Peptides in all three data sets were found to display LC-MS analysis order-dependent bias. Global normalization methods will only to some extent correct this type of bias. Only the novel normalization procedure RegrRun (linear regression followed by analysis order normalization) corrected for this type of bias. The RegrRun procedure performed the best of the normalization methods tested and decreased the median S.D. by 43% on average compared with raw data. This method also produced the smallest fraction of peptides with interblock differences while producing the largest fraction of differentially expressed peaks between treatment groups in all three data sets. Linear regression normalization (Regr) performed second best and decreased median S.D. by 38% on average compared with raw data. All other examined methods reduced median S.D. by 20–30% on average compared with raw data.

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