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

Genevieve L Stein-O'Brien; Brian S Clark; Thomas Sherman; Cristina Zibetti; Qiwen Hu; Rachel Sealfon; Sheng Liu; Jiang Qian; Carlo Colantuoni; Seth Blackshaw; Loyal A Goff; Elana J Fertig

doi:10.1016/j.cels.2019.04.004

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

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

Authors

Affiliations

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.

Publication types

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

MeSH terms

Algorithms
Animals
Databases, Genetic
Exome Sequencing / methods
Female
Gene Expression Profiling / methods*
Humans
Machine Learning
Male
Mice
Mice, Transgenic
Retina / embryology
Sequence Analysis, RNA / methods*
Single-Cell Analysis / methods*
Software
Transcriptome / genetics

Abstract

Publication types

MeSH terms

Grants and funding