R/plotPat.TcGSA.R

Defines functions plotPat.TcGSA

Documented in plotPat.TcGSA

#'Plot a Gene Set Trends Heatmap for each Patient.
#'
#'This function plots a series of gene sets dynamic trends heatmaps.  One
#'heatmap is drawn for each patient. NOT IMPLEMENTED YET (TODO)
#'
#'On the heatmap, each line corresponds to a gene set, and each column to a
#'time point.
#'
#'First a heatmap is computed on all the patients (see \code{\link{plot.TcGSA}}
#'and \code{\link{clustTrend}}) to define the clustering. Then, the clustering
#'and coloring thus defined on all the patients are consistently used in the
#'separate heatmaps that are plotted by patient.
#'
#'If \code{expr} is a matrix or a dataframe, then the "original" data are
#'plotted.  On the other hand, if \code{expr} is a list returned in the
#'\code{'Estimations'} element of \code{\link{TcGSA.LR}}, then it is those
#'"estimations" made by the \code{\link{TcGSA.LR}} function that are plotted.
#'
#'If \code{descript} is \code{FALSE}, the second element of \code{margins} can
#'be reduced (for instance use \code{margins = c(5, 10)}), as there is not so
#'much need for space in order to display only the gene set names, without
#'their description.
#'
#'The median shown in the heatmap uses the respectively standardized (reduced
#'and centered) expression of the genes over the patients.
#'
#'@param x 
#'a \bold{tcgsa} object.
#'
#'@param threshold 
#'the threshold at which the FDR or the FWER should be controlled.
#'
#'@param myproc 
#'a vector of character strings containing the names of the
#'multiple testing procedures for which adjusted p-values are to be computed.
#'This vector should include any of the following: "\code{Bonferroni}",
#'"\code{Holm}", "\code{Hochberg}", "\code{SidakSS}", "\code{SidakSD}",
#'"\code{BH}", "\code{BY}", "\code{ABH}", "\code{TSBH}" or "\code{none}".  
#'"\code{none}" indicates no adjustment for multiple testing. See
#'\code{\link[multtest:mt.rawp2adjp]{mt.rawp2adjp}} for details.  Default is
#'"\code{BY}", the Benjamini & Yekutieli (2001) step-up FDR-controlling
#'procedure (general dependency structures).  In order to control the FWER(in
#'case of an analysis that is more a hypothesis confirmation than an
#'exploration of the expression data), we recommend to use "\code{Holm}", the
#'Holm (1979) step-down adjusted p-values for strong control of the FWER.
#'
#'@param nbsimu_pval 
#'the number of observations under the null distribution to
#'be generated in order to compute the p-values. Default is \code{1e+06}.
#'
#'@param expr 
#'either a matrix or dataframe of gene expression upon which
#'dynamics are to be calculated, or a list of gene sets estimation of gene
#'expression.  In the case of a matrix or dataframe, its dimension are \eqn{n}
#'x \eqn{p}, with the \eqn{p} sample in column and the \eqn{n} genes in row.
#'In the case of a list, its length should correspond to the number of gene
#'sets under scrutiny and each element should be an 3 dimension array of
#'estimated gene expression, such as for the list returned in the
#'\code{'Estimations'} element of \code{\link{TcGSA.LR}}.  See Details.
#'
#'@param Subject_ID 
#'a factor of length \eqn{p} that is in the same order as the
#'columns of \code{expr} (when it is a dataframe) and that contains the patient
#'identifier of each sample.
#TODO See Details.
#'
#'@param TimePoint 
#'a numeric vector or a factor of length \eqn{p} that is in
#'the same order as \code{Subject_ID} and the columns of \code{expr} (when it
#'is a dataframe), and that contains the time points at which gene expression
#'was measured.
#TODO See Details.
#'
#'@param baseline 
#'a character string which is the value of \code{TimePoint}
#'used as baseline.  
#TODO See Details.
#'
#'@param only.signif 
#'logical flag for plotting only the significant gene sets.
#'If \code{FALSE}, all the gene sets from the \bold{gmt} object contained in
#'\code{x} are plotted.  Default is \code{TRUE}.
#'@param group.var in the case of several treatment groups, this is a factor of
#'length \eqn{p} that is in the same order as \code{Timepoint},
#'\code{Subject_ID}, \code{sample_name} and the columns of \code{expr}.  It
