Description Usage Arguments Value References Examples
Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)
1 |
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 |
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
Kuijjer, M.L., Tung, M., Yuan, G., Quackenbush, J. and Glass, K., 2015. Estimating sample-specific regulatory networks. arXiv preprint arXiv:1505.06440.
1 2 3 | data(pandaToyData)
linonessRes <- lioness(pandaToyData$motif,
pandaToyData$expression[,1:20],pandaToyData$ppi,hamming=.1,progress=FALSE)
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