BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-expression Analysis

Published: 28 Jun 2016 | Version 1 | DOI: 10.17632/jdz4vtvnm3.1
Contributor(s):
  • Duolin Wang,
    Biochemistry, Genetics and Molecular Biology
    Jilin University and University of missouri columbia
    Design and realize the package
  • Juexin Wang,
    Juexin Wang
    University of Missouri Columbia
    Test the package and review the documents
  • Yuexu Jiang
    Yuexu Jiang
    Jilin University and University of missouri columbia
    Test the package and review the documents.

Description of this data

BFDCA is a comprehensive tool of using Bayes factor for Differential Co-expression (DC) analysis. This package contains three main functions:
(1) clustering condition-specific genes into functional DC subunits;
(2) quantitatively characterizing the regulatory impact of genes based on their differential connectivity within DC structures;
(3) providing a DC-based prediction model to predict case/control phenotypes by taking DC significant gene pairs as markers.

Experiment data files

Steps to reproduce

Users could read the BFDCAVignette.html for quick start of this R package. More details about BFDCA are in the BFDCA-manual.pdf.

This data is associated with the following publication:

BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis

Published in: Journal of Molecular Biology

Latest version

  • Version 1

    2016-06-28

    Published: 2016-06-28

    DOI: 10.17632/jdz4vtvnm3.1

    Cite this dataset

    Wang, Duolin; Wang, Juexin; Jiang, Yuexu (2016), “BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-expression Analysis”, Mendeley Data, v1 http://dx.doi.org/10.17632/jdz4vtvnm3.1

Statistics

Views: 224
Downloads: 36

Institutions

University of Missouri Columbia, Jilin University

Categories

Software, Bioinformatics, Gene Expression, Differential Gene Expression

Licence

CC BY 4.0 Learn more

The files associated with this dataset are licensed under a Creative Commons Attribution 4.0 International licence.

What does this mean?
You can share, copy and modify this dataset so long as you give appropriate credit, provide a link to the CC BY license, and indicate if changes were made, but you may not do so in a way that suggests the rights holder has endorsed you or your use of the dataset. Note that further permission may be required for any content within the dataset that is identified as belonging to a third party.

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