@@ -22,8 +22,8 @@ License: Artistic-2.0

 BugReports: https://github.com/RGLab/COMPASS/issues

 VignetteBuilder: knitr

 Depends:

-    R (>= 3.0.3)

-LinkingTo: Rcpp (>= 0.11.0)

+    R  (>= 3.0.3)

+LinkingTo: Rcpp  (>= 0.11.0)

 Maintainer: Greg Finak <[email protected]>

 Imports:

     methods,

@@ -45,7 +45,8 @@ Imports:

     tidyr,

     rlang,

     BiocStyle,

-    rmarkdown

+    rmarkdown,

+    foreach

 Suggests:

     flowWorkspace (>= 3.33.1),

     flowCore,

@@ -54,9 +55,11 @@ Suggests:

     testthat,

     devtools,

     flowWorkspaceData,

-    ggplot2

+    ggplot2,

+    doMC, 

+    progress 

 LazyLoad: yes

 LazyData: yes

 biocViews: ImmunoOncology, FlowCytometry

 Encoding: UTF-8

-RoxygenNote: 6.1.1

+RoxygenNote: 7.1.0

@@ -45,6 +45,7 @@ export(SimpleCOMPASS)

 export(TotalCellCounts)

 export(UniqueCombinations)

 export(categories)

+export(checkCOMPASSConvergence)

 export(getCounts)

 export(markers)

 export(metadata)

@@ -58,6 +59,7 @@ export(shinyCOMPASSDeps)

 export(translate_marker_names)

 export(transpose_list)

 import(data.table)

+import(foreach)

 import(grid)

 import(magrittr)

 import(methods)

@@ -113,4 +115,6 @@ importFrom(utils,globalVariables)

 importFrom(utils,head)

 importFrom(utils,installed.packages)

 importFrom(utils,sessionInfo)

+importFrom(utils,setTxtProgressBar)

+importFrom(utils,txtProgressBar)

 useDynLib(COMPASS, .registration=TRUE)

@@ -269,8 +269,8 @@ COMPASSContainerFromGatingSet<-function(gs = NULL, node = NULL, filter.fun = NUL

     expr <- do.call(c,strsplit(as.character(expr),"\\|"))

     ##validity check on the parent children relationship

-    children.ids <- sapply(full.child.nodes, flowWorkspace:::.getNodeInd, obj = gs[[1]], USE.NAMES = FALSE)

-    map.ids <- sapply(expr, flowWorkspace:::.getNodeInd, obj = gs[[1]], USE.NAMES = FALSE)

+    children.ids <- sapply(full.child.nodes, flowWorkspace::.getNodeInd, obj = gs[[1]], USE.NAMES = FALSE)

+    map.ids <- sapply(expr, flowWorkspace::.getNodeInd, obj = gs[[1]], USE.NAMES = FALSE)

     ind <- !map.ids %in% children.ids

     if(any(ind))

       stop(paste(expr[ind], collapse = "|"), " are not the children node of ", unique.node)

@@ -3,6 +3,9 @@

 #' @param x  a \code{COMPASSResult} object.

 #' @param markers a \code{vector} of marker names.

 #' @param degree the \code{numeric} degree of functionality to test.

+#' @param max.prob \code{logical} Use the max probability rather than the average across subsets. Defaults to FALSE.

+#' @param at_least_n \code{logical} response of degree x or greater with at_least_n subsets responding.

+

 #' @description

 #' Compute a response probability based on the selected markers, evaluating the probability

 #' that a subject exhibits a response of size \code{degree} or greater.

@@ -11,17 +14,17 @@

 #' @details

 #' The response is computed from the sampled Gamma matrix, subsetting on the selected markers, and

 #' @return  A \code{vector} of response probabilities for each subject.