#'indicates to which treatment group each sample belongs to.  Default is
#'\code{NULL}, which means that there is only one treatment group.  See
#'Details.
#'
#'@param Group_ID_paired 
#'a character vector of length \eqn{p} that is in the
#'same order as \code{Timepoint}, \code{Subject_ID}, \code{sample_name},
#'\code{group.var} and the columns of \code{expr}.  This argument must not be
#'\code{NULL} in the case of a paired analysis, and must be \code{NULL}
#'otherwise.  Default is \code{NULL}.  
#TODO See Details.
#'
#'@param ref 
#'the group which is used as reference in the case of several
#'treatment groups.  Default is \code{NULL}, which means that reference is the
#'first group in alphabetical order of the labels of \code{group.var}.  See
#'Details.
#'
#'@param group_of_interest 
#'the group of interest, for which dynamics are to be
#'computed in the case of several treatment groups.  Default is \code{NULL},
#'which means that group of interest is the second group in alphabetical order
#'of the labels of \code{group.var}.  
#TODO See Details.
#'
#'@param FUNcluster 
#'the clustering function used to agglomerate genes in
#'trends.  Default is \code{NULL}, in which a hierarchical clustering is
#'performed via the function \code{\link[cluster:agnes]{agnes}}, using the
#'metric \code{clustering_metric} and the method \code{clustering_method}.  See
#'\code{\link[cluster:clusGap]{clusGap}}
#'
#'@param clustering_metric 
#'character string specifying the metric to be used
#'for calculating dissimilarities between observations in the hierarchical
#'clustering when \code{FUNcluster} is \code{NULL}.  The currently available
#'options are \code{"euclidean"} and \code{"manhattan"}.  Default is
#'\code{"euclidean"}.  See \code{\link[cluster:agnes]{agnes}}.  Also, a \code{"sts"} option 
#'is available in TcGSA.  It implements the 'Short Time Series' distance 
#'[Moller-Levet et al., Fuzzy Clustering of short time series and unevenly distributed 
#'sampling points, \emph{Advances in Intelligent Data Analysis V}:330-340 Springer, 2003]
#'designed specifically for clustering time series.
#'
#'@param clustering_method 
#'character string defining the agglomerative method
#'to be used in the hierarchical clustering when \code{FUNcluster} is
#'\code{NULL}.  The six methods implemented are \code{"average"} ([unweighted
#'pair-]group average method, UPGMA), \code{"single"} (single linkage),
#'\code{"complete"} (complete linkage), \code{"ward"} (Ward's method),
#'\code{"weighted"} (weighted average linkage).  Default is \code{"ward"}.  See
#'\code{\link[cluster:agnes]{agnes}}.
#'
#'@param B 
#'integer specifying the number of Monte Carlo ("bootstrap") samples
#'used to compute the gap statistics.  Default is \code{500}.  See
#'\code{\link[cluster:clusGap]{clusGap}}.
#'@param max_trends integer specifying the maximum number of different clusters
#'to be tested.  Default is \code{4}.
#'@param aggreg.fun a character string such as \code{"mean"}, \code{"median"}
#'or the name of any other statistics function defined that returns a single
#'numeric value.  It specifies the function used to aggregate the observations
#'before the clustering.  Default is to \code{median}.  Default is
#'\code{"median"}.
#'
#'@param methodOptiClust 
#'character string indicating how the "optimal"" number
#'of clusters is computed from the gap statistics and their standard
#'deviations. Possible values are \code{"globalmax"}, \code{"firstmax"},
#'\code{"Tibs2001SEmax"}, \code{"firstSEmax"} and \code{"globalSEmax"}.
#'Default is \code{"firstSEmax"}.  See \code{'method'} in
#'\code{\link[cluster:clusGap]{clusGap}}, Details and \emph{Tibshirani et al.,
#'2001} in References.
#'
#'@param verbose 
#'logical flag enabling verbose messages to track the computing
#'status of the function.  Default is \code{TRUE}.
#'
#'@param clust_trends 
#'object of class \bold{\link{ClusteredTrends}} containing
#'already computed trends for the plotted gene sets.  Default is \code{NULL}.
#'
#'@param N_clusters 
#'an integer that is the number of clusters in which the
#'dynamics should be regrouped. The cutoff of the clustering tree is
#'automatically calculated accordingly.  Default is \code{NULL}, in which case
