scAEGAN: Unification of single-cell genomics data by adversarial learning of latent space correspondences.

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Khan SA, Lehmann R, Martinez-de-Morentin X, Maillo A, Lagani V, Kiani NA, Gomez-Cabrero D, Tegner J

PLoS ONE 18 (2) e0281315 [2023-02-03; online 2023-02-03]

Recent progress in Single-Cell Genomics has produced different library protocols and techniques for molecular profiling. We formulate a unifying, data-driven, integrative, and predictive methodology for different libraries, samples, and paired-unpaired data modalities. Our design of scAEGAN includes an autoencoder (AE) network integrated with adversarial learning by a cycleGAN (cGAN) network. The AE learns a low-dimensional embedding of each condition, whereas the cGAN learns a non-linear mapping between the AE representations. We evaluate scAEGAN using simulated data and real scRNA-seq datasets, different library preparations (Fluidigm C1, CelSeq, CelSeq2, SmartSeq), and several data modalities as paired scRNA-seq and scATAC-seq. The scAEGAN outperforms Seurat3 in library integration, is more robust against data sparsity, and beats Seurat 4 in integrating paired data from the same cell. Furthermore, in predicting one data modality from another, scAEGAN outperforms Babel. We conclude that scAEGAN surpasses current state-of-the-art methods and unifies integration and prediction challenges.

PubMed 36735690

DOI 10.1371/journal.pone.0281315

Crossref 10.1371/journal.pone.0281315

pmc: PMC9897517
pii: PONE-D-22-30403