Adaptive bandwidth selection for biomarker discovery in mass spectrometry

Abstract

Differential quantification of proteins by liquid chromatography/mass spectrometry requires the alignment of a retention time axis. The alignment automatically corrects for time changes in the liquid chromatography unit when repeating two experiments. In this paper we will show an extension of non-negative canonical correlation analysis. We introduce an adaptive scale space estimation that adapts the complexity of a monotone regression function to the density of measurements across the retention time. Furthermore, a global model selection of the scale is replaced by a local one, where we estimate the scale for each individual time axis, instead of a global parameter that holds for all time axes. We show in experiments that we got a 13% gain. The performance gain is measured in the number of proteins that are detected to differ significantly in abundance for two different biological samples. We conclude that the adaptive scale estimation and the local model selection can outperform the global model selection which yields a more effective selection of differentially abundant proteins.