Data

Tools

Indexes

Applications

Experiments

Open Source Science

vissE

An R/Bioconductor package to identify higher-order molecular phenotypes

https://github.com/davislaboratory/visse

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ✓
    Committers with academic emails
    4 of 7 committers (57.1%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (12.8%) to scientific vocabulary

Keywords

bioinformatics

Keywords from Contributors

bioconductor-package gene bioconductor lipidomics ontology immune-repertoire grna-sequence interactive-visualizations heatmap molecular-biology
Last synced: 11 months ago · JSON representation

Repository

An R/Bioconductor package to identify higher-order molecular phenotypes

Basic Info
Statistics
  • Stars: 18
  • Watchers: 3
  • Forks: 1
  • Open Issues: 5
  • Releases: 0
Topics
bioinformatics
Created over 5 years ago · Last pushed over 2 years ago
Metadata Files
Readme License

README.md

vissE - Visualising Set Enrichment Analysis Results logo

R-CMD-check codecov BioC status

This package implements the vissE algorithm to summarise results of gene-set analyses. Usually, the results of a gene-set enrichment analysis (e.g using limma::fry, singscore or GSEA) consist of a long list of gene-sets. Biologists then have to search through these lists to determines emerging themes to explain the altered biological processes. This task can be labour intensive therefore we need solutions to summarise large sets of results from such analyses.

This package provides an approach to provide summaries of results from gene-set enrichment analyses. It exploits the relatedness between gene-sets and the inherent hierarchical structure that may exist in pathway databases and gene ontologies to cluster results. For each cluster of gene-sets vissE identifies, it performs text-mining to automate characterisation of biological functions and processes represented by the cluster.

An additional power of vissE is to perform a novel type of gene-set enrichment analysis based on the network of similarity between gene-sets. Given a list of genes (e.g. from a DE analysis), vissE can characterise said list by first identifying all other gene-sets that are similar to it, following up with clustering the resulting gene-sets and finally performing text-mining to reveal emerging themes.

In addition to these analyses, it provides visualisations to assist the users in understanding the results of their experiment.

Check out the full tutorial

Installation

vissE can be installed from Bioconductor directly as follows:

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

BiocManager::install("vissE") ```

Owner

  • Name: Davis Laboratory
  • Login: DavisLaboratory
  • Kind: organization
  • Location: Melbourne, Australia

computational systems biology of cancer

GitHub Events

Total
  • Issues event: 2
  • Watch event: 6
  • Issue comment event: 1
Last Year
  • Issues event: 2
  • Watch event: 6
  • Issue comment event: 1

Committers

Last synced: almost 3 years ago

All Time
  • Total Commits: 199
  • Total Committers: 7
  • Avg Commits per committer: 28.429
  • Development Distribution Score (DDS): 0.136
Past Year
  • Commits: 16
  • Committers: 4
  • Avg Commits per committer: 4.0
  • Development Distribution Score (DDS): 0.5
Top Committers
Name Email Commits
bhuva.d b****d@w****u 172
Dharmesh Bhuva d****a@a****u 8
Ahmed Mohamed a****0@g****m 7
Nitesh Turaga n****a@g****m 6
J Wokaty j****y@s****u 2
Ahmed Mohamed m****d@k****p 2
J Wokaty j****y 2
Committer Domains (Top 20 + Academic)
kuicr.kyoto-u.ac.jp: 1 sph.cuny.edu: 1 adelaide.edu.au: 1 wehi.edu.au: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 9
  • Total pull requests: 4
  • Average time to close issues: 10 months
  • Average time to close pull requests: 3 days
  • Total issue authors: 7
  • Total pull request authors: 2
  • Average comments per issue: 0.22
  • Average comments per pull request: 0.0
  • Merged pull requests: 4
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 2
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • chinweetan (3)
  • MafGal (1)
  • clarrity (1)
  • ftencaten (1)
  • ahmohamed (1)
  • lilac-moon (1)
  • cliang-huanglab (1)
Pull Request Authors
  • bhuvad (3)
  • ahmohamed (1)
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • bioconductor 9,445 total
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 5
  • Total maintainers: 1
bioconductor.org: vissE

Visualising Set Enrichment Analysis Results

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 9,445 Total
Rankings
Dependent repos count: 0.0%
Dependent packages count: 0.0%
Stargazers count: 8.5%
Forks count: 18.4%
Average: 21.6%
Downloads: 81.2%
Maintainers (1)
bhuva.d@wehi.edu.au
Last synced: 11 months ago