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, Qiangfeng Cliff Zhang

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish (US)
JournalNature Communications
Volume13
Issue number1
DOIs
StatePublished - Oct 17 2022

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Chemistry(all)
  • Physics and Astronomy(all)

Fingerprint

Dive into the research topics of 'Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space'. Together they form a unique fingerprint.

Cite this