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

A Statistical Approach to Dimensionality Reduction Reveals Scale and Structure in scRNA-seq Data

Eric Johnson, William Kath, Madhav Mani
doi: https://doi.org/10.1101/2020.11.18.389031
Eric Johnson
1Department of Engineering Sciences and Applied Mathematics, Northwestern University
2NSF-Simons Center for Quantitative Biology at Northwestern University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: eric.johnson643@gmail.com
William Kath
1Department of Engineering Sciences and Applied Mathematics, Northwestern University
2NSF-Simons Center for Quantitative Biology at Northwestern University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Madhav Mani
1Department of Engineering Sciences and Applied Mathematics, Northwestern University
2NSF-Simons Center for Quantitative Biology at Northwestern University
3Department of Molecular Biosciences, Northwestern University
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Single-cell RNA sequencing (scRNA-seq) experiments often measure thousands of genes, making them high-dimensional data sets. As a result, dimensionality reduction (DR) algorithms such as t-SNE and UMAP are necessary for data visualization. However, the use of DR methods in other tasks, such as for cell-type detection or developmental trajectory reconstruction, is stymied by unquantified non-linear and stochastic deformations in the mapping from the high- to low-dimensional space. In this work, we present a statistical framework for the quantification of embedding quality so that DR algorithms can be used with confidence in unsupervised applications. Specifically, this framework generates a local assessment of embedding quality by statistically integrating information across embeddings. Furthermore, the approach separates biological signal from noise via the construction of an empirical null hypothesis. Using this approach on scRNA-seq data reveals biologically relevant structure and suggests a novel “spectral” decomposition of data. We apply the framework to several data sets and DR methods, illustrating its robustness and flexibility as well as its widespread utility in several quantitative applications.

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 4.0 International license.
Back to top
PreviousNext
Posted November 23, 2020.
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.
A Statistical Approach to Dimensionality Reduction Reveals Scale and Structure in scRNA-seq Data
(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
A Statistical Approach to Dimensionality Reduction Reveals Scale and Structure in scRNA-seq Data
Eric Johnson, William Kath, Madhav Mani
bioRxiv 2020.11.18.389031; doi: https://doi.org/10.1101/2020.11.18.389031
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
A Statistical Approach to Dimensionality Reduction Reveals Scale and Structure in scRNA-seq Data
Eric Johnson, William Kath, Madhav Mani
bioRxiv 2020.11.18.389031; doi: https://doi.org/10.1101/2020.11.18.389031

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 (3483)
  • Biochemistry (7336)
  • Bioengineering (5305)
  • Bioinformatics (20219)
  • Biophysics (9990)
  • Cancer Biology (7713)
  • Cell Biology (11280)
  • Clinical Trials (138)
  • Developmental Biology (6426)
  • Ecology (9928)
  • Epidemiology (2065)
  • Evolutionary Biology (13294)
  • Genetics (9353)
  • Genomics (12565)
  • Immunology (7686)
  • Microbiology (18979)
  • Molecular Biology (7428)
  • Neuroscience (40940)
  • Paleontology (300)
  • Pathology (1226)
  • Pharmacology and Toxicology (2132)
  • Physiology (3145)
  • Plant Biology (6850)
  • Scientific Communication and Education (1272)
  • Synthetic Biology (1893)
  • Systems Biology (5306)
  • Zoology (1086)