Extended Data Fig. 3: Additional APARENT Attributions and Comparisons.
From: Interpreting neural networks for biological sequences by learning stochastic masks
(a) Top: Example attribution of a polyadenylation signal sequence from the APARENT test set, using Inclusion-Scramblers trained with increasingly large tbits of conservation. Known regulatory motifs annotated. Bottom: Two additional example attributions, showing only the results for the tbits = 1.0 case. (b) Non-trainable convolutional filter with a 1D gaussian kernel (filter width 6) is prepended to the final softplus activation function of the Scrambler. (c) APARENT isoform predictions of original sequences and of corresponding sampled sequences from the PSSMs predicted by an Occlusion-Scrambler trained with tbits = 1.8, with and without the Gaussian filter. (d) Example attributions of tbits = 1.8 - and 1.5 Occlusion-Scramblers, with and without the Gaussian filter. (e) Example attributions of a polyadenylation signal sequence, comparing different methods. (f) Comparison of attribution methods on the ‘Inclusion’-benchmark of Fig. 3b (Perturbing the input patterns by keeping the top X% most important features according to each method and replacing all other features with random samples from a background distribution, n=1,737). Median KL-divergences are computed between original predictions and predictions made on perturbed input patterns (lower is better). These predictions were made using the APARENT model. Shown are also the Spearman r correlation coefficients between original and perturbed predictions using the DeeReCT-APA model (higher is better). The default Scrambler network for the APA task uses 4 residual blocks, while the ‘Deep’ architecture uses a total of 20 residual blocks. The best method(s) are highlighted in green. (g) Attributions of four human polyadenylation signals which are associated with known deleterious variants, comparing the Perturbation method to the reconstructive Inclusion-Scrambler (tbits = 0.25 target bits) on hypothetical variants which have not been found in the population. Gene names and clinical condition associated with the PAS annotated above each sequence. In each of the four examples, the Scrambler correctly detects the loss of the presumed RBP binding site or otherwise important motif due to each respective variant (loss of the CstF binding motif in FOXC1, TP53; loss of the SRSF10 binding motif in INS; loss of the T-rich DSE motif in HBB). (h) Left: Example attributions of a medium-strength polyadenylation signal sequence, using three Scramblers which have been optimized for different objectives: (Reconstructive features) reconstructing the original prediction, (Negative features) minimizing the prediction, and (Positive features) maximizing the prediction. Right: APARENT isoform predictions of original sequences and of corresponding sampled sequences from the PSSMs predicted by the Negative-feature and Positive-feature Scrambler respectively.
