This article is part of the supplement: Neural Information Processing Systems (NIPS) workshop on New Problems and Methods in Computational Biology
Time-series alignment by non-negative multiple generalized canonical correlation analysis
1 Institute of Computational Science, ETH Zurich, Switzerland
2 Competence Center of Systems Physiology and Metabolic Diseases, ETH Zurich, Switzerland
BMC Bioinformatics 2007, 8(Suppl 10):S4 doi:10.1186/1471-2105-8-S10-S4Published: 21 December 2007
Quantitative analysis of differential protein expressions requires to align temporal elution measurements from liquid chromatography coupled to mass spectrometry (LC/MS). We propose multiple Canonical Correlation Analysis (mCCA) as a method to align the non-linearly distorted time scales of repeated LC/MS experiments in a robust way.
Multiple canonical correlation analysis is able to map several time series to a consensus time scale. The alignment function is learned in a supervised fashion. We compare our approach with previously published methods for aligning mass spectrometry data on a large proteomics dataset. The proposed method significantly increases the number of proteins that are identified as being differentially expressed in different biological samples.
Jointly aligning multiple liquid chromatography/mass spectrometry samples by mCCA substantially increases the detection rate of potential bio-markers which significantly improves the interpretability of LC/MS data.