#'the dendrogram is not cut and no clusters are identified.
#'
#'@param myclusters 
#'a character vector of colors for predefined clusters of the
#'represented gene sets, with as many levels as the value of \code{N_clusters}.
#'Default is \code{NULL}, in which case the clusters are automatically
#'identified and colored via the \code{\link[stats:cutree]{cutree}} function and the
#'\code{N_clusters} argument only.
#'
#'@param label.clusters 
#'if \code{N_clusters} is not \code{NULL}, a character
#'vector of length \code{N_clusterss}.  Default is \code{NULL}, in which case
#'if \code{N_clusters} is not \code{NULL}, clusters are simply labeled with
#'numbers.
#'
#'@param prev_rowCL 
#'a \bold{\link[stats:hclust]{hclust}} object, such as the one return by the
#'present plotting function (see Value) for instance.  If not \code{NULL}, no
#'clustering is calculated by the present plotting function and this tree is
#'used to represent the gene sets dynamics.  Default is \code{NULL}.
#'
#'@param descript 
#'logical flag indicating that the description of the gene sets
#'should appear after their name on the right side of the plot if \code{TRUE}.
#'Default is \code{TRUE}.  See Details.
#'
#'@param plotAll 
#'logical flag indicating whether a first heatmap with the median
#'over all the patients should be plotted, or not.  Default is \code{TRUE}.
#'
#'@param color.vec 
#'a character strings vector used to define the color
#'\link[grDevices:palette]{palette} used in the plot.  Default is
#'\code{c("#D73027", "#FC8D59","lightyellow", "#91BFDB", "#4575B4")}.
#'
#'@param legend.breaks 
#'a numeric vector indicating the splitting points for
#'coloring.  Default is \code{NULL}, in which case the break points will be
#'spaced equally and symmetrically about 0.
#'
#'@param label.column 
#'a vector of character strings with the labels to be
#'displayed for the columns (i.e. the time points).  Default is \code{NULL}.
#'
#'@param time_unit 
#'the time unit to be displayed (such as \code{"Y"},
#'\code{"M"}, \code{"W"}, \code{"D"}, \code{"H"}, etc) next to the values of
#'\code{TimePoint} in the columns labels when \code{label.column} is
#'\code{NULL}.  Default is \code{""}.
#'
#'@param cex.label.row 
#'a numerical value giving the amount by which row labels
#'text should be magnified relative to the default \code{1}.
#'
#'@param cex.label.column 
#'a numerical value giving the amount by which column
#'labels text should be magnified relative to the default \code{1}.
#'
#'@param margins 
#'numeric vector of length 2 containing the margins (see
#'\code{\link{par}(mar= *))} for column and row names, respectively.  Default
#'is \code{c(15, 100)}.  See Details.
#'
#'@param heatKey.size 
#'the size of the color key for the heatmap fill.  Default
#'is \code{1}.
#'
#'@param dendrogram.size 
#'the horizontal size of the dendrogram.  Default is
#'\code{1}
#'
#'@param heatmap.height 
#'the height of the heatmap.  Default is \code{1}
#'
#'@param heatmap.width 
#'the width of the heatmap.  Default is \code{1}
#'
#'@param cex.clusterKey 
#'a numerical value giving the amount by which the
#'clusters legend text should be magnified relative to the default \code{1},
#'when \code{N_clusters} is not \code{NULL}.
#'
#'@param cex.main 
#'a numerical value giving the amount by which title text
#'should be magnified relative to the default \code{1}.
#'
#'@param horiz.clusterKey 
#'a logical flag; if \code{TRUE}, set the legend for
#'clusters horizontally rather than vertically.  Only used if the
#'\code{N_clusters} argument is not \code{NULL}.  Default is \code{TRUE}.
#'
#'@param main 
#'a character string for an optional title.  Default is
#'\code{NULL}.
#'
#'@param subtitle 
#'a character string for an optional subtitle.  Default is
#'\code{NULL}.
#'
#'@param \dots 
#'other parameters to be passed through to plotting functions.
#'
#'@return An object of class \bold{\link[stats:hclust]{hclust}} which describes the tree
#'produced by the clustering process.  The object is a list with components:
#'\itemize{
	#'\item \code{merge} an \eqn{n-1} by \eqn{2} matrix.  Row \eqn{i} of
	#'\code{merge} describes the merging of clusters at step i of the clustering.
