Abstract
BackgroundQuantitative 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.ResultsMultiple 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.ConclusionJointly 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.
Highlights
Quantitative analysis of differential protein expressions requires to align temporal elution measurements from liquid chromatography coupled to mass spectrometry (LC/MS)
Jointly aligning multiple liquid chromatography/mass spectrometry samples by multiple Canonical Correlation Analysis (mCCA) substantially increases the detection rate of potential bio-markers which significantly improves the interpretability of LC/MS data
Liquid chromatography coupled to mass spectrometry (LC/MS) has emerged as the technology of choice for the quantitative analysis of proteins over the last decade
Summary
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. Liquid chromatography coupled to mass spectrometry (LC/MS) has emerged as the technology of choice for the quantitative analysis of proteins over the last decade. One of the standard methods for aligning mass spectrometry experiments is called correlation optimized warping (COW) [1], where piece-wise linear functions are fitted to align pairs of time series. A hidden Markov model [2] was proposed to align the mass spectrometry data as well as acoustic time series. In [3] the model was extended to use (page number not for citation purposes)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