-#' @export

-#'

 #' @examples

+#'

 #' Response(CR, markers = c("M1","M2","M3"), degree = 2)

-Response <- function(x, markers, degree){

+#' @export

+Response <- function(x, markers, degree, max.prob, at_least_n){

   UseMethod("Response")

 }

 ##' @rdname Response

 ##' @export

-Response.COMPASSResult <- function(x, markers = NULL, degree = 1) {

+Response.COMPASSResult <- function(x, markers = NULL, degree = 1, max.prob = FALSE, at_least_n = NULL) {

   ## we drop the last column as it is the 'NULL' category

   if (is.null(markers)) {

     markers <- markers(x)

@@ -33,6 +36,7 @@ Response.COMPASSResult <- function(x, markers = NULL, degree = 1) {

       stop("Invalid marker names")

     }

     message("Computing the probability of response of degree >= ",degree, " from markers: ", paste(markers, collapse = ", "))

+    if(!is.null(at_least_n)){message("in at least ",at_least_n," subsets.")}

     new_categories = unique(categories(x, FALSE)[, markers, drop = FALSE])

     all_categories = categories(x, FALSE)[, markers, drop = FALSE]

     suppressWarnings({dmat = as.matrix(pdist(new_categories, all_categories))})

@@ -43,24 +47,39 @@ Response.COMPASSResult <- function(x, markers = NULL, degree = 1) {

     }

     new_mean_gamma = apply(cat_indices, 2, function(i)

       apply(Gamma(x)[, i, ], 1, mean))

+    new_gamma <- Gamma(x)[,cat_indices,]

     new_categories = cbind(new_categories, Counts = rowSums(new_categories))

     reord = c(setdiff(1:nrow(new_categories), which(new_categories[, "Counts"] ==

                                                       0)), which(new_categories[, "Counts"] == 0))

     new_categories = new_categories[reord, ]

     new_mean_gamma = new_mean_gamma[, reord]

+    new_gamma = new_gamma[,reord,]

     colnames(new_mean_gamma) = apply(new_categories[, -ncol(new_categories)], 1, function(x)

       paste0(x, collapse = ""))

+    dimnames(new_gamma)[[2]] = apply(new_categories[, -ncol(new_categories)], 1, function(x)

+      paste0(x, collapse = ""))

     include_cols <- new_categories[,"Counts"] >= degree

     if (sum(include_cols) == 0) {

-      response <- matrix(rep(0,nrow(new_mean_gamma)),nrow=nrow(new_mean_gamma),ncol=1)

+      response <- matrix(rep(0,nrow(new_mean_gamma)),nrow = nrow(new_mean_gamma),ncol=1)

       rownames(response) <- rownames(new_mean_gamma)

       colnames(response) <- paste0("Pr(response|degree >=",degree,")")

     }else{

+      #condition on degree >= x

       response <- new_mean_gamma[,include_cols, drop = FALSE]

-      response <- rowMeans(response)

-      response <- matrix(response, ncol = 1, nrow = length(response))

-      rownames(response) <- rownames(new_mean_gamma)

-      colnames(response) <- paste0("Pr(response | degree >=",degree,")")

+

+      if (!is.null(at_least_n)) {

+      response <- structure(rowMeans(apply(new_gamma[,include_cols,,drop = FALSE],c(1,3),sum) >= at_least_n),

+                            dim = c(nrow(response),1), names = NULL, dimnames = list(rownames(response),paste0("Pr(response | degree >=",degree,")")))

+      }

+

+      if (!max.prob  & is.null(at_least_n)) {

+        response <- structure(rowMeans(response), dim = c(nrow(response),1), names = NULL, dimnames = list(rownames(response),paste0("Pr(response | degree >=",degree,")")))

+      }else if (max.prob  &  is.null(at_least_n)){

+        response <- structure(apply(response,1,max),dim=c(nrow(response),1), names = NULL, dimnames = list(rownames(response), paste0("Pr(response | degree >=",degree,")")))

+      }

+      # response <- matrix(response, ncol = 1, nrow = length(response))

+      # rownames(response) <- rownames(new_mean_gamma)

+      # colnames(response) <- paste0("Pr(response | degree >=",degree,")")

     }

     response

 }

new file mode 100644

@@ -0,0 +1,156 @@

+.rhat<-function(...){

+	#confirm all elements are the same length

+	cond<-diff(range(as.vector(unlist(Map(length,list(...))))))==0

+	if(!cond)

+	{

+		stop("Not all arugments are of equal length")

+	}

+	#rank normalization according to eq 14 of arxiv: 1903.08008.pdf

+	.ranknorm<-function(...){

+		(matrix(rank(do.call(cbind,list(...))),ncol=length(list(...)))-3/4)/(length(list(...)[[1]])-1/4)

+	}

+

+	phimn<-.ranknorm(...)