	#'If an element \eqn{j} in the row is negative, then observation -\eqn{j} was
	#'merged at this stage.  If \eqn{j} is positive then the merge was with the
	#'cluster formed at the (earlier) stage \eqn{j} of the algorithm.  Thus
	#'negative entries in merge indicate agglomerations of singletons, and positive
	#'entries indicate agglomerations of non-singletons.
	#'\item \code{height} a set of \eqn{n-1} real values (non-decreasing for
	#'ultrametric trees).  The clustering height: that is, the value of the
	#'criterion associated with the Ward clustering method.
	#'\item \code{order} a vector giving the permutation of the original
	#'observations suitable for plotting, in the sense that a cluster plot using
	#'this ordering and matrix merge will not have crossings of the branches.
	#'\item \code{labels} the gene sets name.
	#'\item \code{call} the call which produced the result clustering:
	#'\cr\code{hclust(d = dist(map2heat, method = "euclidean"), method = "ward.D2")}
	#'\item method "ward.D2", as it is the clustering method that has been used
#'for clustering the gene set trends.
	#'\item \code{dist.method} "euclidean", as it is the distance that has been used
#'for clustering the gene set trends.
	#'\item \code{legend.breaks} a numeric vector giving the splitting points used
	#'for coloring the heatmap.  If \code{plot} is \code{FALSE}, then it is
	#'\code{NULL}.
	#'\item \code{myclusters} a character vector of colors for clusters of the
	#'represented gene sets, with as many levels as the value of \code{N_clusters}.
	#'If no clusters were represented, than this is \code{NULL}.
	#'\item \code{ddr} a \bold{dendrogram} object with the reordering used for the
	#'heatmap.  See \code{\link[gplots:heatmap.2]{heatmap.2}}.
	#'\item \code{clustersExport} a data frame with 2 variables containing the two
	#'following variables : \itemize{ \item \code{GeneSet}: the gene sets
	#'clustered.  \item \code{Cluster}: the cluster they belong to.  } The data
	#'frame is order by the variable \code{Cluster}.
#'}
#'
#'@author Boris P. Hejblum
#'
#'@seealso \code{\link{plot.TcGSA}}, \code{\link[gplots:heatmap.2]{heatmap.2}},
#'\code{\link{TcGSA.LR}}, \code{\link[stats:hclust]{hclust}}
#'
#'@references Hejblum BP, Skinner J, Thiebaut R, (2015) 
#'Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. 
#'\emph{PLoS Computat. Biol.} 11(6): e1004310.
#'doi: 10.1371/journal.pcbi.1004310
#'
#'@import ggplot2
#'
#'@importFrom stats as.dendrogram
#'
#'@export plotPat.TcGSA
#'
#'@examples
#'
#'\dontrun{
#'data(data_simu_TcGSA)
#'
#'tcgsa_sim_1grp <- TcGSA.LR(expr=expr_1grp, gmt=gmt_sim, design=design, 
#'                           subject_name="Patient_ID", time_name="TimePoint",
#'                           time_func="linear", crossedRandom=FALSE)
#'
#'plotPat.TcGSA(x=tcgsa_sim_1grp, expr=expr_1grp, 
#'     Subject_ID=design$Patient_ID, TimePoint=design$TimePoint,
#'     B=100,
#'     time_unit="H"
#'     )
#'
#'plotPat.TcGSA(x=tcgsa_sim_1grp, expr=tcgsa_sim_1grp$Estimations, 
#'     Subject_ID=design$Patient_ID, TimePoint=design$TimePoint,
#'     baseline=1, 
#'     B=100,
#'     time_unit="H"
#'     )
#'}
#'
plotPat.TcGSA <-
function(x, threshold=0.05, myproc="BY", nbsimu_pval=1e+06, 
         expr, Subject_ID, TimePoint, 
         baseline=NULL, only.signif=TRUE,
         group.var=NULL, Group_ID_paired=NULL, ref=NULL, group_of_interest=NULL,
         FUNcluster=NULL, clustering_metric="euclidian", clustering_method="ward", B=500,
         max_trends=4, aggreg.fun="median",
         methodOptiClust = "firstSEmax",
         verbose=TRUE,
         clust_trends=NULL,
         N_clusters=NULL, myclusters=NULL, label.clusters=NULL, prev_rowCL=NULL,
         descript=TRUE, plotAll=TRUE,
         color.vec=c("darkred", "#D73027", "#FC8D59", "snow", "#91BFDB", "#4575B4", "darkblue"),
         legend.breaks=NULL,
         label.column=NULL, time_unit="", 
         cex.label.row=1, cex.label.column=1, margins=c(5, 25), 
         heatKey.size=1, dendrogram.size=1, heatmap.height=1, heatmap.width=1,
         cex.clusterKey=1, cex.main=1,
         horiz.clusterKey=TRUE,
         main=NULL, subtitle=NULL, 
         ...){
  
