Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Superscan: Supervised Single-Cell Annotation

View ORCID ProfileCarolyn Shasha, Yuan Tian, Florian Mair, Helen E.R. Miller, Raphael Gottardo
doi: https://doi.org/10.1101/2021.05.20.445014
Carolyn Shasha
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Carolyn Shasha
Yuan Tian
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Florian Mair
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Helen E.R. Miller
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Raphael Gottardo
1Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: rgottard@fredhutch.org
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Automated cell type annotation of single-cell RNA-seq data has the potential to significantly improve and streamline single cell data analysis, facilitating comparisons and meta-analyses. However, many of the current state-of-the-art techniques suffer from limitations, such as reliance on a single reference dataset or marker gene set, or excessive run times for large datasets. Acquiring high-quality labeled data to use as a reference can be challenging. With CITE-seq, surface protein expression of cells can be directly measured in addition to the RNA expression, facilitating cell type annotation. Here, we compiled and annotated a collection of 16 publicly available CITE-seq datasets. This data was then used as training data to develop Superscan, a supervised machine learning-based prediction model. Using our 16 reference datasets, we benchmarked Superscan and showed that it performs better in terms of both accuracy and speed when compared to other state-of-the-art cell annotation methods. Superscan is pre-trained on a collection of primarily PBMC immune datasets; however, additional data and cell types can be easily added to the training data for further improvement. Finally, we used Superscan to reanalyze a previously published dataset, demonstrating its applicability even when the dataset includes cell types that are missing from the training set.

Competing Interest Statement

R.G. has received consulting income from Illumina and declares ownership in Ozette Technologies and minor stock ownerships in 10X Genomics.

Footnotes

  • https://github.com/cshasha/superscan

  • https://fh-pi-gottardo-r-eco-public.s3.amazonaws.com/SingleCellDatasets/SingleCellDatasets.html

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.
Back to top
PreviousNext
Posted May 22, 2021.
Download PDF

Supplementary Material

Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Superscan: Supervised Single-Cell Annotation
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Superscan: Supervised Single-Cell Annotation
Carolyn Shasha, Yuan Tian, Florian Mair, Helen E.R. Miller, Raphael Gottardo
bioRxiv 2021.05.20.445014; doi: https://doi.org/10.1101/2021.05.20.445014
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Superscan: Supervised Single-Cell Annotation
Carolyn Shasha, Yuan Tian, Florian Mair, Helen E.R. Miller, Raphael Gottardo
bioRxiv 2021.05.20.445014; doi: https://doi.org/10.1101/2021.05.20.445014

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4237)
  • Biochemistry (9157)
  • Bioengineering (6797)
  • Bioinformatics (24052)
  • Biophysics (12149)
  • Cancer Biology (9562)
  • Cell Biology (13814)
  • Clinical Trials (138)
  • Developmental Biology (7653)
  • Ecology (11730)
  • Epidemiology (2066)
  • Evolutionary Biology (15534)
  • Genetics (10663)
  • Genomics (14349)
  • Immunology (9503)
  • Microbiology (22883)
  • Molecular Biology (9118)
  • Neuroscience (49080)
  • Paleontology (357)
  • Pathology (1487)
  • Pharmacology and Toxicology (2576)
  • Physiology (3851)
  • Plant Biology (8348)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2299)
  • Systems Biology (6202)
  • Zoology (1302)