+

+	# mean of each chain

+	.phiM <- function(phimn){

+		colMeans(phimn)

+	}

+

+	#mean across chains

+	.phi <- function(phim){

+		mean(phim)

+	}

+

+	PHIM<-.phiM(phimn)

+	PHI<-.phi(PHIM)

+

+	#between chain variance

+	.B <- function(PHI,PHIM,PHIMN){

+		sum((PHIM-PHI)^2)*nrow(PHIMN)/(ncol(PHIMN)-1)

+	}

+

+	between<-.B(PHI,PHIM,phimn)

+	# s_squared

+	.ssq<-function(PHIMN,PHIM){

+		colSums((t(t(PHIMN)-PHIM))^2)/(nrow(PHIMN)-1)

+	}

+

+	s2<-.ssq(phimn,PHIM)

+	within<-mean(s2)

+	#Rhat

+	sqrt((((nrow(phimn)-1)/nrow(phimn))*within+(1/nrow(phimn))*between)/within)

+

+}

+

+

+#' Check the convergence of multiple COMPASS models fit with different seeds.

+#'

+#' Computes Rhat for all nsubjects x nsubsets parameters across the list of models, treated as separate chains.

+#' Flags cell populations and subjects with Rhat > 1.01. 

+#' The most frequently flagged population is passed for further diagnostis to compute Rhat between all pairs of models and

+#' to try and identify the model or models that are outliers.

+#' The outliers are removed and a list of good models is returned.

+#' @note Uses foreach and doMC, so it won't work on windows.

+#' 

+#' @param mlist A list of COMPASSResult models. Each should be fit to the same data, but with different seeds.

+#' @param ncores The number of cores to use, if supported on the system.

+#' @return A list of COMPASSResult models that are consistent / have converged. 

+#' @export

+#' @importFrom utils txtProgressBar setTxtProgressBar

+#' @import foreach

+#' @examples

+#' data(COMPASS)

+#' set.seed(100)

+#' fit <- COMPASS(CC,

+#'   category_filter=NULL,

+#'   treatment=trt == "Treatment",

+#'   control=trt == "Control",

+#'   verbose=FALSE,

+#'   iterations=100 ## set higher for a real analysis

+#' )

+#' set.seed(200)

+#' fit2 <- COMPASS(CC,

+#'   category_filter=NULL,

+#'   treatment=trt == "Treatment",

+#'   control=trt == "Control",

+#'   verbose=FALSE,

+#'   iterations=100 ## set higher for a real analysis

+#' )

+#' checkCOMPASSConvergence(list(fit,fit2))

+checkCOMPASSConvergence<-function(mlist,ncores=1){

+  if(!is.list(mlist)){

+    stop("mlist should be a list of COMPASSResult fit to the same data with different random seeds.")

+  }

+  allok<-TRUE

+  if(!requireNamespace("foreach",quietly=TRUE)){

+	  message("foreach is required to run checkCOMPASSConvergence")

+	  allok<-FALSE

+  }

+  if(requireNamespace("doMC",quietly=TRUE)&allok){

+  	doMC::registerDoMC(ncores)

+  }else{

+	message("You may want to install the doMC package")

+  }

+  if(!requireNamespace("progress",quietly=TRUE)){

+	  message("progress is required to run checkCOMPASSConvergence")

+	  allok<-FALSE

+  }

+  if(allok){

+  

+  if(length(mlist)<2){

+    stop("mlist must be a list of > 2 COMPASS Results fit to the same data.")

+  }

+  for(i in 2:length(mlist)){

+    if(!all.equal(mlist[[1]]$data,mlist[[i]]$data)){

+      stop("Input models are not all fit to the same data. Stopping,")

+    }

+  }

+  # List of models is compatible.

+  # Extract the gamma matrices

+  gammas<-Map(function(x)x$fit$gamma,mlist)

+  #How many subjects and cell subsets

+  nsamples<-dim(gammas[[1]])[1]

+  nsubsets<-dim(gammas[[1]])[2]

+  message(paste0("Checking convergence of ",nsamples*nsubsets, " parameters"))

+  rhat_matrix_all<-matrix(0,nrow=nsamples,ncol=nsubsets)

+  pb<-txtProgressBar(min=0,max=1,style=3,title = "Checking convergence...")

