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

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

School of Medicine

Research output: Contribution to journal › Article › peer-review

35 Scopus citations

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 language	English (US)
Pages (from-to)	395-411.e8
Journal	Cell Systems
Volume	8
Issue number	5
DOIs	https://doi.org/10.1016/j.cels.2019.04.004
State	Published - 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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10.1016/j.cels.2019.04.004

Cite this

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

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.",

keywords = "NMF, developmental biology, dimension reduction, integrated analysis, latent spaces, retina, scRNA-seq, single cells, transfer learning",

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language = "English (US)",

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T1 - Decomposing Cell Identity for Transfer Learning across Cellular Measurements, Platforms, Tissues, and Species

AU - Stein-O'Brien, Genevieve L.

AU - Clark, Brian S.

AU - Sherman, Thomas

AU - Zibetti, Cristina

AU - Hu, Qiwen

AU - Sealfon, Rachel

AU - Liu, Sheng

AU - Qian, Jiang

AU - Colantuoni, Carlo

AU - Blackshaw, Seth

AU - Goff, Loyal A.

AU - Fertig, Elana J.

PY - 2019/5/22

Y1 - 2019/5/22

N2 - 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.

AB - 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.

KW - NMF

KW - developmental biology

KW - dimension reduction

KW - integrated analysis

KW - latent spaces

KW - retina

KW - scRNA-seq

KW - single cells

KW - transfer learning

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JO - Cell Systems

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

Abstract

Keywords

ASJC Scopus subject areas

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