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

Modular and efficient pre-processing of single-cell RNA-seq

View ORCID ProfilePáll Melsted, View ORCID ProfileA. Sina Booeshaghi, Fan Gao, View ORCID ProfileEduardo Beltrame, View ORCID ProfileLambda Lu, View ORCID ProfileKristján Eldjárn Hjorleifsson, View ORCID ProfileJase Gehring, View ORCID ProfileLior Pachter
doi: https://doi.org/10.1101/673285
Páll Melsted
1Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, University of Iceland, Reykjavik, Iceland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Páll Melsted
A. Sina Booeshaghi
2Department of Mechanical Engineering, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for A. Sina Booeshaghi
Fan Gao
3Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Eduardo Beltrame
3Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Eduardo Beltrame
Lambda Lu
3Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lambda Lu
Kristján Eldjárn Hjorleifsson
4Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Kristján Eldjárn Hjorleifsson
Jase Gehring
3Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jase Gehring
Lior Pachter
3Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California
4Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Lior Pachter
  • For correspondence: lpachter@caltech.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Data/Code
  • Preview PDF
Loading

Abstract

Analysis of single-cell RNA-seq data begins with pre-processing of sequencing reads to generate count matrices. We investigate algorithm choices for the challenges of pre-processing, and describe a workflow that balances efficiency and accuracy. Our workflow is based on the kallisto (https://pachterlab.github.io/kallisto/) and bustools (https://bustools.github.io/) programs, and is near-optimal in speed and memory. The workflow is modular, and we demonstrate its flexibility by showing how it can be used for RNA velocity analyses. Documentation and tutorials for using the kallisto | bus workflow are available at https://www.kallistobus.tools/.

Footnotes

  • Additional RNA velocity analysis (spliced vs standard workflow). Additional plot (MA plot) to benchmark panel figures. Additional info on IO in supplementary table. Additional references. Fixed typos. Updated all files.

  • https://github.com/pachterlab/MBGBLHGP_2019

  • https://pachterlab.github.io/kallistobustools/

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.
Back to top
PreviousNext
Posted July 26, 2019.
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.
Modular and efficient pre-processing of single-cell RNA-seq
(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
Modular and efficient pre-processing of single-cell RNA-seq
Páll Melsted, A. Sina Booeshaghi, Fan Gao, Eduardo Beltrame, Lambda Lu, Kristján Eldjárn Hjorleifsson, Jase Gehring, Lior Pachter
bioRxiv 673285; doi: https://doi.org/10.1101/673285
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Modular and efficient pre-processing of single-cell RNA-seq
Páll Melsted, A. Sina Booeshaghi, Fan Gao, Eduardo Beltrame, Lambda Lu, Kristján Eldjárn Hjorleifsson, Jase Gehring, Lior Pachter
bioRxiv 673285; doi: https://doi.org/10.1101/673285

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 (4681)
  • Biochemistry (10357)
  • Bioengineering (7670)
  • Bioinformatics (26330)
  • Biophysics (13523)
  • Cancer Biology (10683)
  • Cell Biology (15438)
  • Clinical Trials (138)
  • Developmental Biology (8497)
  • Ecology (12820)
  • Epidemiology (2067)
  • Evolutionary Biology (16850)
  • Genetics (11394)
  • Genomics (15477)
  • Immunology (10614)
  • Microbiology (25206)
  • Molecular Biology (10220)
  • Neuroscience (54455)
  • Paleontology (401)
  • Pathology (1668)
  • Pharmacology and Toxicology (2897)
  • Physiology (4342)
  • Plant Biology (9243)
  • Scientific Communication and Education (1586)
  • Synthetic Biology (2557)
  • Systems Biology (6779)
  • Zoology (1466)