“…The leap in computational power and data availability has enabled high-parameter complex neural networks to disrupt the computational paradigm in several fields including computational biology [25,26]. Specifically, in single-cell analysis, various components of the data processing pipeline such as batch effect correction [27][28][29][30], automatic celltype and population identification [31,32], data compression and visualization [23], missing data imputation [23,33], and end-to-end clinical outcome classification [34] have been developed and are on par, or more frequently superior to the performance of traditional machine learning methods. However, as discussed earlier, the potential of deep learning is limited by the availability of sufficient training data.…”