  cat("NOT IMPLEMENTED YET")
	
if(FALSE){
  Fun_byIndex<-function(X, index, fun){
    tapply(X, INDEX=index, FUN = fun)
  }
  
  gmt <- x[["GeneSets_gmt"]]
  
  if(!is.null(baseline)){
    if(!(baseline %in% unique(TimePoint))){
      stop("The 'baseline' value used is not one of the time points in 'TimePoint'...\n\n")
    }
  }
  
  if(is.null(main)){
    mymain <-paste("Median trends", subtitle, sep="")
  }else if(!is.null(subtitle)){
    mymain <-paste(main, "\n", subtitle, sep="")
  }else{
    mymain <- main
  }
  
  
  
  if(is.null(clust_trends)){
    clust_trends <- clustTrend(x=x, expr=expr, Subject_ID=Subject_ID, TimePoint=TimePoint, baseline=baseline, only.signif=TRUE,
                               group.var=group.var, Group_ID_paired=Group_ID_paired, ref=ref, group_of_interest=group_of_interest,
                               FUNcluster=FUNcluster, clustering_metric=clustering_metric, clustering_method=clustering_method, B=B,
                               max_trends=max_trends, aggreg.fun=aggreg.fun,
                               methodOptiClust = methodOptiClust,
                               indiv="genes",
                               verbose=verbose
    )
  }else if(class(clust_trends)!="ClusteredTrends"){
    stop("The 'clust_trends' argument is not of the class 'ClusteredTrends', see the clustTrend function")
  }
  
  if(verbose){cat("Initializing clustering on all the patients...\n")}
  hc <- plot.TcGSA(x=x, threshold=threshold, myproc=myproc, nbsimu_pval=nbsimu_pval, 
                   expr=expr, Subject_ID=Subject_ID, TimePoint=TimePoint, 
                   baseline=baseline, only.signif=only.signif,
                   group.var=group.var, Group_ID_paired=Group_ID_paired, ref=ref, group_of_interest=group_of_interest,
                   FUNcluster=FUNcluster, clustering_metric=clustering_metric, clustering_method=clustering_method, B=B,
                   max_trends=max_trends, aggreg.fun=aggreg.fun,
                   methodOptiClust = methodOptiClust,
                   indiv="genes",
                   verbose=verbose,
                   clust_trends=clust_trends,
                   N_clusters=N_clusters, myclusters=myclusters, label.clusters=label.clusters, prev_rowCL=prev_rowCL,
                   descript=descript, plot=plotAll,
                   color.vec=color.vec,
                   legend.breaks=legend.breaks,
                   label.column=label.column, time_unit=time_unit, 
                   cex.label.row=cex.label.row, cex.label.column=cex.label.column, margins=margins, heatKey.size=heatKey.size, dendrogram.size=dendrogram.size, heatmap.height=heatmap.height, heatmap.width=heatmap.width,
                   cex.clusterKey=cex.clusterKey, cex.main=cex.main,
                   horiz.clusterKey=horiz.clusterKey,
                   main="Median trends", subtitle="over all patients")

