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

Usage

lioness(
  expr,
  motif = NULL,
  ppi = NULL,
  network.inference.method = "panda",
  ncores = 1,
  ...
)

Arguments

expr: A mandatory expression dataset, as a genes (rows) by samples (columns) data.frame
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.
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".
ncores: int specifying the number of cores to be used. Default is 1. (Note: constructing panda networks can be memory-intensive, and the number of cores should take into consideration available memory.)
...: 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. Kuijjer, M.L., Hsieh, PH., Quackenbush, J. et al. lionessR: single sample network inference in R. BMC Cancer 19, 1003 (2019). https://doi.org/10.1186/s12885-019-6235-7

Examples

data(pandaToyData)
lionessRes <- lioness(expr = pandaToyData$expression[,1:3], motif = pandaToyData$motif, ppi = pandaToyData$ppi,hamming=1,progress=FALSE)