Metabolomics Personalized Pathway Analysis Tool

Computes the pathway deregulation score for a given set of metabolites, selects the pathways with the highest mutual information and then uses them to build a classifier. F. Alakwaa, S. Huang, and L. Garmire (2018) .


Lilikoi computes the pathway deregulation score for a given set of metabolites, selects the pathways with the highest mutual information and then uses them to build a classifier.

"Lilikoi: an R package for personalized pathway-based classification modeling using metabolomics data. F. Alakwaa, S. Huang, and L. Garmire (2018) \doi{10.1101/283408}."

Installation

install.packages("lilikoi")

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

Example

# library(lilikoi)

filename <- system.file("extdata", "plasma_breast_cancer.csv", package = "lilikoi")
metaboliteMeasurements <- read.csv(file = filename, check.names = FALSE, row.names = 1)
metaboliteNames <- colnames(metaboliteMeasurements)[-1]
clinicalFactorsData <- read.csv(file = system.file("extdata", "plasma_breast_cancer_Meta.csv",
  package = "lilikoi"))

# The below lines shrink the dataset for faster test runs. Remove them to operate on
# full dataset
metaboliteMeasurements <- metaboliteMeasurements[, 1:20]
metaboliteNames <- colnames(metaboliteMeasurements)[-1]

metabolitePathwayTable <- lilikoi.metab_to_pathway(metaboliteNames, "name")

# We use a subset of the database to speed up tests.
# Swap the comments on the below two lines to run on the full database.
# PDSmatrix <- lilikoi.get_pd_scores(metaboliteMeasurements, metabolitePathwayTable)
PDSmatrix <- lilikoi.get_pd_scores(metaboliteMeasurements, metabolitePathwayTable,
  lilikoi::data.smpdb[1:25,])


significantPathways <- lilikoi.select_pathways(PDSmatrix, metaboliteMeasurements,
  threshold = 0.42, method = "gain")

mlResults <- lilikoi.machine_learning(PDSmatrix, metaboliteMeasurements$Label,
  significantPathways)

finalModel <- lilikoi.adjust_model(mlResults$mlResults, PDSmatrix, significantPathways,
  metaboliteMeasurements, clinicalFactorsData, factors = c("Age", "Race"))

Updating the External Databases

Lilikoi depends on data from HMDB, SMPDB, and MetaboAnalyst. This library ships with the latest data as of the date of publication. To update to the latest data from these sources, load and run the lilikoi.update_database() method found in the lilikoi.update_database.r file.

Warning: the datasets are large (>5GB) and this step may take greater than 20 minutes.

Built By

More Examples

News

lilikoi 0.1.0.9000

  • first release

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("lilikoi")

0.1.0 by Fadhl Alakwaa, a year ago


https://github.com/lanagarmire/lilikoi


Report a bug at https://github.com/lanagarmire/lilikoi/issues


Browse source code at https://github.com/cran/lilikoi


Authors: Fadhl Alakwaa [aut, cre] , Sijia Huang [aut]


Documentation:   PDF Manual  


GPL-2 license


Imports caret, corrplot, devtools, dplyr, e1071, gbm, ggplot2, glmnet, hash, Hmisc, infotheo, Matrix, pamr, R.oo, princurve, pROC, randomForest, reshape2, RWeka, stringr

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See at CRAN