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LoadGeneSets
Automatic loader for gene sets from the IPA and Great tools.
LoadGeneSets(file_location, groupnames = c("MClust_1", "MClust_26", "MClust_3457"), P.cutoff = 1.3, Mol.cutoff = 5, Source = "IPA", Great.Background = F, type = "Canonical_Pathways", topranks = "", structure = "SYMBOL", Organism = "org.Hs.eg.db", seperator = ",")
file_location A location string in a vector.
groupnames A vector with group names of the different gene set experiments
P.cutoff numeric Pvalue cutoff for filtering.
Mol.cutoff numeric value for minimum number of molecules.
Source Tool used to generate gene sets.
Great.Background If the Great tool was used, did the user supply a background.
type For IPA data if Canonical pathways or Functional Anotations were supplied.
topranks numeric with the number of ranks per group to be loaded, usefull when there is a lot of data.
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
a pathwayobject
Great.files <- c(system.file("extdata", "MM10.GREAT.KO.uGvsMac.bed.tsv",
package = "GeneSetCluster"),
system.file("extdata", "MM10.GREAT.KO.uGvsMac.bed_BCKGRND.tsv", package = "GeneSetCluster"),
system.file("extdata", "MM10.GREAT.WT.uGvsMac.bed.tsv", package = "GeneSetCluster"),
system.file("extdata", "MM10.GREAT.WT.uGvsMac.bed_BCKGRND.tsv", package = "GeneSetCluster"))
Great.files.bckgrnd <- Great.files[grepl("BCKGRND", Great.files)]
Great.bckgnrd.Object1 <- LoadGeneSets(file_location = Great.files.bckgrnd,
groupnames= c("KO", "WT"),
P.cutoff = 0.05,
Mol.cutoff = 5,
Source = "Great",
Great.Background = TRUE,
type = "Canonical_Pathways",
topranks = 20,
structure = "SYMBOL",
Organism = "org.Mm.eg.db",
seperator = ",")
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