LandSCENT: Landscape Single Cell Entropy

#' Landscape Single Cell Entropy
#'
#' \pkg{LandSCENT} (Landscape Single Cell Entropy) is a R-package for the 
#' analysis of single-cell RNA-Seq data. One important feature of this 
#' package is the computation of signaling entropy, which allows single 
#' cells to be ordered according to differentiation potency. \pkg{LandSCENT} 
#' also integrates cell density with potency distribution to dissect cell types
#' across all potency states and generates high-quality figures to show this.
#' 
#' \packageIndices{LandSCENT}
#' \pkg{LandSCENT} will be of interest to those analysing 
#' single-cell RNA-Sequencing data. A core component of \pkg{LandSCENT} 
#' is the computation of signaling entropy at the single-cell level
#' (\code{CompSRana}), allowing cells to be ordered according to
#' differentiation potency or phenotypic plasticity. It also incorporates
#' functionality for quantifying intercellular heterogeneity, for identifying
#' interesting subpopulations of cells that differ in terms of potency or
#' plasticity, as well as to infer dependencies between single cell
#' clusters, which for instance can help identify lineage trajectories.
#'
#' @name LandSCENT-package
#' 
#' @docType package
#' 
#' @author Weiyan Chen & Andrew E Teschendorff
#' 
#' @references 
#' Teschendorff AE, Tariq Enver. 
#' \emph{Single-cell entropy for accurate estimation of differentiation 
#' potency from a cell’s transcriptome.}
#' Nature communications 8 (2017): 15599.
#' doi:\href{https://doi.org/10.1038/ncomms15599}{
#' 10.1038/ncomms15599}.
#' 
#' Teschendorff AE, Banerji CR, Severini S, Kuehn R, Sollich P. 
#' \emph{Increased signaling entropy in cancer requires the scale-free 
#' property of protein interaction networks.}
#' Scientific reports 5 (2015): 9646.
#' doi:\href{https://doi.org/10.1038/srep09646}{
#' 10.1038/srep09646}.
#' 
#' Banerji, Christopher RS, et al. 
#' \emph{Intra-tumour signalling entropy determines clinical outcome 
#' in breast and lung cancer.}
#' PLoS computational biology 11.3 (2015): e1004115.
#' doi:\href{https://doi.org/10.1371/journal.pcbi.1004115}{
#' 10.1371/journal.pcbi.1004115}.
#' 
#' Teschendorff, Andrew E., Peter Sollich, and Reimer Kuehn.
#' \emph{Signalling entropy: A novel network-theoretical framework 
#' for systems analysis and interpretation of functional omic data.}
#' Methods 67.3 (2014): 282-293.
#' doi:\href{https://doi.org/10.1016/j.ymeth.2014.03.013}{
#' 10.1016/j.ymeth.2014.03.013}.
#' 
#' Banerji, Christopher RS, et al. 
#' \emph{Cellular network entropy as the energy potential in 
#' Waddington's differentiation landscape.}
#' Scientific reports 3 (2013): 3039.
#' doi:\href{https://doi.org/10.1038/srep03039}{
#' 10.1038/srep03039}.
#' 
#' @examples 
#' ### see example for CompSRana function for typical workflow
#' 
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