Extended Data Fig. 8: Alternative measures of the single perturbation prediction performance. | Nature Methods

Extended Data Fig. 8: Alternative measures of the single perturbation prediction performance.

From: Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines

Extended Data Fig. 8

(a) The Pearson delta measure calculates the correlation of the prediction and observations after subtracting the expression in the control condition. The horizontal red lines show the mean per model and the dashed line indicates the correlation of the best-performing model. (b) Prediction error as a function of n, the number of read-out genes. Top: genes ranked by expression in the control condition. Bottom: by differential expression between observed value and expression in the control condition. Note that sorting by differential expression is only possible if access to the ground truth is available and can thus not be applied in real-world use cases. The dashed line at n = 1000 is the choice in Panel a and elsewhere in this work. LM: linear model, DL: deep learning.

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