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SGCP

SGCP: A semi-supervised pipeline for gene clustering using self-training approach in gene co-expression networks

Bioconductor version: 3.23 · Package version: 1.12.0

SGC is a semi-supervised pipeline for gene clustering in gene co-expression networks. SGC consists of multiple novel steps that enable the computation of highly enriched modules in an unsupervised manner. But unlike all existing frameworks, it further incorporates a novel step that leverages Gene Ontology information in a semi-supervised clustering method that further improves the quality of the computed modules.

Installation

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("SGCP")

Details

MaintainerNiloofar AghaieAbiane <niloofar.abiane@gmail.com>
AuthorNiloofar AghaieAbiane [aut, cre] (ORCID: <https://orcid.org/0000-0003-1096-7592>), Ioannis Koutis [aut]
LicenseGPL-3
URLhttps://github.com/na396/SGCP
StatusDeprecated
Downloads rank202
Source branchRELEASE_3_23
biocViewsClassification, Clustering, DimensionReduction, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Network, NetworkEnrichment, NeuralNetwork, RNASeq, Software, SystemsBiology, Visualization, mRNAMicroarray

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Dependencies

Depends: R (>= 4.2.0)

Imports: ggplot2, expm, caret, plyr, dplyr, GO.db, annotate, SummarizedExperiment, genefilter, GOstats, RColorBrewer, xtable, Rgraphviz, reshape2, openxlsx, ggridges, DescTools, org.Hs.eg.db, methods, grDevices, stats, RSpectra, graph

Suggests: knitr, rmarkdown, BiocManager, devtools, BiocStyle