Application of multidimensional scaling in numerical taxonomy: analysis of isoenzyme types of Candida species.

Multidimensional scaling (MDS) was applied to the numerical taxonomy of Candida species based on isoenzyme profiles. Multidimensional scaling uses proximity measures to generate a spatial configuration of points in multidimensional space where distances between points reflect similarity among types. The biochemical profiles of 35 types of Candida species based on 26 tests consisting of isoenzymes of alpha-glucosidase, alkaline phosphatase, glucose-6-phosphate dehydrogenase, malate dehydrogenase, isocitrate dehydrogenase, and superoxide dismutase were analyzed. Cluster analysis of MDS, using the Euclidean distance as a proximity measure, separated C. tropicalis and C. paratropicalis from C. albicans and C. stellatoidea. Stepwise multiple linear regression revealed the isoenzyme tests which influenced each of the MDS dimensions. MDS was able to reduce the dimensionality of the test profile.