Asca
Purpose
ANOVA-simultaneous component analysis (ASCA) is a method to determine which factors within a fixed effects experimental design are significant relative to the residual error. ASCA permits an ANOVA-like analysis even when there are many more variables than samples. ASCA is implemented following Smilde et al, "ANOVA-simultaneous component analysis (ASCA): a new tool for analyzing designed metabolomics data", Bioinformatics, 2005.
Synopsis
- [model] = asca(x, F);
- [model] = asca(x, F, ncomp);
- [model] = asca(x, F, ncomp, options);
Description
Build an ASCA model by applying ASCA to X-block data, X, measured according to an experimental design, F. An ASCA model is intended to show which factors have a significant in explaining the experimental data. A P-value estimating the significance of each factor or interaction is calculated based on a permutation test of the factor's levels.
Inputs
- X = first input is this.
- F = first input is this.
Optional Inputs
- ncomp = optional second input is this.
Outputs
- firstout = first output is this.
Options
options = a structure array with the following fields:
- plots: [ {'none'} | 'final' ] governs plotting of results, and
- order: positive integer for polynomial order {default = 1}.
Example
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