Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach.
Partial least squares discriminant analysis (PLS-DA) is a partial least squares regression of a set Y of binary variables describing the categories of a categorical variable on a set X of predictor variables. It is a compromise between the usual discriminant analysis and a discriminant analysis on the significant principal components of the predictor variables. This technique is specially suited to deal with a much larger number of predictors than observations and with multicollineality, two of the main problems encountered when analysing microarray expression data. We explore the performance of PLS-DA with published data from breast cancer (Perou et al. 2000). Several such analyses were carried out: (1) before vs after chemotherapy treatment, (2) estrogen receptor positive vs negative tumours, and (3) tumour classification. We found that the performance of PLS-DA was extremely satisfactory in all cases and that the discriminant cDNA clones often had a sound biological interpretation. We conclude that PLS-DA is a powerful yet simple tool for analysing microarray data.
Pérez-Enciso
Miguel
M
Station d'Amélioration Génétique des Animaux, Institut National de la Recherche Agronomique, BP 27, 31326 Castanet-Tolosan, France. mperez@toulouse.inra.fr
Tenenhaus
Michel
M
D003627:Data Interpretation, Statistical; D004358:Drug Therapy; D006801:Humans; D016018:Least-Squares Analysis; D009369:Neoplasms / Q000145:classification / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D011960:Receptors, Estrogen / Q000378:metabolism
D011960:Receptors, Estrogen