Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space

Lei Xiong, Kang Tian, Yuzhe Li, Weixi Ning, Xin Gao and Qiangfeng Cliff Zhang ()
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Lei Xiong: Tsinghua University
Kang Tian: Tsinghua University
Yuzhe Li: Tsinghua University
Weixi Ning: Tsinghua University
Xin Gao: King Abdullah University of Science and Technology (KAUST)
Qiangfeng Cliff Zhang: Tsinghua University

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: Abstract Computational tools for integrative analyses of diverse single-cell experiments are facing formidable new challenges including dramatic increases in data scale, sample heterogeneity, and the need to informatively cross-reference new data with foundational datasets. Here, we present SCALEX, a deep-learning method that integrates single-cell data by projecting cells into a batch-invariant, common cell-embedding space in a truly online manner (i.e., without retraining the model). SCALEX substantially outperforms online iNMF and other state-of-the-art non-online integration methods on benchmark single-cell datasets of diverse modalities, (e.g., single-cell RNA sequencing, scRNA-seq, single-cell assay for transposase-accessible chromatin use sequencing, scATAC-seq), especially for datasets with partial overlaps, accurately aligning similar cell populations while retaining true biological differences. We showcase SCALEX’s advantages by constructing continuously expandable single-cell atlases for human, mouse, and COVID-19 patients, each assembled from diverse data sources and growing with every new data. The online data integration capacity and superior performance makes SCALEX particularly appropriate for large-scale single-cell applications to build upon previous scientific insights.

Date: 2022
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DOI: 10.1038/s41467-022-33758-z

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