Fig. 1

General overview of our approach. Expression data act as input to the autoencoder (b) which models the data. The model’s representation of the data set can be visualized by a dimensionality reduction plot (c). The impact of gene sets of interest to our representation method can be visualized, either for the whole data set (d) or for a comparison between two groups of cells (e). b: A general outlook of an autoencoder artificial neural network. The autoencoder shown has an input, a hidden and an output layer, but it is common that it contains more hidden layers. Usually the hidden layer in the middle of the network acts as the representation layer, which contains the compressed information of the original data. The representation is decompressed in the output layer, where the input is recreated with some accuracy. a & c: Uniform Manifold Approximation and Projection (UMAP) of Paul et al. The UMAP of the original input data is visualized on (a) and UMAP of the evaluation of the representation layer, after training is done, is visualized on (c). We can see that the neighboring structure of the original input data is retained in the representation layer. d & e: Heatmaps of the impact of the Hallmark molecular pathways on the representation layer of the autoencoder trained on Paul et al. The impact is computed via saliency maps (see Methods section). To enhance visual clarity, only the high impact pathways are visualized. We plot the impact of the gene signatures for the whole dataset (d) and for the comparison between two groups of the dataset, CMP CD41 and Cebpe control, which also includes differentiated cells (e). The comparison is done by subtracting the impact of the hallmark pathways of one group versus the other. The difference in impact is overlaid on the “general” heatmap (d)