runLioness: Run python implementation of LIONESS

In twangxxx/netZoo: Integrate methods: PANDA, LIONESS, CONDOR, ALPACA into one workflow.

Description

LIONESS(Linear Interpolation to Obtain Network Estimates for Single Samples) is a method to estimate sample-specific regulatory networks. [(LIONESS arxiv paper)]).

Usage

runLioness(e = expression, m = motif, ppi = ppi,
  rm_missing = FALSE)

Arguments

e	Character String indicatining the file path of expression values file, as each gene (row) by samples (columns) required
m	Character String indicatining the file path of pair file of motif edges, when not provided, analysis continues with Pearson correlation matrix. optional
ppi	Character String indicatining the pair file path of Protein-Protein interaction dataset. optional
rm_missing	Boolean indicatining whether to remove missing values. If TRUE, removes missing values. if FALSE, keep missing values. THe default value is FALSE. optional

Value

A data frame with columns representing each sample, rows representing the regulator-target pair in PANDA network generated by runPanda. Each cell filled with the related score, representing the estimated contribution of a sample to the aggregate network.

Examples

# refer to the input datasets files of control in inst/extdat as example
control_expression_file_path <- system.file("extdata", "expr10.txt", package = "netZoo", mustWork = TRUE)
motif_file_path <- system.file("extdata", "chip.txt", package = "netZoo", mustWork = TRUE)
ppi_file_path <- system.file("extdata", "ppi.txt", package = "netZoo", mustWork = TRUE)

# Run PANDA algorithm
control_lioness_result <- runLioness(e = control_expression_file_path, m = motif_file_path, ppi = ppi_file_path, rm_missing = TRUE )

twangxxx/netZoo documentation built on May 17, 2019, 1:02 p.m.