Abstract
A long-standing effort in biology is to precisely define and group phenotypes that characterize a biological process, and the genes that underpin them. In Saccharomyces cerevisiae and other organisms, functional screens have generated rich lists of phenotypes associated with individual genes. However, it is often challenging to identify sets of phenotypes and genes that are most closely associated with a given biological process. Here, we focused on the 166 phenotypes arising from loss-of-function and the 86 phenotypes from gain-of-function mutations in 571 genes currently assigned to cell cycle-related ontologies in S. cerevisiae To reduce this complexity, we applied unbiased, computational approaches of correspondence analysis to identify a minimum set of phenotypic variables that accounts for as much of the variability in the data as possible. Loss-of-function phenotypes can be reduced to 20 dimensions, while gain-of-function ones to 14 dimensions. We also pinpoint the contributions of phenotypes and genes in each set. The approach we describe not only simplifies the categorization of phenotypes associated with cell cycle progression but might also potentially serve as a discovery tool for gene function.