Application of multivariate curve resolution to the analysis of yeast genome-wide screens

Abstract

In this work, the application of Multivariate Curve Resolution to the analysis of yeast genome-wide screens obtained by means of DNA microarray technology is shown. In order to perform the analysis of this type of data, two algorithms based on Alternating Least Squares (MCR-ALS) and on its maximum likelihood weighted projection (MCR-WALS) variant are compared. The utilization of the modified weighted alternating least (WALS) squares algorithm is motivated by the rather poor quality, uncertainties and experimental noise associated to DNA microarray data. Moreover, a large number of missing values are usually present in these data sets and the weighted WALS approach allowed circumventing this problem. Two different experimental datasets were used for this comparison. In the first dataset, gene expression values in budding yeast were monitored in-response to glucose limitation. In the second dataset, the changes in the gene expression caused by the daunorubicin drug were monitored as a function of time. Results obtained by application of Multivariate Curve Resolution in the two cases allowed a good recovery of the evolving gene expression profiles and the identification of metabolic pathways and individual genes involved in these gene expression changes.