### R code from vignette source 'PathNet.Rnw' ################################################### ### code chunk number 1: options ################################################### options(width=60, continue=" ") set.seed(123) ################################################### ### code chunk number 2: Install packages (eval = FALSE) ################################################### ## if (!requireNamespace("BiocManager", quietly=TRUE)) ## install.packages("BiocManager") ## BiocManager::install("PathNet") ## BiocManager::install("PathNetData") ################################################### ### code chunk number 3: Import Package and show Disease Progression ################################################### library("PathNet") library("PathNetData") data(disease_progression) head(disease_progression) ################################################### ### code chunk number 4: Show Brain Regions ################################################### data(brain_regions) head(brain_regions) ################################################### ### code chunk number 5: Show Adjacency Matrix ################################################### data(A) # Load genes from direct evidence gene_ID <- brain_regions[,1] # Construct adjacency matrix A <- A [rownames(A) %in% gene_ID, rownames(A) %in% gene_ID] # Display a sample of the adjacency matrix contents A [100:110,100:110] ################################################### ### code chunk number 6: Show pathway ################################################### data(pathway) pathway[965:975,] ################################################### ### code chunk number 7: Show custom loading ################################################### # We use system.file to locate the directory with the # example text files from the PathNetData Package current <- getwd() setwd(system.file(dir="extdata", package="PathNetData")) # Begin loading datasets from the text files brain_regions <- as.matrix(read.table( file = "brain_regions_data.txt", sep = "\t", header = T)) disease_progression <- as.matrix(read.table( file = "disease_progression_data.txt", sep = "\t", header = T)) A <- as.matrix(read.table( file = "adjacency_data.txt", sep = "\t", header = T)) pathway <- read.table( file = "pathway_data.txt", sep = "\t", header = T) # Change back to our previous working directory setwd(current) ################################################### ### code chunk number 8: Enrichment Analysis ################################################### # Note we use a subset of evidence and a small number of # permutations for demonstration purposes results <- PathNet(Enrichment_Analysis = TRUE, DirectEvidence_info = brain_regions[1:2000,], Adjacency = A, pathway = pathway, Column_DirectEvidence = 7, n_perm = 10, threshold = 0.05) ################################################### ### code chunk number 9: Enrichment Results ################################################### # Retrieve the first ten entrichment results results$enrichment_results[1:10,] # Retrieve the first ten combined evidence entries results$enrichment_combined_evidence[1:10,] ################################################### ### code chunk number 10: Contextual analysis ################################################### # Perform a contextual analysis with pathway enrichment # Note we use a subset of evidence and a small number of # permutations for demonstration purposes results <- PathNet(Enrichment_Analysis = FALSE, Contextual_Analysis= TRUE, DirectEvidence_info = brain_regions[1:500,], Adjacency = A, pathway = pathway, Column_DirectEvidence = 7, use_sig_pathways = FALSE, n_perm = 10, threshold = 0.05) ################################################### ### code chunk number 11: Contextual results ################################################### # Show the first four rows and first two columns # of the contextual association from the # demonstration results$conn_p_value[1:4, 1:2] # Show the first four rows and first two columns # of the pathway overlap scores from the # demonstration results$pathway_overlap[1:4, 1:2]