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Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning

Advances in single-cell technologies enable the routine interrogation of chromatin accessibility for tens of thousands of single cells, elucidating gene regulatory processes at an unprecedented resolution. Meanwhile, size, sparsity and high dimensionality of the resulting data continue to pose challenges for its computational analysis, and specifically the integration of data from different sources. We have developed a dedicated computational approach: a variational auto-encoder using a noise model specifically designed for single-cell ATAC-seq (assay for transposase-accessible chromatin with high-throughput sequencing) data, which facilitates simultaneous dimensionality reduction and batch correction via an adversarial learning strategy. We showcase its benefits for detailed cell-type characterization on individual real and simulated datasets as well as for integrating multiple complex datasets. High-throughput single-cell sequencing data can provide valuable biological insights but are computationally challenging to analyse due to the dimensionality of the data and batch-specific biases. Kopp and colleagues have developed a variational auto-encoder-based method using a novel loss function for simultaneous batch correction and dimensionality reduction.