Selecting Differentially Expressed Proteomic Markers from Mass Spectrometry Data

High-throughput mass spectrometry and statistical analysis methodologies are promising technologies to aid the medical diagnostics field by detecting the cancer-related proteomic markers. We propose statistical methods to cull the potential markers by ranking them in relations to their power of separability distinguishing cancerous patients from normal persons or among different cancer stages. To assess the training variability, resampling via bootstrap strategy is adopted to select stable markers which show the potential of a large probability to classify specific groups. Selected marker pattern is validated by a combined classifier. Methods are demonstrated by a colon cancer dataset screened by SELDI technology