NORMALIZATION , BASELINE CORRECTION AND ALIGNMENT OF HIGH-THROUGHPUT MASS SPECTROMETRY DATA

We propose several preprocessing steps to be used before biomarker clustering or classifying for high-throughput Mass Spectrometry (MS) data. These preprocessing steps for the mass spectra are multiple alignment of technical replicates, baseline correction and normalization along the mass/charge axis. While the benefits from baseline correction and alignment seem obvious we studied more carefully the benefit from normalizing using some human prostate cancer SELDI TOF MS data (obtained from the Virginia Prostate Center Tissue and body Fluid Bank and approved by the Eastern Virginia Medical School). We show on these data that our global normalization by scaling helps in distinguishing between different cancer groups as well as between cancer and non-cancer groups. We used the Between to Within sum of squares ratio introduced by Fisher as well as visual inspection to illustrate the improvement brought by the normalization.