scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning

Nat Biotechnol. 2022 May;40(5):703-710. doi: 10.1038/s41587-021-01161-6. Epub 2022 Jan 20.

Abstract

Single-cell multiomics data continues to grow at an unprecedented pace. Although several methods have demonstrated promising results in integrating several data modalities from the same tissue, the complexity and scale of data compositions present in cell atlases still pose a challenge. Here, we present scJoint, a transfer learning method to integrate atlas-scale, heterogeneous collections of scRNA-seq and scATAC-seq data. scJoint leverages information from annotated scRNA-seq data in a semisupervised framework and uses a neural network to simultaneously train labeled and unlabeled data, allowing label transfer and joint visualization in an integrative framework. Using atlas data as well as multimodal datasets generated with ASAP-seq and CITE-seq, we demonstrate that scJoint is computationally efficient and consistently achieves substantially higher cell-type label accuracy than existing methods while providing meaningful joint visualizations. Thus, scJoint overcomes the heterogeneity of different data modalities to enable a more comprehensive understanding of cellular phenotypes.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chromatin Immunoprecipitation Sequencing*
  • Exome Sequencing
  • Machine Learning
  • RNA-Seq
  • Sequence Analysis, RNA
  • Single-Cell Analysis* / methods