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Cell Syst. 2019 May 22;8(5):395-411.e8. doi: 10.1016/j.cels.2019.04.004.

Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species.

Author information

1
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA; McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD, USA.
2
Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA.
3
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA.
4
Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
5
Flatiron Institute, New York, NY, USA.
6
Department of Ophthalmology, Johns Hopkins University, Baltimore, MD, USA.
7
Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA.
8
Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA; Kavli Neurodiscovery Institute, Johns Hopkins University, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA; Department of Ophthalmology, Johns Hopkins University, Baltimore, MD, USA; Center for Human Systems Biology, Johns Hopkins University, Baltimore, MD, USA.
9
Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, USA; Kavli Neurodiscovery Institute, Johns Hopkins University, Baltimore, MD, USA; McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA.
10
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, USA; McKusick-Nathans Institute for Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA; Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA; Mathematical Institute for Data Science, Johns Hopkins University, Baltimore, MD, USA; Institute for Cell Engineering, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering and Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA. Electronic address: ejfertig@jhmi.edu.

Abstract

Analysis of gene expression in single cells allows for decomposition of cellular states as low-dimensional latent spaces. However, the interpretation and validation of these spaces remains a challenge. Here, we present scCoGAPS, which defines latent spaces from a source single-cell RNA-sequencing (scRNA-seq) dataset, and projectR, which evaluates these latent spaces in independent target datasets via transfer learning. Application of developing mouse retina to scRNA-Seq reveals intrinsic relationships across biological contexts and assays while avoiding batch effects and other technical features. We compare the dimensions learned in this source dataset to adult mouse retina, a time-course of human retinal development, select scRNA-seq datasets from developing brain, chromatin accessibility data, and a murine-cell type atlas to identify shared biological features. These tools lay the groundwork for exploratory analysis of scRNA-seq data via latent space representations, enabling a shift in how we compare and identify cells beyond reliance on marker genes or ensemble molecular identity.

KEYWORDS:

NMF; developmental biology; dimension reduction; integrated analysis; latent spaces; retina; scRNA-seq; single cells; transfer learning

PMID:
31121116
DOI:
10.1016/j.cels.2019.04.004
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