A Partial Least Squares Algorithm for Microarray Data Analysis Using the VIP Statistic for Gene Selection and Binary Classification
Francisco J. Burguillo, Luis A. Corchete, Javier Martin, Inmaculada Barrera and William G. Bardsley
Affiliation: Departamento de Química Física, Facultad de Farmacia, Universidad de Salamanca, 37080-Salamanca, Spain.
Keywords: Classification, gene selection, microarray, partial least squares, PLS, VIP statistic.
An important application of microarray technology is the assignment of new subjects to known clinical groups
(class prediction), but the huge number of screened genes and the small number of samples make this task difficult. To
overcome this problem, the usual approach has been to extract a small subset of significant genes (gene selection) or to
use the whole set of genes to build latent components (dimension reduction), then applying some usual multivariate
classification procedure. Alternatively, both aims -gene selection and class prediction- can be achieved at the same time
by using methods based on Partial Least Squares (PLS), as reported in the present work.
We present an iterative PLS algorithm based on backward variable elimination through the “Variable Influence on
Projection” (VIP) statistic, which finds an optimal PLS model through training and test sets. It simultaneously manages to
reduce the number of selected genes by an iterative procedure and finds the best number of PLS factors to reach an
optimal classification performance. It is a simple approach that uses only one mathematical method, maintains the
identification of discriminatory genes, and builds an optimal predicting model with a fast computation. The algorithm runs
as a module of the SIMFIT statistical package, where the optimal model and datasets can be re-run to further interpret the
system through additional PLS options, such as scores and loadings plots, or class assignment of new samples.
The proposed algorithm was tested under different scenarios occurring in microarray analysis using simulated data. The
results are also compared against different classification methods such as KNN, PAM, SVM, RF and standard PLS.
Rights & PermissionsPrintExport