lilikoi (version 2.1.1)

lilikoi.KEGGplot: lilikoi.KEGGplot

Description

Visualizes selected pathways based on their metabolites expression data.

Usage

lilikoi.KEGGplot(
  metamat,
  sampleinfo,
  grouporder,
  pathid = "00250",
  specie = "hsa",
  filesuffix = "GSE16873",
  Metabolite_pathway_table = Metabolite_pathway_table
)

Value

Pathview visualization output

Arguments

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

Examples

# \donttest{
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)
# }