The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Lilikoi is a novel tool for personalized pathway analysis of metabolomics data.

Previously we developed Lilikoi, a personalized pathway-based method to classify diseases using metabolomics data. Given the new trends of computation in the metabolomics field, here we report the next version of Lilikoi as a significant upgrade. The new Lilikoi v2 R package has implemented a deep-learning method for classification, in addition to popular machine learning methods. It also has several new modules, including the most significant addition of prognosis prediction, implemented by Cox-PH model and the deep-learning based Cox-nnet model. Additionally, Lilikoi v2 supports data preprocessing, exploratory analysis, pathway visualization and metabolite-pathway regression. In summary, Lilikoi v2 is a modern, comprehensive package to enable metabolomics analysis in R programming environment.

Installation

install.packages("lilikoi")

# Or for the latest dev version:
devtools::install_github("lanagarmire/lilikoi2")

Example

# library(lilikoi)

dt <- lilikoi.Loaddata(file=system.file("extdata", "plasma_breast_cancer.csv", package = "lilikoi"))
Metadata <- dt$Metadata
dataSet <- dt$dataSet

# Transform the metabolite names to the HMDB ids using Lilikoi MetaTOpathway function
convertResults=lilikoi.MetaTOpathway('name')
Metabolite_pathway_table = convertResults$table
head(Metabolite_pathway_table)

# Transform metabolites into pathway using pathtracer algorithm
PDSmatrix=lilikoi.PDSfun(Metabolite_pathway_table)

# Select the most signficant pathway related to phenotype.
selected_Pathways_Weka= lilikoi.featuresSelection(PDSmatrix,threshold= 0.50,method="gain")

# Machine learning
lilikoi.machine_learning(MLmatrix = Metadata, measurementLabels = Metadata$Label,
                              significantPathways = 0,
                              trainportion = 0.8, cvnum = 10, dlround=50,nrun=10, Rpart=TRUE,
                              LDA=TRUE,SVM=TRUE,RF=TRUE,GBM=TRUE,PAM=FALSE,LOG=TRUE,DL=TRUE)
                              
# Prognosis model
lilikoi.prognosis(event, time, exprdata, percent=percent, alpha=0, nfold=5, method="quantile",
          cvlambda=cvlambda,python.path=NULL,coxnnet=FALSE,coxnnet_method="gradient")
          
# Metabolites-pathway regression
lilikoi.meta_path(PDSmatrix = PDSmatrix, selected_Pathways_Weka = selected_Pathways_Weka, Metabolite_pathway_table = Metabolite_pathway_table, pathway = "Pyruvate Metabolism")

# KEGG plot
lilikoi.KEGGplot(metamat = metamat, sampleinfo = sampleinfo, grouporder = grouporder,
                 pathid = '00250', specie = 'hsa',
                 filesuffix = 'GSE16873', 
                 Metabolite_pathway_table = Metabolite_pathway_table)

Built By

More Examples

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.