Figure 4. Analyses of vertebral patterns using the global test and Moran’s I are useful in discriminating somatic mutant phenotypes.

(A-C) Monte Carlo simulations of somatic vertebral patterns were analyzed for sensitivity in detecting differences in phenotypic measures. Representative mosaic patterns are shown for simulations of microscale (A) and macroscale (B) loss-of-function clusters. Values indicate extent of gene loss. Sensitivity of the global test is higher compared to univariate approaches for simulated mutants with microscale (A’) or macroscale (B’) loss-of-function clusters. (C) Sensitivity of the global test in detecting changes in centrum volume compared to univariate approaches for simulated mutants with microscale loss-of-function clusters and different degrees of intra-animal variability (parameterized by lambda). Improvement of the global test over univariate tests is heightened as mosaicism increases (i.e., as lambda goes to zero). (D) Bootstrap analysis using experimentally-derived data from plod2 somatic mutants. The global test out-performs M.-W. tests for 6 of the 7 statistically significant phenotypic measures detected for plod2 somatic mutants and is nearly as sensitive for the final measure. For details on bootstrap analysis, (e.g. use of n) see STAR Methods. (E) Schematic of Moran’s I for different simulated patterns of mosaicism. (E’) Moran’s I changes with characteristic effect size, d. Simulated microscale loss-of-function clusters decrease Moran’s I, whereas simulated macroscale clusters increase it. (F,F’) Moran’s I quantified for each of the 10 primary combinatorial measures in plod2 (F) and bmpla (F’) somatic mutants. (G,G’) Combined distributions of all quantities calculated for Moran’s I shown in panels F and F’. Moran’s I for plod2 mutants (G) shows a shift towards a random distribution compared to controls, while that of bmpla mutants (G’) does not.