View source: R/lilikoi.KEGGplot.R
lilikoi.KEGGplot | R Documentation |
Visualizes selected pathways based on their metabolites expression data.
lilikoi.KEGGplot( metamat, sampleinfo, grouporder, pathid = "00250", specie = "hsa", filesuffix = "GSE16873", Metabolite_pathway_table = Metabolite_pathway_table )
metamat |
metabolite expression data matrix |
sampleinfo |
is a vector of sample group, with element names as sample IDs. |
grouporder |
grouporder is a vector with 2 elements, the first element is the reference group name, like 'Normal', the second one is the experimental group name like 'Cancer'. |
pathid |
character variable, Pathway ID, usually 5 digits. |
specie |
character, scientific name of the targeted species. |
filesuffix |
output file suffix |
Metabolite_pathway_table |
Metabolites mapping table |
Pathview visualization output
dt = lilikoi.Loaddata(file=system.file("extdata","plasma_breast_cancer.csv", package = "lilikoi")) Metadata <- dt$Metadata dataSet <- dt$dataSet # convertResults=lilikoi.MetaTOpathway('name') # Metabolite_pathway_table = convertResults$table # data_dir=system.file("extdata", "plasma_breast_cancer.csv", package = "lilikoi") # plasma_data <- read.csv(data_dir, check.names=FALSE, row.names=1, stringsAsFactors = FALSE) # sampleinfo <- plasma_data$Label # names(sampleinfo) <- row.names(plasma_data) # metamat <- t(t(plasma_data[-1])) # metamat <- log2(metamat) # grouporder <- c('Normal', 'Cancer') # make sure install pathview package first before running the following code. # library(pathview) # data("bods", package = "pathview") # options(bitmapType='cairo') #lilikoi.KEGGplot(metamat = metamat, sampleinfo = sampleinfo, grouporder = grouporder, #pathid = '00250', specie = 'hsa',filesuffix = 'GSE16873', #Metabolite_pathway_table = Metabolite_pathway_table)
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