CompSRana: Computes the signaling entropy of given gene expression...
In ChenWeiyan/LandSCENT: Landscape Single Cell Entropy

Description Usage Arguments Value References Examples

This is the main user function for computing signaling entropy of single cells. It takes as input the gene expression profile of single cells and the adjacency matrix of a connected network. These inputs will be typically the output of the DoIntegPPI function.

1	CompSRana(Integration.l, local = FALSE, mc.cores = 1)

`Integration.l`	A list object from `DoIntegPPI` function.
`local`	A logical (default is FALSE). If TRUE, function computes the normalized local signaling entropies of each gene in the network.
`mc.cores`	The number of cores to use, i.e. at most how many child processes will be run simultaneously. The option is initialized from environment variable MC_CORES if set. Must be at least one, and parallelization requires at least two cores.

A list incorporates the input list and four new elements:

SR The global signaling entropy rate. It is normalized by the maximum rate, hence a value between 0 and 1

inv The stationary distribution of every sample

s The unnormlised local entropies of each gene in every cell

ns The normalised local entropies of each gene, so that each value is between 0 and 1

Teschendorff AE, Tariq Enver. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nature communications 8 (2017): 15599. doi: 10.1038/ncomms15599.

Teschendorff AE, Banerji CR, Severini S, Kuehn R, Sollich P. Increased signaling entropy in cancer requires the scale-free property of protein interaction networks. Scientific reports 5 (2015): 9646. doi: 10.1038/srep09646.

Banerji, Christopher RS, et al. Intra-tumour signalling entropy determines clinical outcome in breast and lung cancer. PLoS computational biology 11.3 (2015): e1004115. doi: 10.1371/journal.pcbi.1004115.

Teschendorff, Andrew E., Peter Sollich, and Reimer Kuehn. Signalling entropy: A novel network-theoretical framework for systems analysis and interpretation of functional omic data. Methods 67.3 (2014): 282-293. doi: 10.1016/j.ymeth.2014.03.013.

Banerji, Christopher RS, et al. Cellular network entropy as the energy potential in Waddington's differentiation landscape. Scientific reports 3 (2013): 3039. doi: 10.1038/srep03039.

### define a small network
ppiA.m <- matrix(0,nrow=10,ncol=10);
ppiA.m[1,] <- c(0,1,1,1,1);
for(r in 2:nrow(ppiA.m)){
  ppiA.m[r,1] <- 1;
}
rownames(ppiA.m) <- paste("G",1:10,sep="");
colnames(ppiA.m) <- paste("G",1:10,sep="");

### define a positively valued expression matrix (20 genes x 10 samples)
exp.m <- matrix(rpois(20*10,8),nrow=20,ncol=10);
colnames(exp.m) <- paste("S",1:10,sep="");
rownames(exp.m) <- paste("G",1:20,sep="");

### integrate data and network
Integration.l <- DoIntegPPI(exp.m, ppiA.m);

### compute SR values
Integration.l <- CompSRana(Integration.l);

### output global signaling entropy
print(Integration.l$SR);