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Northstar enables automatic classification of known and novel cell types from tumor samples

View ORCID ProfileFabio Zanini, View ORCID ProfileBojk A. Berghuis, View ORCID ProfileRobert C. Jones, Benedetta Nicolis di Robilant, View ORCID ProfileRachel Yuan Nong, Jeffrey Norton, Michael F. Clarke, View ORCID ProfileStephen R. Quake
doi: https://doi.org/10.1101/820928
Fabio Zanini
1Department of Bioengineering, Stanford University, Stanford, CA, USA
2Prince of Wales Clinical School, Lowy Cancer Research Centre, University of New South Wales, Sydney, Australia
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  • For correspondence: fabio.zanini@unsw.edu.au quake@stanford.edu
Bojk A. Berghuis
1Department of Bioengineering, Stanford University, Stanford, CA, USA
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Robert C. Jones
1Department of Bioengineering, Stanford University, Stanford, CA, USA
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Benedetta Nicolis di Robilant
3Department of Oncology, Stanford School of Medicine, Stanford, CA, USA
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Rachel Yuan Nong
1Department of Bioengineering, Stanford University, Stanford, CA, USA
4Department of Immunology, Genetics and Pathology and SciLifeLab, Uppsala University, Uppsala, Sweden
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Jeffrey Norton
5Department of Surgery - Stanford Pancreas Cancer Research Group, General Surgery, Stanford University School of Medicine, Stanford, CA, USA
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Michael F. Clarke
3Department of Oncology, Stanford School of Medicine, Stanford, CA, USA
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Stephen R. Quake
1Department of Bioengineering, Stanford University, Stanford, CA, USA
6Department of Applied Physics, Stanford University, Stanford, CA, USA
7Chan Zuckerberg Biohub, San Francisco, CA, USA
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  • For correspondence: fabio.zanini@unsw.edu.au quake@stanford.edu
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Abstract

Single cell transcriptomics is revolutionising our understanding of tissue and disease heterogeneity, yet cell type identificationl remains a partially manual task. Published algorithms for automatic cell annotation are limited to known cell types and fail to capture novel populations, especially cancer cells. We developed northstar, a computational approach to classify thousands of cells based on published data within seconds while simultaneously identifying and highlighting new cell states such as malignancies. We tested northstar on human glioblastoma and melanoma and obtained high accuracy and robustness. We collected eleven pancreatic tumors and identified three shared and five private neoplastic cell populations, offering insight into the origins of neuroendocrine and exocrine tumors. northstar is a useful tool to assign known and novel cell type and states in the age of cell atlases.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • New version with some additional panels and instructions.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted April 25, 2020.
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Northstar enables automatic classification of known and novel cell types from tumor samples
Fabio Zanini, Bojk A. Berghuis, Robert C. Jones, Benedetta Nicolis di Robilant, Rachel Yuan Nong, Jeffrey Norton, Michael F. Clarke, Stephen R. Quake
bioRxiv 820928; doi: https://doi.org/10.1101/820928
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Northstar enables automatic classification of known and novel cell types from tumor samples
Fabio Zanini, Bojk A. Berghuis, Robert C. Jones, Benedetta Nicolis di Robilant, Rachel Yuan Nong, Jeffrey Norton, Michael F. Clarke, Stephen R. Quake
bioRxiv 820928; doi: https://doi.org/10.1101/820928

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