gene_map_pathway: Create Pathway-Gene Mapping Data Frame

In TransProR: Analysis and Visualization of Multi-Omics Data

Create Pathway-Gene Mapping Data Frame

Description

This function takes multiple data frames and pathway IDs, merging them into a new data frame. Each data frame represents a type of analysis (e.g., BP, KEGG, MF, etc.).

Usage

gene_map_pathway(
  BP_dataframe,
  BP_ids,
  KEGG_dataframe,
  KEGG_ids,
  MF_dataframe,
  MF_ids,
  REACTOME_dataframe,
  REACTOME_ids,
  CC_dataframe,
  CC_ids,
  DO_dataframe,
  DO_ids
)

Arguments

BP_dataframe	Data frame for Biological Process analysis
BP_ids	Selected pathway IDs for Biological Process analysis
KEGG_dataframe	Data frame for KEGG analysis
KEGG_ids	Selected pathway IDs for KEGG analysis
MF_dataframe	Data frame for Molecular Function analysis
MF_ids	Selected pathway IDs for Molecular Function analysis
REACTOME_dataframe	Data frame for REACTOME analysis
REACTOME_ids	Selected pathway IDs for REACTOME analysis
CC_dataframe	Data frame for Cellular Component analysis
CC_ids	Selected pathway IDs for Cellular Component analysis
DO_dataframe	Data frame for Disease Ontology analysis
DO_ids	Selected pathway IDs for Disease Ontology analysis

Value

A new data frame that includes pathways, gene, type, and value columns

Examples

# Simulating data for different analysis types

# Simulate Biological Process (BP) data frame
BP_df <- data.frame(
  ID = c("GO:0002376", "GO:0019724"),
  geneID = c("GENE1/GENE2", "GENE3/GENE4"),
  Description = c("Immune response", "Glycosylation process")
)

# Simulate KEGG data frame
KEGG_df <- data.frame(
  ID = c("12345", "67890"),
  geneID = c("GENE5/GENE6", "GENE7/GENE8"),
  Description = c("Pathway 1", "Pathway 2")
)

# Simulate Molecular Function (MF) data frame
MF_df <- data.frame(
  ID = c("ABC123", "DEF456"),
  geneID = c("GENE9/GENE10", "GENE11/GENE12"),
  Description = c("Molecular function A", "Molecular function B")
)

# Simulate REACTOME data frame
REACTOME_df <- data.frame(
  ID = c("R-HSA-12345", "R-HSA-67890"),
  geneID = c("GENE13/GENE14", "GENE15/GENE16"),
  Description = c("Pathway in Reactome 1", "Pathway in Reactome 2")
)

# Simulate Cellular Component (CC) data frame
CC_df <- data.frame(
  ID = c("GO:0005575", "GO:0005634"),
  geneID = c("GENE17/GENE18", "GENE19/GENE20"),
  Description = c("Cellular component A", "Cellular component B")
)

# Simulate Disease Ontology (DO) data frame
DO_df <- data.frame(
  ID = c("DOID:123", "DOID:456"),
  geneID = c("GENE21/GENE22", "GENE23/GENE24"),
  Description = c("Disease A", "Disease B")
)

# Example pathway IDs for each analysis
BP_ids <- c("GO:0002376", "GO:0019724")
KEGG_ids <- c("12345", "67890")
MF_ids <- c("ABC123", "DEF456")
REACTOME_ids <- c("R-HSA-12345", "R-HSA-67890")
CC_ids <- c("GO:0005575", "GO:0005634")
DO_ids <- c("DOID:123", "DOID:456")

# Generate the pathway-gene map using the gene_map_pathway function
pathway_gene_map <- gene_map_pathway(
  BP_dataframe = BP_df, BP_ids = BP_ids,
  KEGG_dataframe = KEGG_df, KEGG_ids = KEGG_ids,
  MF_dataframe = MF_df, MF_ids = MF_ids,
  REACTOME_dataframe = REACTOME_df, REACTOME_ids = REACTOME_ids,
  CC_dataframe = CC_df, CC_ids = CC_ids,
  DO_dataframe = DO_df, DO_ids = DO_ids
)

# Display the resulting pathway-gene mapping data frame
print(pathway_gene_map)

TransProR documentation built on April 4, 2025, 3:16 a.m.