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


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Documentation for package ‘SGCP’ version 1.9.0

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adjacencyMatrix Performs netwrok construction step in the SGCP pipeline
cheng Normalized gene expression data from Cheng et al.'s publication on ischemic cardiomyopathy (ICM).
clustering Perform network clustering step in the SGCP pipeline
ezSGCP Integrated execution of the SGCP pipeline
geneOntology Performs gene ontology enrichment step in the SGCP pipeline.
resClus An example of the output from 'clustering' function in the SGCP pipeline
resFinalGO An example of the output from 'geneOntololgy' function in the SGCP pipeline
resInitialGO An example of the output from the 'geneOntololgy' function in the SGCP pipeline
resSemiLabel An example of the output from 'semiLabeling' function in the SGCP pipeline
resSemiSupervised An example of the output from 'semiSupervised' function in the SGCP pipeline
semiLabeling Performs gene semi-labeling step in the SGCP pipeline
semiSupervised Performs the semi-supervised step in the SGCP pipeline
sgcp An example of the output of 'ezSGCP' function in the SGCP pipeline
SGCP_ezPLOT Comprehensive SGCP plotting in one execution
SGCP_plot_bar Mean p-value bar chart for gene ontology enrichment in the SGCP pipeline
SGCP_plot_conductance Cluster conductance index bar chart in the SGCP Pipeline
SGCP_plot_density Visualization of gene ontology term p-value distribution in the SGCP pipeline
SGCP_plot_heatMap Adjacency matrix heatmap in the SGCP pipeline
SGCP_plot_jitter P-value jitter chart for gene ontology enrichment in the SGCP pipeline.
SGCP_plot_pca PCA visualization in the SGCP Pipeline
SGCP_plot_pie Gene ontology analysis pie chart in the SGCP pipeline
SGCP_plot_silhouette Cluster silhouette index chart in the SGCP Pipeline