The Primary function of the DAM is to serve as a method for multi-class classification. It incorporates the dimensional reduction method Multivariate Partial Least Squares (MPLS) and two classification analysis methods Polychotomous Discrimination (PDA) and Quadratic Discriminant Analysis (QDA). Either PDA or QDA are performed after the starting data has been reduced by MPLS.
The classification selection option indicates whether to classify genes or experiments.
Number Of Classes The number of classes.
Number Of Gene Components The number of gene components for Dimension Reduction.
Alpha The alpha value chosen for the t-distribution in preliminary gene screening.
The Initial Classification Algorithm and assessment algorithms A0, A1 & A2 essentially contain the same fundamental stages (i.e. Preliminary Gene Selection, Dimension Reduction(MPLS) & Classification/Prediction based on Leave One Out Cross Validation (LOOCV)) but they are ordered in a different sequence.
1. Preliminary Gene Selection: Use ANOVA to select genes from given gene expression matrix.
For each sample in the gene expression matrix, leave out this sample to obtain a sub expression matrix.
2. Dimension Reduction: Use MPLS algorithm to obtain gene components matrix from the sub gene expression matrix.
3. Classification/Prediction: Fit classifier to the remaining samples in gene components matrix. Use the fitted classifier to predict left out sample.
NOTE: The first modification to A0 given as A1 assesses the affects of dimension reduction. The dimension reduction as well as the classifier is refitted N times, one for each sample left out.
For each sample in the gene expression matrix, leave out this sample to obtain a sub expression matrix.
NOTE: The second modification to A0 given as A2 assesses the affects of gene selection. The gene set is reselected for classification each time a sample is left out.
The Classification methods are performed after the data has reduced by dimension reduction (MPLS)
Polychotomous Discrimination
Quadratic Discriminant Analysis