  if(is.null(prev_rowCL)){
    clRows=TRUE
    if(only.signif){
      signif <- multtest.TcGSA(x, threshold, myproc, nbsimu_pval)
      select <- which(signif$adj_pval<0.05)
      if(!length(select)>0){
        stop("No gene sets significant")
      }
      subtitle <- paste(subtitle, "\n", length(which(signif$adj_pval<0.05)), "/", length(signif$adj_pval), " gene sets significant with ", x[["func_form"]], " shape", sep="")
    }else{
      select <- 1:length(gmt$geneset.names)
      paste(subtitle, "\n", length(signif$adj_pval), " gene sets", sep="")
    }
  }else{
    if(!is.null(prev_rowCL$ddr)){
      clRows=prev_rowCL$ddr
    }else{
      clRows=stats::as.dendrogram(prev_rowCL)
    }
    
    select <- match(prev_rowCL$labels, gmt$geneset.names)
    if(length(which(is.na(select)))>0){
      select <- match(prev_rowCL$labels, gsub(": Undetermined","", paste(gmt$geneset.names, ": ", gmt$geneset.description, sep="")))
    }
    if(length(which(is.na(select)))>0){
      stop("Geneset names used in the previous clustering don't match the 'geneset.names' from the 'gmt' element of the 'x' argument")
    }
    
    if(is.null(myclusters) && !is.null(prev_rowCL$myclusters)){
      myclusters <- prev_rowCL$myclusters
    }  
  }
  
  pat <- levels(Subject_ID)
  #par('mfrow'=c(ceiling(length(pat)/4),4))
  

  for (i in 1:length(pat)){
    p <- pat[i]
    clust_trends_p <- clust_trends
    
    if(verbose){
      cat(paste("Patient ", i, "/", length(pat),":", sep=""))
    }
    
    for(interest in select){
      gs <- gmt$geneset.names[interest]
      if(is.null(group.var)){
        if(is.data.frame(expr)){
          select_probe <- intersect(rownames(expr), unique(gmt$genesets[[interest]]))
          data_sel <- as.matrix(expr[select_probe, ])
        }else if(is.list(expr)){
          expr_sel <- expr[[interest]]
          expr_sel <- expr_sel[, , order(as.numeric(dimnames(expr_sel)[[3]]))]
          data_sel <- matrix(expr_sel, nrow=dim(expr_sel)[1], ncol=dim(expr_sel)[2]*dim(expr_sel)[3])
          select_probe <- dimnames(expr_sel)[[1]]
          rownames(data_sel) <- select_probe
          TimePoint <- sort(as.numeric(rep(dimnames(expr_sel)[[3]], dim(expr_sel)[2])))
          Subject_ID <- rep(dimnames(expr_sel)[[2]], dim(expr_sel)[3])
        }
        
        data_stand <- t(apply(X=data_sel, MARGIN=1, FUN=scale))
        # So the genes expression is comparable over the patients.
        
      }else{
        if(!is.null(baseline)){
          stop("the 'baseline' argument is not NULL while a grouping variable is supplied in 'group.var'...\n")
        }
        if(is.data.frame(expr)){
          select_probe <- intersect(rownames(expr), unique(gmt$genesets[[interest]]))
          data_sel <- as.matrix(expr[select_probe,])
        }else if(is.list(expr)){
          expr_sel <- expr[[interest]]
          expr_sel <- expr_sel[, , order(as.numeric(dimnames(expr_sel)[[3]]))]
          data_sel <- matrix(expr_sel, nrow=dim(expr_sel)[1], ncol=dim(expr_sel)[2]*dim(expr_sel)[3])
          select_probe <- dimnames(expr_sel)[[1]]
          rownames(data_sel) <- select_probe
          if(!is.null(Group_ID_paired)){
            Group_ID_paired <- Group_ID_paired[order(TimePoint)] # watch out for the ordering
          }
          TimePoint <- sort(as.numeric(rep(dimnames(expr_sel)[[3]], dim(expr_sel)[2])))
          Subject_ID <- rep(dimnames(expr_sel)[[2]], dim(expr_sel)[3])
        }
        data_stand_ref <- t(apply(X=data_sel[,group.var==ref], MARGIN=1, FUN=scale))
        data_stand_interest <- t(apply(X=data_sel[,group.var==group_of_interest], MARGIN=1, FUN=scale))
        
