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

A unified framework for geneset network analysis

View ORCID ProfileViola Fanfani, View ORCID ProfileGiovanni Stracquadanio
doi: https://doi.org/10.1101/699926
Viola Fanfani
*School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Viola Fanfani
Giovanni Stracquadanio
*School of Biological Sciences, The University of Edinburgh, Edinburgh EH9 3BF, United Kingdom
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Giovanni Stracquadanio
  • For correspondence: giovanni.stracquadanio@ed.ac.uk
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Gene and protein interaction experiments provide unique opportunities to study their wiring in a cell. Integrating this information with high-throughput functional genomics data can help identifying networks associated with complex diseases and phenotypes.

Here we propose a unified statistical framework to test network properties of single and multiple genesets. We focused on testing whether a geneset exhibits network properties and if two genesets are strongly interacting with each other.

We then assessed power and false discovery rate of the proposed tests, showing that tests based on a probabilistic model of gene and protein interaction are the most robust.

We implemented our tests in an open-source framework, called Python Geneset Network Analysis (PyGNA), which provides an integrated environment for network studies. While most available tools are designed as web applications, we designed PyGNA to be easily integrated into existing high-performance data analysis pipelines.

Our software is available on GitHub (http://github.com/stracquadaniolab/pygna) and can be easily installed from PyPi or anaconda.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted July 11, 2019.
Download PDF

Supplementary Material

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 unified framework for geneset network analysis
(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 unified framework for geneset network analysis
Viola Fanfani, Giovanni Stracquadanio
bioRxiv 699926; doi: https://doi.org/10.1101/699926
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A unified framework for geneset network analysis
Viola Fanfani, Giovanni Stracquadanio
bioRxiv 699926; doi: https://doi.org/10.1101/699926

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 (4687)
  • Biochemistry (10370)
  • Bioengineering (7687)
  • Bioinformatics (26356)
  • Biophysics (13542)
  • Cancer Biology (10708)
  • Cell Biology (15449)
  • Clinical Trials (138)
  • Developmental Biology (8506)
  • Ecology (12829)
  • Epidemiology (2067)
  • Evolutionary Biology (16874)
  • Genetics (11406)
  • Genomics (15488)
  • Immunology (10631)
  • Microbiology (25240)
  • Molecular Biology (10232)
  • Neuroscience (54548)
  • Paleontology (402)
  • Pathology (1670)
  • Pharmacology and Toxicology (2898)
  • Physiology (4349)
  • Plant Biology (9260)
  • Scientific Communication and Education (1587)
  • Synthetic Biology (2558)
  • Systems Biology (6785)
  • Zoology (1469)