scJVAE: A novel method for integrative analysis of multimodal single-cell data.

The study of cellular decision-making can be approached comprehensively using multimodal single-cell omics technology. Recent advances in multimodal single-cell technology have enabled simultaneous profiling of more than one modality from the same cell, providing more significant insights into cell characteristics. However, learning the joint representation of multimodal single-cell data is challenging due to batch effects. Here we present a novel method, scJVAE (single-cell Joint Variational AutoEncoder), for batch effect removal and joint representation of multimodal single-cell data. The scJVAE integrates and learns joint embedding of paired scRNA-seq and scATAC-seq data modalities. We evaluate and demonstrate the ability of scJVAE to remove batch effects using various datasets with paired gene expression and open chromatin. We also consider scJVAE for downstream analysis, such as lower dimensional representation, cell-type clustering, and time and memory requirement. We find scJVAE a robust and scalable method outperforming existing state-of-the-art batch effect removal and integration methods.

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

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.