R/run.phenograph.R

In ImmuneDynamics/Spectre: High-dimensional cytometry and imaging analysis.

#' run.phenograph - Run phenograph clustering
#'
#' This function allows you to perform phenograph clustering on a data.table
#' 
#' @usage run.phenograph()
#'
#' @param ref.dat NO DEFAULT. A data.table consisting of the 'refernece' data you will use to train the alignment algorithm

#' @return Returns a data.table with clustering results added
#'
#' @author Thomas M Ashhurst, \email{thomas.ashhurst@@sydney.edu.au}
#'
#' @references Ashhurst, T. M., et al. (2019). \url{https://www.ncbi.nlm.nih.gov/pubmed/31077106}
#'
#' @examples
#' 
#' cell.dat <- Spectre::demo.asinh
#' cell.dat <- Spectre::do.subsample(cell.dat, 5000)
#' 
#' use.cols <- names(cell.dat)[c(11:19)]
#' 
#' cell.dat <- run.phenograph(dat = cell.dat, use.cols = use.cols)
#'
#' @import data.table
#'
#' @export

run.phenograph <- function(dat,
                           use.cols,
                           k = 45,
                           clust.name = 'Phenograph_cluster'){
  
  ### Packages
  
      # devtools::install_github("JinmiaoChenLab/Rphenograph")
  
      if(!is.element('Rphenograph', installed.packages()[,1])) stop("Rphenograph is required but not installed. Can be installed by running devtools::install_github('JinmiaoChenLab/Rphenograph)")
      if(!is.element('data.table', installed.packages()[,1])) stop('data.table is required but not installed')
      if(!is.element('Rcpp', installed.packages()[,1])) stop('Rcpp is required but not installed')
  
      require(Rphenograph)
      require(data.table)
      require(Rcpp)
  
  ### Testing data
  
      # dat <- Spectre::demo.asinh
      # dat <- Spectre::do.subsample(dat, 5000)
      # use.cols <- names(dat)[c(11:19)]
      # k <- 45
      # clust.name <- 'Phenograph_cluster'

  ### Data setup
  
      dat.start <- dat
      dat <- dat[, use.cols, with = FALSE]
      dat <- as.matrix(dat)
      
  ### Clustering

      res <- Rphenograph(dat, k = k)
      res <- factor(membership(res[[2]]))
      
  ### Attach result
      
      res <- as.data.table(res)
      names(res) <- clust.name
      dat.start <- cbind(dat.start, res)
      
  ### Return
      
      message("Phenograph clustering complete")
      return(dat.start)
}