Data are the new gold, and single-cell genomics is a good match for data-hungry machine-learning algorithms. Machine learning has become increasingly crucial in single-cell genomics. Recent progress in machine learning6, primarily image classification, has been revolutionized by convolutional neural networks. The trick is to focus on local patches of an image and then build up the whole image step by step -- similar to, and inspired by, the way that hierarchies of receptive fields have been discovered in the human brain. Such convolutional neural networks have become state-of-the-art tools for several prediction problems in genomics and bioinformatics, such as the prediction of transcription-factor binding sites, analysis of genetic variants, sequence analysis and protein conformation prediction7.