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

Fischer B, Roth V, Buhmann JM - BMC Bioinformatics (2007)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch

ABSTRACT

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.

Conclusion: 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.

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A sketch of an LC/MS alignment. The crosses depict detected peaks, the circles depict identified peaks.
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Figure 1: A sketch of an LC/MS alignment. The crosses depict detected peaks, the circles depict identified peaks.

Mentions: The above differential protein expression estimate is the mean of the log-ratios of peptide expressions over all peptides that correspond to a particular protein. Due to unknown ionization efficiency and digestion rate only the differential protein expression value can be reliably estimated [6,7]; absolute expression level cannot be robustly measured in precision experiments. The basis for estimating differential protein expressions is a large set of peptides that are measured in both samples. This work primarily addresses the issue to reliably find correspondences between peptide measurements in several replicated samples. Liquid chromatography/mass spectrometry (LC/MS) allows us to measure the amount of peptide ions. Figure 1 schematically depicts two LC/MS experiments. The time corresponds to the retention time when the peptide ion elutes from the liquid chromatography column. Ions with the same peptide structure will elute within a small time window. After some preprocessing (see [6]) one gets a list of peaks within the two dimensional image with a mass/charge coordinate and a time coordinate. Each cross in Figure 1 depicts a peptide (with a certain charge state). In addition, the amount of peptide ions pai is measured by the peak intensities.


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

Fischer B, Roth V, Buhmann JM - BMC Bioinformatics (2007)

A sketch of an LC/MS alignment. The crosses depict detected peaks, the circles depict identified peaks.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230505&req=5

Figure 1: A sketch of an LC/MS alignment. The crosses depict detected peaks, the circles depict identified peaks.
Mentions: The above differential protein expression estimate is the mean of the log-ratios of peptide expressions over all peptides that correspond to a particular protein. Due to unknown ionization efficiency and digestion rate only the differential protein expression value can be reliably estimated [6,7]; absolute expression level cannot be robustly measured in precision experiments. The basis for estimating differential protein expressions is a large set of peptides that are measured in both samples. This work primarily addresses the issue to reliably find correspondences between peptide measurements in several replicated samples. Liquid chromatography/mass spectrometry (LC/MS) allows us to measure the amount of peptide ions. Figure 1 schematically depicts two LC/MS experiments. The time corresponds to the retention time when the peptide ion elutes from the liquid chromatography column. Ions with the same peptide structure will elute within a small time window. After some preprocessing (see [6]) one gets a list of peaks within the two dimensional image with a mass/charge coordinate and a time coordinate. Each cross in Figure 1 depicts a peptide (with a certain charge state). In addition, the amount of peptide ions pai is measured by the peak intensities.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch

ABSTRACT

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.

Conclusion: 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.

Show MeSH