## ----------------------------------------------------------------------------- library(coexnet) ## ---- eval=FALSE-------------------------------------------------------------- # # # Downloading the microarray raw data from GSE8216 study # # The accession number of the microarray chip related with this study is GPL2025 # # getInfo(GSE = "GSE8216", GPL = "GPL2025",directory = ".") # # # Shows the actual path file with the folder, its GSE number and the .soft file # # dir() # ## ----eval=FALSE--------------------------------------------------------------- # # # Reading some GSM samples from GSE4773 study, the folder with the # # GSM files are called GSE1234. # # affy <- getAffy(GSE = "GSE1234",directory = system.file("extdata",package = "coexnet")) # affy # ## ----eval=FALSE--------------------------------------------------------------- # # # The variable affy doesn't have the CDF (Chip Definition File) information. # # You can include this information modifying the AffyBatch object afterwards. # # affy@cdfName <- "HG-U133_Plus_2" # ## ----------------------------------------------------------------------------- # Create the table with the match between probesets and IDs. gene_table <- geneSymbol(GPL = "GPL2025",directory = system.file("extdata",package = "coexnet")) head(gene_table) ## ----------------------------------------------------------------------------- # The created table have NA and empty IDs information. # We can delete this unuseful information. # Deletion of IDs with NA information gene_na <- na.omit(gene_table) # Deletion of empty IDs final_table <- gene_na[gene_na$ID != "",] head(final_table) ## ---- eval=FALSE-------------------------------------------------------------- # # # Loading gata # # if (require(affydata)) { # data(Dilution) # } # # # Loading table with probeset and gene/ID information # # data("info") # # # Calculating the expression matrix with rma # # rma <- exprMat(affy = Dilution,genes = info,NormalizeMethod = "rma", # SummaryMethod = "median",BatchCorrect = FALSE) # head(rma) ## ----------------------------------------------------------------------------- # Simulated expression data n <- 200 m <- 20 # The vector with treatment samples and control samples t <- c(rep(0,10),rep(1,10)) # Calculating the expression values normalized mat <- as.matrix(rexp(n, rate = 1)) norm <- t(apply(mat, 1, function(nm) rnorm(m, mean=nm, sd=1))) # Calculating the coefficient of variation to case samples case <- cofVar(expData = norm,complete = FALSE,treatment = t,type = "case") head(case) # Creating the boxplot to coefficient of variation results boxplot(case$cv) # Extracting the number of atipic data length(boxplot.stats(case$cv)$out) ## ----------------------------------------------------------------------------- # Calculating the coefficient of variation to whole matrix complete <- cofVar(norm) head(complete) # Creating the boxplot to coefficient of variation results boxplot(complete$cv) # Extracting the number of atipic data length(boxplot.stats(complete$cv)$out) ## ---- eval=FALSE-------------------------------------------------------------- # # # Creating a matrix with 200 genes and 20 samples # # n <- 200 # m <- 20 # # # The vector with treatment samples and control samples # # t <- c(rep(0,10),rep(1,10)) # # # Calculating the expression values normalized # # mat <- as.matrix(rexp(n, rate = 1)) # norm <- t(apply(mat, 1, function(nm) rnorm(m, mean=nm, sd=1))) # # # Running the function using the two approaches # # sam <- difExprs(expData = norm,treatment = t,fdr = 0.2,DifferentialMethod = "sam") # head(sam) ## ---- eval=FALSE-------------------------------------------------------------- # # # Loading data # # pathfile <- system.file("extdata","expression_example.txt",package = "coexnet") # data <- read.table(pathfile,stringsAsFactors = FALSE) # # # Finding threshold value # # cor_pearson <- findThreshold(expData = data,method = "correlation") # cor_pearson # ## ----------------------------------------------------------------------------- # Loading data pathfile <- system.file("extdata","expression_example.txt",package = "coexnet") data <- read.table(pathfile,stringsAsFactors = FALSE) # Building the network cor_pearson <- createNet(expData = data,threshold = 0.7,method = "correlation") plot(cor_pearson) ## ---- eval=FALSE-------------------------------------------------------------- # # # Creating a vector with identifiers # # ID <- c("FN1","HAMP","ILK","MIF","NME1","PROCR","RAC1","RBBP7", # "TMEM176A","TUBG1","UBC","VKORC1") # # # Creating the PPI network # # ppi <- ppiNet(molecularIDs = ID,evidence = c("neighborhood","coexpression","experiments")) # plot(ppi) ## ----------------------------------------------------------------------------- # Creating a PPI network from external data ppi <- ppiNet(file = system.file("extdata","ppi.txt",package = "coexnet")) plot(ppi) ## ----------------------------------------------------------------------------- # Loading data data("net1") data("net2") # Obtaining Common Connection Patterns ccp <- CCP(net1,net2) plot(ccp) ## ----------------------------------------------------------------------------- # Loading data data("net1") data("net2") # Obtain shared components share <- sharedComponents(net1,net2) share