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SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species

View ORCID ProfileYuqi Tan, Patrick Cahan
doi: https://doi.org/10.1101/508085
Yuqi Tan
1Institute for Cell Engineering Johns Hopkins University School of Medicine Baltimore, Maryland, 21205 USA
3Department of Molecular Biology and Genetics Johns Hopkins University School of Medicine Baltimore, Maryland, 21205 USA
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  • ORCID record for Yuqi Tan
Patrick Cahan
1Institute for Cell Engineering Johns Hopkins University School of Medicine Baltimore, Maryland, 21205 USA
2Department of Biomedical Engineering Johns Hopkins University School of Medicine Baltimore, Maryland, 21205 USA
3Department of Molecular Biology and Genetics Johns Hopkins University School of Medicine Baltimore, Maryland, 21205 USA
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Summary

Single cell RNA-Seq has emerged as a powerful tool in diverse applications, ranging from determining the cell-type composition of tissues to uncovering the regulators of developmental programs. A near-universal step in the analysis of single cell RNA-Seq data is to hypothesize the identity of each cell. Often, this is achieved by finding cells that express combinations of marker genes that had previously been implicated as being cell-type specific, an approach that is not quantitative and does not explicitly take advantage of other single cell RNA-Seq studies. Here, we describe our tool, SingleCellNet, which addresses these issues and enables the classification of query single cell RNA-Seq data in comparison to reference single cell RNA-Seq data. SingleCellNet compares favorably to other methods, and it is notably able to make sensitive and accurate classifications across platforms and species. We demonstrate how SingleCellNet can be used to classify previously undetermined cells, and how it can be used to assess the outcome of cell fate engineering experiments.

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  • SingleCellNet (SCN) enables the classification of scRNA-Seq data across platforms and species

  • SCN is open source and extendible

  • We illustrate the utility of SCN with three example applications

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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-NC-ND 4.0 International license.
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Posted December 31, 2018.
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SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species
Yuqi Tan, Patrick Cahan
bioRxiv 508085; doi: https://doi.org/10.1101/508085
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SingleCellNet: a computational tool to classify single cell RNA-Seq data across platforms and across species
Yuqi Tan, Patrick Cahan
bioRxiv 508085; doi: https://doi.org/10.1101/508085

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