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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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Pseudo-Code for non-negative CCA algorithm.
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Related In: Results  -  Collection

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Figure 5: Pseudo-Code for non-negative CCA algorithm.

Mentions: This problem is again solved iteratively by minimizing the objective function with respect to one vector βk while keeping the other vectors βl, l ≠ k fixed. The pseudo code is given in Figure 5.


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

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

Pseudo-Code for non-negative CCA algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Pseudo-Code for non-negative CCA algorithm.
Mentions: This problem is again solved iteratively by minimizing the objective function with respect to one vector βk while keeping the other vectors βl, l ≠ k fixed. The pseudo code is given in Figure 5.

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