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

Bioconductor version: Release (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>

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


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Reference Manual PDF


biocViews Classification, Clustering, DimensionReduction, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Network, NetworkEnrichment, NeuralNetwork, RNASeq, Software, SystemsBiology, Visualization, mRNAMicroarray
Version 1.4.0
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,, methods, grDevices, stats, RSpectra, graph
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