pcxn-package: Exploring, analyzing and visualizing functions utilizing the...

In hidelab/pcxn: Exploring, analyzing and visualizing functions utilizing the pcxnData package

Description

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).

Details

Package:	pcxn
Type:	Package
Version:	2.0.0
Date:	2018-4-1
License:	MIT

Author(s)

Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei
Maintainer: Sokratis Kariotis [e-mail]

References

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."

Examples

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)

hidelab/pcxn documentation built on May 21, 2019, 3:03 a.m.