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ObjectCreator
Manually create a PathwayObject
ObjectCreator(Pathways, Molecules, Groups, Source, type, structure, Organism = NA, seperator)
Pathways A vector with Gene-Set labels.
Molecules A vector with strings of genes within each Gene-Set. Each string is seperated using the seperator supplied.
Groups A vector with group names of the different gene set experiments
Source Tool used to generate gene sets..
type For IPA data if Canonical pathways or Functional Anotations were supplied.
structure The structure of the genes. is it SYMBOLS, ENSEMBL, NCBI etc. Used for converting when there is mutiple structure in the object.
Organism the package name for the human or mouse data, used for converting the gene structure. name of the package, currently org.Hs.eg.db and org.Mm.eg.db supported.
seperator A character used within in the string to seperate genes
Value a pathwayobject
Test.object <- matrix(data = NA, nrow = 50, ncol = 3)
colnames(Test.object) <- c("Pathways", "Genes", "Group")
Test.object[,"Pathways"] <- paste("Pathway", 1:nrow(Test.object),
sep = "_")
Test.object[1:25,"Group"] <- "Group1"
Test.object[26:50,"Group"] <- "Group2"
#Create a random amount of genes per pathway
random.gene <- function()
{
genenames <- paste("Gene", 1:200, sep = "_")#this gives 200 gene names
genes <- round(runif(n = runif(n = 1,min = 7,max = 20),
min = 1, max = 200), digits = 0)
#This gives between 7 and 20 random whole numbers
genes <- unique(genes)#remove duplicate numbers
genes <- genenames[genes]#get random genesnames
return(genes)
}
for(i in 1:nrow(Test.object))
{
Test.object[i, "Genes"] <- paste(random.gene(), collapse=",")
}
Test.object1 <- ObjectCreator(Pathways = Test.object[,1],
Molecules = Test.object[,2],
Groups = Test.object[,3],
Source = "Random",#we randomly generated this data
type = "Test",
structure = "SYMBOL",
sep = ",")#neccesay to seperate the different genes for combinations.
Example Script: Example
Step 1A: Loading the data
Step 1B: Creating an Object
Step 2: Combine and Cluster
Step 2B: User supplied distance function
Step 2C: Highlighting-Genes
Step 3: Exporting Data
Step 4: Functional Investigation
Video: Step-by-step user guide