Comparison of multidimensional scaling and principal component analysis of interspecific variation in bacteria

Multidimensional scaling (MDS) and principal component analysis (PCA) were applied to bacterial taxonomy. The biochemical profiles of 42 isolates consisting of four species of Enterobacteriaceae were used. Both MDS and PCA use proximity measures such as the correlation coefficient or Euclidean distance to generate a spatial configuration (map) of points in multidimensional space where distances between points reflect the similarity among isolates. Multidimensional scaling and principal component analysis were able to discriminate organisms in two dimensions. The test components of the MDS and PCA factors (derived variables composed of linear combination of biochemical tests) were different for a two-dimensional solution.