ScJVAE: A novel method for integrative analysis of multimodal single-cell data. (May 2023)

• Record Type: Journal Article
• Authors: Wani, Shahid Ahmad; Khan, Sumeer Ahmad; Quadri, S.M.K.
• Is Part Of: Computers in biology and medicine. Volume 158(2023)
• Publication Date: 2023-05
• Subjects: Multiomics integration -- Cell clustering -- Deep learning -- Batch effect -- Single-cell genomics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2023.106865
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: scJVAE learns a comprehensive joint representation of paired single-cell RNA and ATAC data modalities. scJVAE outperforms state-of-art methods in batch effect removal and integration tasks. Nonlinear representation of single-cell data by scJVAE characterizes cells efficiently and preserves biology. scJVAE is a robust method to handle smaller datasets and scalable with large datasets.