pcxn_explore_analyze: Discover correlated pathway/gene sets of a single...

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

Description

Using pcxn_explore, select a single pathway/gene set from one of the four collections ( MSigDB H hallmark gene sets, MSigDB C2 Canonical pathways, MSigDB C5 GO BP gene sets, and Pathprint ) and discover its correlated pathway/gene sets within the same collection.

Using pcxn_analyze, discover correlation relationships among multiple pathways/gene sets identified by GSEA (gene set enrichment analysis). All the input pathways/gene sets should come from the same collection. MSigDB H hallmark gene sets, MSigDB C2 Canonical pathways, MSigDB C5 GO BP gene sets, and Pathprint are treated as four separate collections.

Usage

pcxn_explore(collection = c("pathprint", "MSigDB_H","MSigDB_C2_CP",
                            "MSigDB_C5_GO_BP"),
            query_geneset,
            adj_overlap = FALSE,
            top = 10,
            min_abs_corr = 0.05,
            max_pval = 0.05)

pcxn_analyze(collection = c("pathprint", "MSigDB_H","MSigDB_C2_CP",
                            "MSigDB_C5_GO_BP"),
            phenotype_0_genesets,
            phenotype_1_genesets,
            adj_overlap = FALSE,
            top = 10,
            min_abs_corr = 0.05,
            max_pval = 0.05)

Arguments

collection	pathways' collection chosen among: "pathprint", "MSigDB_H", "MSigDB_C2_CP", "MSigDB_C5_GO_BP"
query_geneset	the single pathway of interest
phenotype_0_genesets	genesets/pathways of the first group of pathways
phenotype_1_genesets	genesets/pathways of the second group of pathways
adj_overlap	whether the correlation coefficients are adjusted for gene overlap
top	most correlated genesets/pathways
min_abs_corr	minimum absolute correlation
max_pval	maximum p-value

Value

a pcxn object

Author(s)

Sokratis Kariotis

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

# pcxn_explore function can be used with the default parameters:
pcxn_explore("pathprint","Alzheimer's disease (KEGG)")


# If specific parameters are desired we can use the full list of arguments:
pcxn_explore("pathprint","Alzheimer's disease (KEGG)", FALSE,
                                100, 0.02, 0.045)

# pcxn_analyze can be used with two gene sets and the default parameters:
pcxn_analyze("pathprint",c("ABC transporters (KEGG)",
                            "ACE Inhibitor Pathway (Wikipathways)",
                            "AR down reg. targets (Netpath)"),
                            c("DNA Repair (Reactome)"))

# Alternatively, you can use only one gene set:
pcxn_analyze("MSigDB_H",c("HALLMARK_COAGULATION","HALLMARK_UV_RESPONSE_UP"))

# If specific parameters are desired we can use the full list of arguments:
pcxn_analyze("pathprint",c("ABC transporters (KEGG)",
                            "ACE Inhibitor Pathway (Wikipathways)",
                            "AR down reg. targets (Netpath)"),
                            c("DNA Repair (Reactome)"),
                            FALSE,
                            top = 100,
                            min_abs_corr = 0.025,
                            max_pval = 0.03)

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