Fig. 2: Single perturbation prediction.
a, Beeswarm plot of the prediction errors for 134, 210 and 24 unseen single perturbations across two test–training splits (Methods). The prediction error is measured by the L2 distance between the mean predicted and observed expression profile of the n = 1,000 most highly expressed genes. The horizontal red lines show the mean per model, which, for the best-performing model, is extended by the dashed line. DL, deep learning; LM, linear model. b, Schematic of the LM and how it can accommodate available gene (G) or perturbation (P) embeddings. c, Forest plot comparing the performance of all models relative to the error of the ‘mean’ baseline. The point ranges show the overall mean and 95% confidence interval of the bootstrapped mean ratio between each model and the baseline for 134, 210 and 24 unseen single perturbations across two test–training splits. The opacity of the point range is reduced if the confidence interval contains 0. Source data for Fig. 2 are provided.
