PCA is a well-established technique in multivariate statistics; the objective is to determine a new coordinate system such that the first coordinate (named "the first principal component") explains the maximal amount of variance in the data and successive components explain maximal variance while being orthogonal to the first. Therefore, PCA identifies the proportion of variance captured in each of the components. Results of the sporulation data are presented below :

Figure legend : (A) Microarray results for the 724 genes induced during the sporulation process in S. pombe were analyzed by PCA. The amount of variability captured in each of the components were calculated. The first three principal components account for more than 95% of the global variance in the genes used for this analysis.

Figure legend : (B) Microarray results for the 528 genes induced during the sporulation process in S. cerevisiae were analyzed by PCA. The amount of variability captured in each of the components were calculated. The first three principal components account for more than 93% of the global variance in the genes used for this analysis.