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

The rise of sparser single-cell RNAseq datasets; consequences and opportunities

View ORCID ProfileGerard A. Bouland, View ORCID ProfileAhmed Mahfouz, View ORCID ProfileMarcel J.T. Reinders
doi: https://doi.org/10.1101/2022.05.20.492823
Gerard A. Bouland
1Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
2Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gerard A. Bouland
Ahmed Mahfouz
1Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
2Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
3Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Ahmed Mahfouz
  • For correspondence: a.mahfouz@lumc.nl m.j.t.reinders@tudelft.nl
Marcel J.T. Reinders
1Delft Bioinformatics Lab, Delft University of Technology, Delft, The Netherlands
2Department of Human Genetics, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
3Leiden Computational Biology Center, Leiden University Medical Center, Leiden 2333ZC, The Netherlands
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Marcel J.T. Reinders
  • For correspondence: a.mahfouz@lumc.nl m.j.t.reinders@tudelft.nl
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

There is an exponential increase in the number of cells measured in single-cell RNA sequencing (scRNAseq) datasets. Concurrently, scRNA-seq datasets become increasingly sparser as more zero counts are measured for many genes. We discuss that with increasing sparsity the binarized representation of gene expression becomes as informative as count-based expression. We show that downstream analyses based on binarized gene expressions give similar results to analyses based on count-based expressions. Moreover, a binarized representation scales to 17-fold more cells that can be analyzed using the same amount of computational resources. Based on these observations, we recommend the development of specialized tools for bit-aware implementations for downstream analyses tasks, creating opportunities to get a more fine-grained resolution of biological heterogeneity.

Competing Interest Statement

The authors have declared no competing interest.

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 4.0 International license.
Back to top
PreviousNext
Posted May 21, 2022.
Download PDF
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.
The rise of sparser single-cell RNAseq datasets; consequences and opportunities
(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
The rise of sparser single-cell RNAseq datasets; consequences and opportunities
Gerard A. Bouland, Ahmed Mahfouz, Marcel J.T. Reinders
bioRxiv 2022.05.20.492823; doi: https://doi.org/10.1101/2022.05.20.492823
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
The rise of sparser single-cell RNAseq datasets; consequences and opportunities
Gerard A. Bouland, Ahmed Mahfouz, Marcel J.T. Reinders
bioRxiv 2022.05.20.492823; doi: https://doi.org/10.1101/2022.05.20.492823

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 (4840)
  • Biochemistry (10767)
  • Bioengineering (8026)
  • Bioinformatics (27216)
  • Biophysics (13947)
  • Cancer Biology (11096)
  • Cell Biology (16019)
  • Clinical Trials (138)
  • Developmental Biology (8764)
  • Ecology (13256)
  • Epidemiology (2067)
  • Evolutionary Biology (17332)
  • Genetics (11670)
  • Genomics (15891)
  • Immunology (11005)
  • Microbiology (26023)
  • Molecular Biology (10620)
  • Neuroscience (56412)
  • Paleontology (417)
  • Pathology (1729)
  • Pharmacology and Toxicology (2999)
  • Physiology (4534)
  • Plant Biology (9611)
  • Scientific Communication and Education (1610)
  • Synthetic Biology (2677)
  • Systems Biology (6963)
  • Zoology (1508)