Analysis of longitudinal metabolomics data

• DOI: 10.1093/bioinformatics/bth268
• Publication Date: 2004-04-20T00:33:32Z
• Authors: Jeroen J. Jansen; Huub C. J. Hoefsloot; Hans F. M. Boelens; Jan van der Greef; Age K. Smilde
• Keywords: Longitudinal data

Abstract Motivation: Metabolomics datasets are generally large and complex. Using principal component analysis (PCA), a simplified view of the variation in data is obtained. The PCA model can be interpreted processes underlying analysed. In metabolomics, often priori information present about data. Various forms this used an unsupervised with weighted (WPCA). A WPCA will give on that different from obtained using PCA, it add to interpretation metabolomics dataset. Results: method presented translate spectra repeated measurements into weights describing experimental error. These WPCA. where non-uniform error accounted for. Therefore, focus more natural Availability: M-files for MATLAB algorithm research available at http://www-its.chem.uva.nl/research/pac/Software/pcaw.zip