## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(PhenoGeneRanker) ## ----eval=FALSE--------------------------------------------------------------- # BiocManager::install("PhenoGeneRanker") # library(PhenoGeneRanker) ## ----------------------------------------------------------------------------- inputDf <- read.table(system.file("extdata", "input_file.txt", package = "PhenoGeneRanker"), header=TRUE, sep="\t", stringsAsFactors = FALSE) print(inputDf) ## ----------------------------------------------------------------------------- geneLayerDf <- read.table(system.file("extdata", inputDf$file_name[1], package = "PhenoGeneRanker"), header=TRUE, sep="\t", stringsAsFactors = FALSE) print(head(geneLayerDf)) ## ----------------------------------------------------------------------------- ptypeLayerDf <- read.table(system.file("extdata", inputDf$file_name[3], package = "PhenoGeneRanker"), header=TRUE, sep="\t", stringsAsFactors = FALSE) print(head(ptypeLayerDf)) ## ----------------------------------------------------------------------------- biLayerDf <- read.table(system.file("extdata", inputDf$file_name[5], package = "PhenoGeneRanker"), header=TRUE, sep="\t", stringsAsFactors = FALSE) print(head(biLayerDf)) ## ----------------------------------------------------------------------------- # Generate walk matrix for RandomWalkWithRestart function use walkMatrix <- CreateWalkMatrix('input_file.txt') ## ----------------------------------------------------------------------------- # accesses the walk matrix itself wm <- walkMatrix[["WM"]] # sorted genes in the final network sortedGenes <- walkMatrix[["genes"]] # sorted phenotypes in the final network sortedPhenotypes <- walkMatrix[["phenotypes"]] # the degree of genes in the final network geneConnectivity <- walkMatrix[["gene_connectivity"]] # the degree of phenotypes in the final network phenotypeConnectivity <- walkMatrix[["phenotype_connectivity"]] # the number of gene layers numberOfGeneLayers <- walkMatrix[["LG"]] print(numberOfGeneLayers) # the number of phenotype layers numberOfPhenotypeLayers <- walkMatrix[["LP"]] print(numberOfPhenotypeLayers) # the number of genes in the network numberOfGenes <- walkMatrix[["N"]] print(numberOfGenes) # the number of phenotypes in the network numberOfPhenotypes <- walkMatrix[["M"]] print(numberOfPhenotypes) ## ----------------------------------------------------------------------------- # utilizes only gene seeds and generates p-values for ranks using 50 runs with random seeds ranks <- RandomWalkRestart(walkMatrix, c('g1', 'g5'), c("p1"), S=50) print(head(ranks)) # utilizes gene and phenotype seeds and does not generate p-values. ranks <- RandomWalkRestart(walkMatrix, c('g1'), c('p1', 'p2'), generatePValue=FALSE) print(head(ranks)) # utilizes only gene seeds, custom values for parameters r, eta, tau and phi for a complex network with two gene layers and two phenotype layers. ranks <- RandomWalkRestart(walkMatrix, c('g1'), c(), TRUE, r=0.8, eta=0.6, tau=c(0.5, 1.5), phi=c(1.5, 0.5)) print(head(ranks))