SGCP

This is the development version of SGCP; for the stable release version, see SGCP.

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

Bioconductor version: Development (3.19)

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.

Author: Niloofar AghaieAbiane [aut, cre] , Ioannis Koutis [aut]

Maintainer: Niloofar AghaieAbiane <niloofar.abiane at gmail.com>

Citation (from within R, enter citation("SGCP")):

Installation

To install this package, start R (version "4.4") and enter:


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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

BiocManager::install("SGCP")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation


Reference Manual	PDF

Need some help? Ask on the Bioconductor Support site!

Details

biocViews	Classification, Clustering, DimensionReduction, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Network, NetworkEnrichment, NeuralNetwork, RNASeq, Software, SystemsBiology, Visualization, mRNAMicroarray
Version	1.3.0
In Bioconductor since	BioC 3.17 (R-4.3) (1 year)
License	GPL-3
Depends	R (>= 4.3.0)
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
System Requirements
URL	https://github.com/na396/SGCP

Suggests	knitr, BiocManager
Linking To
Enhances
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report	Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package
Windows Binary
macOS Binary (x86_64)
macOS Binary (arm64)
Source Repository	git clone https://git.bioconductor.org/packages/SGCP
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/SGCP
Package Short Url	https://bioconductor.org/packages/SGCP/
Package Downloads Report	Download Stats