        if(is.null(Group_ID_paired)){
          data_stand_ByTP_ref <- t(apply(X=data_stand_ref, MARGIN=1, FUN=Fun_byIndex, index=as.factor(TimePoint)[group.var==ref], fun=aggreg.fun))
          data_stand_ByTP_interest <- t(apply(X=data_stand_interest, MARGIN=1, FUN=Fun_byIndex, index=as.factor(TimePoint)[group.var==group_of_interest], fun=aggreg.fun))                         
          data_stand_ByTP <- data_stand_ByTP_interest-data_stand_ByTP_ref
        }else{
          data_diff <- t(apply(X=cbind.data.frame(data_stand_interest, -data_stand_ref), MARGIN=1, FUN=Fun_byIndex, 
                                index=(as.factor(c(TimePoint[group.var==group_of_interest], TimePoint[group.var==ref])):as.factor(c(as.character(Group_ID_paired)[group.var==group_of_interest], as.character(Group_ID_paired)[group.var==ref]))),
                                fun=sum))
          data_stand <- t(apply(X=data_diff, MARGIN=1, FUN=Fun_byIndex, index=sort(as.factor(TimePoint[group.var==group_of_interest])), fun=aggreg.fun))
        }
      }
      
      
      data_stand_ByTP <- data_stand[,Subject_ID==p]
      
      if(!is.null(baseline)){
        colbaseline <- which(sort(unique(TimePoint))==baseline)
        if(length(colbaseline)==0){
          stop("the 'baseline' value used is not one of the time points in 'TimePoint'...\n")
        }
        data_stand_ByTP <- data_stand_ByTP-data_stand_ByTP[,colbaseline]
      }
      
      medoids <- as.data.frame(t(apply(X=data_stand_ByTP, MARGIN=2, FUN=Fun_byIndex, index=clust_trends_p$GenesPartition[[gs]], fun="median")))
      if(dim(medoids)[1]==1){
        medoids <- cbind.data.frame("TimePoint"= TimePoint[Subject_ID==p], "1"=t(medoids))
        rownames(medoids) <- TimePoint[Subject_ID==p]
      }else{
        medoids <- cbind.data.frame("TimePoint"= rownames(medoids), medoids)
      }
      clust_trends_p$ClustMeds[[gs]] <- medoids
    }

    plot.TcGSA(x=x, 
               threshold=threshold, myproc=myproc, nbsimu_pval=nbsimu_pval, 
               expr=NULL, Subject_ID=NULL, TimePoint=TimePoint, 
               baseline=baseline, only.signif=only.signif,
               group.var=group.var, Group_ID_paired=Group_ID_paired, ref=ref, group_of_interest=group_of_interest,
               FUNcluster=FUNcluster, clustering_metric=clustering_metric, clustering_method=clustering_method, B=B,
               max_trends=max_trends, aggreg.fun=aggreg.fun,
               methodOptiClust = methodOptiClust,
               indiv="genes",
               verbose=verbose,
               clust_trends=clust_trends_p,
               N_clusters=NULL, myclusters=hc$myclusters, label.clusters=label.clusters, prev_rowCL=hc,
               descript=descript, plot=TRUE,
               color.vec=color.vec,
               legend.breaks=hc$legend.breaks,
               label.column=label.column, time_unit=time_unit, 
               cex.label.row=cex.label.row, cex.label.column=cex.label.column, margins=margins, heatKey.size=heatKey.size, dendrogram.size=dendrogram.size, heatmap.height=heatmap.height, heatmap.width=heatmap.width,
               cex.clusterKey=cex.clusterKey, cex.main=cex.main,
               horiz.clusterKey=horiz.clusterKey,
               main="Median Trends", subtitle=paste("Patient", p)
    )
      
    if(verbose){
      cat(" done\n")
    }           
  }
  return(hc)
}
}
borishejblum/TcGSA documentation built on Dec. 6, 2017, 2:04 p.m.