Defines functions gs_get_singlecell_expression_by_gate getSingleCellExpressionByGate gs_get_singlecell_expression getSingleCellExpression .getPopChnlMapping .getIndiceMat gh_pop_get_indices_mat

Documented in getSingleCellExpression getSingleCellExpressionByGate gh_pop_get_indices_mat gs_get_singlecell_expression gs_get_singlecell_expression_by_gate

#' Return the single-cell matrix of 1/0 dichotomized expression
#' @param gh \code{GatingHierarchy} object
#' @param y \code{character} vector containing the node names
#' @export
gh_pop_get_indices_mat <- function(gh,y){
  nodes <- as.character(y)
  .getIndiceMat(gh, sampleNames(gh), nodes)

.getIndiceMat <- function(gs, thisSample, nodes){
  #extract logical indices for each cytokine gate
  indice_list <- sapply(nodes,function(this_node).cpp_getIndices(gs@pointer, thisSample, this_node)
      ,simplify = FALSE)
  #construct the indice matrix
  do.call(cbind, indice_list)
#  as.data.table(indice_list)
#' create mapping between pops and channels
#' The goal is translate the markers provided by user map or directly parsed from popNames (when map is NULL)
#' to the accurate channel info for indexing the flow data
#' because user might give the short form of either  'name' or 'desc' of flowFrame based on swap argument.
#' @param this_pd \code{data.frame} extraced from flowFrame object to provide the channel and marker info
#' @param popNames \code{character} node names in gating tree
#' @param map \code{list} contains the node-to-marker mapping explicitly specified user
#' @importFrom dplyr mutate %>%
#' @noRd 
.getPopChnlMapping <- function(this_pd, popNames, map =  NULL, swap = FALSE, ignore.case = FALSE){
  datSrc <- ifelse(swap, "name", "desc")
  this_pd[, "name"] <- as.vector(this_pd[, "name"])
  this_pd[, "desc"] <- as.vector(this_pd[, "desc"])
  #match to the pdata of flow frame
  all_markers <- this_pd[,datSrc]
  all_markers[is.na(all_markers)] <- "NA"
  lapply(popNames, function(popName){
        #fetch the marker from map first
        toMatch <- map[[popName]]
        #if not supplied, parse it from popName
          this_pops <- strsplit(split="/", popName, fixed=TRUE)[[1]]
          #get the terminal node
          toMatch <- this_pops[length(this_pops)]
        #partial match first
          matchedInd <- grep(tolower(toMatch), tolower(all_markers), fixed = TRUE)
          matchedInd <- grep(toMatch, all_markers, fixed = TRUE)
        if(length(matchedInd) > 1)
          #Switch to exact match because multiple markers matched
            matchedInd <- match(tolower(toMatch), tolower(all_markers))
            matchedInd <- match(toMatch, all_markers)
          matchedInd <- matchedInd[!is.na(matchedInd)]
          if(length(matchedInd) == 0){
            #no exact match
            stop(toMatch, "  paritally matched to multiple markers but failed to exactly matched to any of them. ")
          }else if(length(matchedInd) > 1){
            #multiple exact matches
            stop(toMatch, "exactly matches to multiple markers!")
        }else if(length(matchedInd) == 0){
          #no partial match
          stop("Marker not found: ", toMatch)
        this_pd[matchedInd,c("name", "desc")]
      })%>% bind_rows %>% mutate(pop = popNames)
#' @templateVar old getSingleCellExpression
#' @templateVar new gs_get_singlecell_expression
#' @template template-depr_pkg

#' @export
getSingleCellExpression <- function(...){

#' Return the cell events data that express in any of the single populations defined in \code{y}
#' Returns a list of matrix containing the events that expressed in any one of the populations defined in \code{y}
#' @name gs_get_singlecell_expression
#' @aliases getSingleCellExpression getSingleCellExpressionByGate
#' @param x A \code{GatingSet} or \code{GatingSetList} object .
#' @param nodes \code{character} vector specifying different cell populations
#' @param other.markers \code{character} vector specifying the extra markers/channels to be returned besides the ones derived from "nodes" and "map" argument.It is only valid when threshold is set to FALSE.
#' @param swap \code{logical} indicates whether channels and markers of flow data are swapped.
#' @param threshold \code{logical} indicates whether to threshold the flow data by setting intensity value to zero when it is below the gate threshold.
#' @param marginal \code{logical} indicates whether to the gate is treaded as 1d marginal gate. Default is TRUE, which means markers are determined either
#'                                by node name or by 'map' argument explained below. When FALSE, the markers are determined by the gate dimensions.
#'                                and node name and 'map' argument are ignored.
#' @param ... other arguments
#' 		map a named list providing the mapping between node names (as specified in the gating hierarchy of the gating set) and channel
#'                         names (as specified in either the \code{desc} or \code{name}
#'                          columns of the parameters of the associated \code{flowFrame}s
#'                          in the \code{GatingSet}). see examples.
#'        ignore.case whether to ignore case when match the marker names. Default is FALSE.
#' @param mc.cores passed to \code{mclapply}. Default is 1, which means the process runs in serial mode. When it is larger than 1, parallel mode is enabled.
#' @param inverse.transform logical flag indicating whether to inverse transform the data
#' @return A \code{list} of \code{numerci matrices}
#' @aliases gs_get_singlecell_expression
#' @author Mike Jiang \email{wjiang2@@fhcrc.org}
#' @seealso \code{\link{gh_pop_get_indices}}  \code{\link{gs_pop_get_count_fast}}
#' @examples \dontrun{
#'   #G is a GatingSet
#' 	nodes <- c("4+/TNFa+", "4+/IL2+")
#' 	res <- gs_get_singlecell_expression(gs, nodes)
#' 	res[[1]]
#'  # if it fails to match the given nodes to the markers, then try to provide the mapping between node and marker explicitly
#' 	res <- gs_get_singlecell_expression(gs, nodes , map = list("4+/TNFa+" = "TNFa", "4+/IL2+" = "IL2"))
#' 	# It can also operate on the 2d gates by setting marginal to FALSE
#' 	# The markers are no longer deduced from node names or supplied by map
#' 	# Instead, it retrieves the markers that are associated with the gates
#' 	nodes <- c("4+/TNFa+IFNg+", "4+/IL2+IL3+")
#' 	res <- gs_get_singlecell_expression(gs, nodes, marginal = FALSE)
#' 	#or simply call convenient wrapper
#' 	gs_get_singlecell_expression_by_gate(gs, nodes)
#' }
#' @importFrom dplyr bind_rows
#' @export
gs_get_singlecell_expression <- function(x, nodes
											, other.markers = NULL
											, swap = FALSE
											, threshold = TRUE
											, marginal = TRUE
											, mc.cores = getOption("mc.cores", 1L)
											, inverse.transform = FALSE
											, ...){

