Time-series alignment by non-negative multiple generalized canonical correlation analysis.
BACKGROUND
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.


RESULTS
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.


CONCLUSIONS
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.
Fischer
Bernd
B
Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch
Roth
Volker
V
Buhmann
Joachim M
JM
D015415:Biomarkers / Q000032:analysis; D002853:Chromatography, Liquid / Q000592:standards; D013058:Mass Spectrometry / Q000592:standards; D015999:Multivariate Analysis; D040901:Proteomics / Q000379:methods* / Q000592:standards; D020539:Sequence Analysis, Protein / Q000379:methods / Q000592:standards; D013223:Statistics as Topic / Q000379:methods; D013997:Time Factors
D015415:Biomarkers