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Related Experiment Videos

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

Bernd Fischer1, Volker Roth, Joachim M Buhmann

  • 1Institute of Computational Science, ETH Zurich, Switzerland. bernd.fischer@inf.ethz.ch

BMC Bioinformatics
|February 27, 2008
PubMed
Summary
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Multiple Canonical Correlation Analysis (mCCA) robustly aligns liquid chromatography-mass spectrometry (LC/MS) data. This improves the detection of differentially expressed proteins and enhances biomarker discovery in proteomics.

Area of Science:

  • Proteomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Quantitative analysis of differential protein expression necessitates accurate alignment of temporal elution measurements from liquid chromatography-mass spectrometry (LC/MS).
  • Non-linear distortions in time scales of repeated LC/MS experiments pose a significant challenge for robust data alignment.

Purpose of the Study:

  • To introduce and validate multiple Canonical Correlation Analysis (mCCA) as a robust method for aligning non-linearly distorted time scales in LC/MS experiments.
  • To enhance the quantitative analysis of differential protein expressions by improving the alignment of temporal elution measurements.

Main Methods:

  • Application of multiple Canonical Correlation Analysis (mCCA) to map multiple time series onto a consensus time scale.
  • Supervised learning approach to derive the alignment function.

Related Experiment Videos

  • Comparison of mCCA with existing alignment methods on a large-scale proteomics dataset.

    • Main Results:

    • mCCA successfully maps multiple time series to a consensus time scale.
    • The proposed mCCA method significantly increases the number of identified differentially expressed proteins compared to previous methods.
    • Demonstrated robustness and effectiveness on a large proteomics dataset.

    Conclusions:

    • Jointly aligning multiple LC/MS samples using mCCA substantially increases the detection rate of potential biomarkers.
    • This improved alignment significantly enhances the interpretability of LC/MS data in proteomics research.
    • mCCA offers a powerful tool for advancing quantitative proteomics and biomarker discovery.