DoLandSCENT: Runs the LandSCENT algorithm
In ChenWeiyan/LandSCENT: Landscape Single Cell Entropy

Description Usage Arguments Value Examples

Main user function implement LandSCENT. This function is the typical workflow of the whole package for you to easily use.

DoLandSCENT(
  exp.m,
  ppiA.m,
  log_trans = FALSE,
  mc.cores = 1,
  pheno.v = NULL,
  coordinates = NULL,
  PLOT = TRUE,
  PDF = TRUE
)

`exp.m`	Can be three major kinds of input: One is a scRNA-Seq data matrix with rows labeling genes and columns labeling single cells. And it can be either a log-transformed data matrix with minimal value around 0.1 (recommended), or an nonlog-transformed data matrix with minimal value 0. The other two kinds of input can be either a "SingleCellExperiment" class object or a "CellDataSet" class object
`ppiA.m`	The adjacency matrix of a user-given PPI network with rownames and colnames labeling genes (same gene identifier as in `exp.m`)
`log_trans`	A logical. Whether to do log-transformation on the input data matrix or not. Default is FALSE
`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 (default), and parallelization requires at least two cores.
`pheno.v`	A phenotype vector for the single cells, of same length and order as the columns of `exp.m`. Function can also automatically extract phenotype information from your original sce/cds data, please store the phenotype information as name of `phenoInfo`.
`coordinates`	Optional. The previous reduced dimension coordinates, with rows lalabeling cells and two colums labeling reduced dimensions
`PLOT`	A logical. Decides whether to generate (default) the landSR figure or not.
`PDF`	A logical. Output figure via pdf file or not, default is TRUE

Integration.l A list contains input information and SR values, potency states and more other results.

A PDF file If PDF is TRUE(default), then it will automatically generate a pdf file ploting cell density against potency states.

## Not run: 
### 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="")

DoLandSCENT.o <- DoLandSCENT(exp.m, ppiA.m, PLOT = FALSE, PDF = FALSE)

## End(Not run)