Bootstrap confidence intervals for principal response curves
Computational Statistics & Data Analysis
Two-way analysis of high-dimensional collinear data
Data Mining and Knowledge Discovery
| Hi-index | 3.84 |
Motivation: Datasets resulting from metabolomics or metabolic profiling experiments are becoming increasingly complex. Such datasets may contain underlying factors, such as time (time-resolved or longitudinal measurements), doses or combinations thereof. Currently used biostatistics methods do not take the structure of such complex datasets into account. However, incorporating this structure into the data analysis is important for understanding the biological information in these datasets. Results: We describe ASCA, a new method that can deal with complex multivariate datasets containing an underlying experimental design, such as metabolomics datasets. It is a direct generalization of analysis of variance (ANOVA) for univariate data to the multivariate case. The method allows for easy interpretation of the variation induced by the different factors of the design. The method is illustrated with a dataset from a metabolomics experiment with time and dose factors. Availability: M-files for MATLAB for the algorithm used in this research are available at: http://www-its.chem.uva.nl/research/pac/Software/ or at http://www.bdagroup.nl Contact: asmilde@science.uva.nl