gwena

Gene Co-expression Network analysis pipeline

https://github.com/kumquatum/gwena

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Keywords

co-expression enrichment-analysis gene network-analysis pipeline transcriptomics

Keywords from Contributors

bioconductor-package grna-sequence immune-repertoire ontology sequencing genomics proteomics statistical-analysis data-access microbiome-analysis
Last synced: 6 months ago · JSON representation

Repository

Gene Co-expression Network analysis pipeline

Basic Info
  • Host: GitHub
  • Owner: Kumquatum
  • License: gpl-3.0
  • Language: R
  • Default Branch: master
  • Homepage:
  • Size: 10.1 MB
Statistics
  • Stars: 19
  • Watchers: 4
  • Forks: 3
  • Open Issues: 10
  • Releases: 0
Topics
co-expression enrichment-analysis gene network-analysis pipeline transcriptomics
Created almost 7 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

GWENA

Overview

GWENA (Gene Whole co-Expression Network Analysis) is an R package to perform gene co-expression network analysis in a single pipeline. This pipeline includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparisons of networks between conditions.

Figure 1. Analysis pipeline of GWENA, from expression data to characterization of the modules and comparison of conditions.

Using transcriptomics data from either RNA-seq or microarray, the package follows the steps displayed in Figure 1:

  1. Input: data is provided as a data.frame or a matrix of expression intensities (pre-normalized).
  2. Gene filtering: data is filtered according to the transcriptomic technology used.
  3. Network building: a matrix of similarity score is computed between each gene with Spearman correlation, then transformed into an adjacency matrix, and finally into a topological overlap matrix.
  4. Modules detection: groups of genes with closest similarity scores are detected as modules.
  5. Biological integration: gene set enrichment analysis and phenotypic association (if phenotypes are provided) are performed on modules.
  6. Graph and topological analysis: identification of hub genes is made available, as well as modules visualization.
  7. Networks comparison: if multiple conditions are available (time points, treatments, phenotype, etc.), analysis of modules preservation/non-preservation between conditions can be performed.

Installation

```R

Prerequisites

if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") if (!requireNamespace("devtools", quietly=TRUE)) install.packages("devtools") ```

Installation can either be from:

  1. the official version of the last Bioconductor release (recommended). R BiocManager::install("GWENA")
  2. the last stable version from the Bioc Devel branch. R # 2. From Bioconductor devel BiocManager::install("GWENA", version = "devel")
  3. the day-to-day development version from the Github repository. R # 3. From Github repository BiocManager::install("Kumquatum/GWENA") # OR devtools::install_github("Kumquatum/GWENA")

Package tutorial

A vignette is available at vignette/GWENA_guide.Rmd. To see the html version, use vignette("GWENA_guide"). Note: if you want to install from Github, please specify build_vignettes = TRUE in install_github.

Licence and use

GWENA is a free software; you can redistribute it and/or modify it under the terms of the GNU General Public License 3 (GPL 3) as published by the Free Software Foundation. It is distributed in the hope that it will be useful, but without any warranty and without even the implied warranty of merchantability or fitness for a particular purpose. See the GNU General Public License for more details.

Contact

  • If you have any question, feel free to send a mail to the author and maintainer Gwenaëlle Lemoine at lemoine.gwenaelle[@t)gmail{d0t]com.
  • To report a bug, please use the Github issues system.

Owner

  • Name: Gwenaëlle Lemoine
  • Login: Kumquatum
  • Kind: user

Post-doc at Mines ParisTech - GWAS networks on auto immune diseases / PhD in molecular medicine, gene co-expression networks in aging / Bioinfo-fr.net admin

GitHub Events

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Last Year
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Last synced: over 2 years ago

All Time
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  • Avg Commits per committer: 106.25
  • Development Distribution Score (DDS): 0.028
Past Year
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  • Avg Commits per committer: 2.0
  • Development Distribution Score (DDS): 0.667
Top Committers
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