	if(is(x, "GatingSetList"))
		res <- lapply(x, function(gs)gs_get_singlecell_expression(gs, nodes, other.markers, swap, threshold, marginal, mc.cores, inverse.transform, ...), level = 1)
		unlist(res, recursive = FALSE)	
		  datSrc <- ifelse(swap, "name", "desc")
		  fs <- gs_pop_get_data(x)#, inverse.transform = inverse.transform) #avoid cp the h5 data ,do inverse in-mem instead
		  sn <- sampleNames(x)
		  names(sn) <- sn
		  thisCall <- quote(lapply(sn,function(sample){
		            message(".", appendLF = FALSE)
		            fr <- fs[[sample, use.exprs = FALSE]]
		            this_pd <- pData(parameters(fr))
		            if(marginal){#parse marker info from node name or map argument
		              #get pop vs channel mapping
		              pop_chnl <- .getPopChnlMapping(this_pd, nodes, swap = swap, ...)  
		              chnls <- as.character(pop_chnl[["name"]])
		              pops <-  as.character(pop_chnl[["pop"]])
		              markers <- as.character(pop_chnl[[datSrc]])
		            }else{#parse markers from gates
		              chnls <- lapply(nodes, function(node){
		                    gate <- gh_pop_get_gate(x[[sample]], node)
		                    if(is(gate, "booleanFilter"))
		                      stop("can't operate on boolean gates: ", node)
		                    dim <- parameters(gate)
		                    names(dim) <- rep(node, length(dim))
		              chnls <- unlist(chnls)
		              #pops will be repeated for 2d gate to keep its length consistent with the number of chnls
		              #so that the original c++ code still works without modification
		              pops <- names(chnls)
		              marker_chnl <- lapply(chnls, function(c){
		                                    res <- getChannelMarker(fr, c)
		                                    if(is.na(res[["desc"]]))#replace marker with channel name when marker is not present
		                                      res[["desc"]] <- res[["name"]]
		                })%>% bind_rows
		              markers <- marker_chnl[[datSrc]]
		              markers.unique <- unique(markers)
		              markers_pops <- sapply(markers.unique, function(marker){
		                                                    pops[markers == marker]
		                                            }, simplify = FALSE)
		              markers <- markers.unique
		              chnls <- unique(chnls)
		            #append the extra markers
		              other_marker_chnl <- lapply(other.markers, getChannelMarker, frm = fr)%>% bind_rows
		              #remove the other_chnls that are already present in chnls
		              toKeep <- ! other_marker_chnl[["name"]] %in% chnls
		              other_marker_chnl <- other_marker_chnl[toKeep, ,drop = FALSE]
		              chnls <- c(chnls, other_marker_chnl[["name"]])
		              markers <- c(markers, other_marker_chnl[[datSrc]])
					data <- fs[[sample, unique(chnls), returnType = "flowFrame"]]
					  trans <- gh_get_transformations(x[[sample]], inverse = TRUE)
					    stop("No inverse transformation is found from the GatingSet!")
					  trans <- trans[names(trans) %in% colnames(data)]
					  trans <- transformList(names(trans), trans)
					  data <- transform(data, trans)
					data <- exprs(data)
		              data <- .cpp_getSingleCellExpression(x@pointer, sample, pops, data, markers, threshold)
		            else#modify data in place
		              data <- .cpp_getSingleCellExpressionByGate(x@pointer, sample, markers_pops, data, markers, threshold)
		  if(mc.cores > 1){
		    thisCall[[1]] <- quote(mclapply)
		    thisCall[["mc.cores"]] <- mc.cores

#' @export
getSingleCellExpressionByGate <- function(...){
  gs_get_singlecell_expression(..., marginal = FALSE)  

#' @rdname gs_get_singlecell_expression
#' @export
gs_get_singlecell_expression_by_gate <- function(...){
	gs_get_singlecell_expression(..., marginal = FALSE)  
RGLab/flowWorkspace documentation built on July 18, 2021, 9:05 p.m.