pcxn {pcxn} | R Documentation |
Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint).
Package: | pcxn |
Type: | Package |
Version: | 2.0.0 |
Date: | 2018-4-1 |
License: | MIT |
Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei
Maintainer: Sokratis Kariotis s.kariotis@sheffield.ac.uk
Pita-Juarez Y.,Altschuler G.,Kariotis S.,Wei W.,Koler K.,Tanzi R. and W. A. Hide (2018). "The Pathway Coexpression Network: Revealing Pathway Relationships."
library(pcxnData) # load the data ds = c("cp_gs_v5.1", "gobp_gs_v5.1", "h_gs_v5.1","pathprint.Hs.gs", "pathCor_CPv5.1_dframe", "pathCor_CPv5.1_unadjusted_dframe", "pathCor_GOBPv5.1_dframe", "pathCor_GOBPv5.1_unadjusted_dframe", "pathCor_Hv5.1_dframe", "pathCor_Hv5.1_unadjusted_dframe", "pathCor_pathprint_v1.2.3_dframe", "pathCor_pathprint_v1.2.3_unadjusted_dframe") data(list = ds) # Explore the static extendable network (correlation coefficients are adjusted # for gene overlap) by focusing on single pathways and their 10 most correlated # neighbours in the pathprint collection pcxn.obj <- pcxn_explore(collection = "pathprint", query_geneset = "Alzheimer's disease (KEGG)", adj_overlap = TRUE, top = 10, min_abs_corr = 0.05, max_pval = 0.05) # Explore the static extendable network (correlation coefficients are not # adjusted for gene overlap) by focusing on single pathways and their # 10 most correlated neighbours in the pathprint collection pcxn.obj <- pcxn_explore(collection = "pathprint", query_geneset = "Alzheimer's disease (KEGG)", adj_overlap = FALSE, top = 10, min_abs_corr = 0.05, max_pval = 0.05) # Analyse relationships between groups of pathways shown to be enriched in the # collection by gene set enrichment (correlation coefficients are adjusted # for gene overlap) pcxn.obj <- pcxn_analyze(collection = "pathprint", phenotype_0_genesets = c("ABC transporters (KEGG)", "ACE Inhibitor Pathway (Wikipathways)", "AR down reg. targets (Netpath)"), phenotype_1_genesets = c("DNA Repair (Reactome)"), adj_overlap = TRUE, top = 10, min_abs_corr = 0.05, max_pval = 0.05 ) # Analyse relationships between groups of pathways shown to be enriched in the # collection by gene set enrichment (correlation coefficients are not adjusted # for gene overlap) pcxn.obj <- pcxn_analyze(collection = "pathprint", phenotype_0_genesets = c("ABC transporters (KEGG)", "ACE Inhibitor Pathway (Wikipathways)", "AR down reg. targets (Netpath)"), phenotype_1_genesets = c("DNA Repair (Reactome)"), adj_overlap = FALSE, top = 10, min_abs_corr = 0.05, max_pval = 0.05 ) # Generate the heatmap for any pcxn object generated by the pcxn_explore() or # pcxn_analyze() function hm <- pcxn_heatmap(pcxn.obj , cluster_method = "complete") # Get the gene members (Entrez Ids and names) of any pathway/geneset in the # available collections genesets_list <- pcxn_gene_members(pathway_name = "Alzheimer's disease (KEGG)") # Create a network for any pcxn object generated by the pcxn_explore() or # pcxn_analyze() function # network <- pcxn_network(pcxn.obj)