(A) To infer fitness-relevant phenotypes, we measure the fitness of mutants in a collection of environments and compare their fitness profiles. Mutants with similar fitness profiles (mutants 1 and 2) are inferred to have similar effects on phenotypes. Mutants with dissimilar fitness profiles (mutants 3 and 4) are inferred to have dissimilar phenotypic effects. We use SVD to decompose these fitness profiles into a model consisting of two abstract spaces: one that represents the fitness-relevant phenotypes affected by mutants (P) and another which represents the degree to which each phenotype impacts fitness in each environment (E). Here, we represent the model with k fitness-relevant phenotypes. The model’s estimate for fitness for a particular mutant in a particular environment is a linear combination of each mutant phenotype (mutant one is represented by the vector ()) scaled by the degree to which that phenotype affects fitness in the relevant environment (environment one is represented by the vector ). We show two examples of the equation used to estimate fitness for the mutants and environments highlighted in the left panel. Note that, for presentation purposes, we show SVD as inferring two matrices. It in fact infers three, but is consistent with our presentation if you fold the third matrix, which represents the singular values, into E (see Materials and methods). (B) Decomposing the fitness profiles of 292 adaptive mutants across 25 subtle environmental perturbations reveals eight fitness-relevant phenotypic components. The variance explained by each component is indicated as a percentage of the total variance. The percentages in parentheses indicate the relative amount of variation explained by each component when excluding the first component. Each of these components explain more variation in fitness than do components that capture variation across a simulated dataset in which fitness varies due to measurement noise. These simulations were repeated 1000 times (gray lines) and used to define the limit of detection (dotted line). (C) An abstract space containing eight fitness-relevant phenotypic components reflects known biological features. This plot shows the relationships of the mutants in a seven-dimensional phenotypic space that excludes the first component, visualized using Uniform Manifold Approximation and Projection (UMAP). Mutants that are close together have similar fitness profiles and are inferred to have similar effects on fitness-relevant phenotypes. Mutants with mutations in the same gene tend to be closer together than random, in particular IRA1 nonsense mutants in dark blue, GPB2 mutants in dark green, PDE2 mutants in dark orange, and diploid mutants in red. Six diploid mutants that had higher than average diploid EC fitness (and thus are likely to harbor additional mutation(s) so are categorized as ‘diploid with additional mutation’) also form a cluster. Colors are as in Figure 2B; IRA1 missense mutants shown in light blue, IRA2 in dark gray, GPB1 in light green, other Ras/PKA pathway mutants in brown, TOR/Sch9 pathway mutants in purple, other adaptive mutants in light gray, and known neutral lineages in black.