Journal article
DiffNetFDR: differential network analysis with false discovery rate control
Bioinformatics, v 35(17), pp 3184-3186
01 Sep 2019
PMID: 30689728
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Abstract
Summary
To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks.
Availability and implementation
R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR.
Supplementary information
Supplementary data are available at Bioinformatics online.
Metrics
Details
- Title
- DiffNetFDR: differential network analysis with false discovery rate control
- Creators
- Xiao-Fei Zhang - Central China Normal UniversityLe Ou-Yang - Shenzhen UniversityShuo Yang - Wuhan No.1 HospitalXiaohua Hu - Drexel UniversityHong Yan - City University of Hong Kong
- Contributors
- Oliver Stegle (Editor)
- Publication Details
- Bioinformatics, v 35(17), pp 3184-3186
- Publisher
- Oxford University Press
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000487323400048
- Scopus ID
- 2-s2.0-85072046160
- Other Identifier
- 991019167595204721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Computer Science, Interdisciplinary Applications
- Mathematical & Computational Biology
- Statistics & Probability