lioness: Compute LIONESS (Linear Interpolation to Obtain Network...

In QuackenbushLab/pandaR: PANDA Algorithm

Description

Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)

Usage

lioness(motif, expr, ppi = NULL, network.inference.method, ...)

Arguments

motif	A motif dataset, a data.frame, matrix or exprSet containing 3 columns. Each row describes an motif associated with a transcription factor (column 1) a gene (column 2) and a score (column 3) for the motif.
expr	A mandatory expression dataset, as a genes (rows) by samples (columns) data.frame
ppi	A Protein-Protein interaction dataset, a data.frame containing 3 columns. Each row describes a protein-protein interaction between transcription factor 1(column 1), transcription factor 2 (column 2) and a score (column 3) for the interaction.
network.inference.method	String specifying choice of network inference method. Default is "panda". Options include "pearson".
...	additional arguments for panda analysis

Value

A list of length N, containing objects of class "panda" corresponding to each of the N samples in the expression data set.
"regNet" is the regulatory network
"coregNet" is the coregulatory network
"coopNet" is the cooperative network

References

Kuijjer, M.L., Tung, M., Yuan, G., Quackenbush, J. and Glass, K., 2015. Estimating sample-specific regulatory networks. arXiv preprint arXiv:1505.06440.

Examples

data(pandaToyData)
linonessRes <- lioness(pandaToyData$motif,
    pandaToyData$expression[,1:20],pandaToyData$ppi,hamming=.1,progress=FALSE)

QuackenbushLab/pandaR documentation built on May 8, 2019, 3:49 a.m.