bioconductor-sgcp

name

bioconductor-sgcp

description

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.

SGCP

Workflows

Standard Workflow

Construct gene co-expression networks, perform clustering, and apply semi-supervised classification using Gene Ontology information.

library(SGCP)
library(SummarizedExperiment)
library(org.Hs.eg.db)

# Load example dataset
data(cheng)
expData <- assay(cheng)
geneID <- rowData(cheng)$ENTREZID
annotation_db <- "org.Hs.eg.db"

# Option 1: Automatic Run using ezSGCP
# (Precomputed data loaded here to avoid long execution times)
data(sgcp)
summary(sgcp, show.data=


SGCP_ezPLOTsgcp  sgcp expreData  expData silhouette_index   keep  



resAdja  adjacencyMatrixexpData  expData hm  


dataresClus
summaryresClus


pl  SGCP_plot_pcam  expData clusLabs   tit   ps  
printpl