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

Research output: Contribution to journalArticlepeer-review

Abstract

We present tools and workflows for latent space exploration across datasets. scCoGAPS is an implementation of NNMF that is specifically suited for large, sparse scRNA-seq datasets. ProjectR implements a transfer-learning framework that rapidly projects new data into learned latent spaces. We demonstrate the utility of this approach for de novo annotation of new datasets, cross-species analysis, linking genomic regulatory and transcriptional signatures, and exploration of features across a catalog of cell types.

Original languageEnglish (US)
Pages (from-to)395-411.e8
JournalCell Systems
Volume8
Issue number5
DOIs
StatePublished - May 22 2019

Keywords

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

ASJC Scopus subject areas

  • Pathology and Forensic Medicine
  • Histology
  • Cell Biology

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