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

Source:`R/LIONESS.R`

`lioness.Rd`

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)
```