+  suppressWarnings({

+  rhat_matrix_all<-foreach(i = 1:nsamples,.combine = rbind,.errorhandling = "remove") %dopar% {

+    setTxtProgressBar(pb,max(pb$getVal(),i/nsamples))

+     result<-rep(0,nsubsets)

+    for(j in 1:nsubsets){

+      result[j]<-do.call(.rhat,Map(function(x)x[i,j,],gammas))

+    }

+     result

+   }})

+   

+   ranked_problematic_subsets<-sort(table(which(rhat_matrix_all>1.01,T)[,2]),decreasing=TRUE)

+   message(paste0("\nDetected convergence issues in ",length(ranked_problematic_subsets)," cell subsets"))

+   message("Running further diagnostics to identify outlier chains..")

+   top_problem_subset<-as.numeric(names(ranked_problematic_subsets)[1])

+   subset_phenotype<-paste0(paste(names(which(mlist[[1]]$fit$categories[top_problem_subset,]==1)),collapse="+"),"+")

+   subset_phenotype<-gsub("Counts\\+","",subset_phenotype)

+   

+   #pick a subject with poor convergence for this subset

+   j<-as.numeric(gsub("result\\.","",names(which(which(rhat_matrix_all>1.01,T)[,2]==top_problem_subset)[1])))

+   chains<-Map(function(x)x[j,top_problem_subset,],gammas)

+   combinations<-combn(length(gammas),2)

+   rhat_pair_matrix<-matrix(0,ncol=length(gammas),nrow=length(gammas))

+   for(i in 1:ncol(combinations)){

+     j<-combinations[,i][1]

+     k<-combinations[,i][2]

+     rhat_pair_matrix[j,k]<-.rhat(chains[[j]],chains[[k]])

+   }

+   rhat_pair_matrix[lower.tri(rhat_pair_matrix)]<-t(rhat_pair_matrix)[lower.tri(t(rhat_pair_matrix))]

+   drop_ind<-sapply(1:length(gammas), function(i) {

+     if (all(rhat_pair_matrix[, i][-i] > 1.01)) {

+       message(paste0("Dropping model ",i))

+       i

+     }

+   })

+   drop_ind<-unlist(Filter(function(x)!is.null(x),drop_ind))

+   mlist[-drop_ind]

+  }

+}

+

@@ -4,12 +4,22 @@

 \alias{COMPASS}

 \title{Fit the COMPASS Model}

 \usage{

-COMPASS(data, treatment, control, subset = NULL,

+COMPASS(

+  data,

+  treatment,

+  control,

+  subset = NULL,

   category_filter = function(x) colSums(x > 5) > 2,

-  filter_lowest_frequency = 0, filter_specific_markers = NULL,

-  model = "discrete", iterations = 40000, replications = 8,

-  keep_original_data = FALSE, verbose = TRUE, dropDegreeOne = FALSE,

-  ...)

+  filter_lowest_frequency = 0,

+  filter_specific_markers = NULL,

+  model = "discrete",

+  iterations = 40000,

+  replications = 8,

+  keep_original_data = FALSE,

+  verbose = TRUE,

+  dropDegreeOne = FALSE,

+  ...

+)

 }

 \arguments{

 \item{data}{An object of class \code{COMPASSContainer}.}

@@ -4,8 +4,14 @@

 \alias{COMPASSContainer}

 \title{Generate the Data Object used by COMPASS}

 \usage{

-COMPASSContainer(data, counts, meta, individual_id, sample_id,

-  countFilterThreshold = 0)

+COMPASSContainer(

+  data,

+  counts,

+  meta,

+  individual_id,

+  sample_id,

+  countFilterThreshold = 0

+)

 }

 \arguments{

 \item{data}{A list of matrices. Each matrix \code{M_i} is made up of

@@ -4,10 +4,17 @@

 \alias{COMPASSContainerFromGatingSet}

 \title{Create a COMPASS Container from a GatingSet}

 \usage{

-COMPASSContainerFromGatingSet(gs = NULL, node = NULL,

-  filter.fun = NULL, individual_id = "PTID", mp = NULL,

-  matchmethod = c("Levenshtein", "regex"), markers = NA,

-  swap = FALSE, countFilterThreshold = 5000)

+COMPASSContainerFromGatingSet(

+  gs = NULL,

+  node = NULL,

+  filter.fun = NULL,

+  individual_id = "PTID",

+  mp = NULL,

+  matchmethod = c("Levenshtein", "regex"),

+  markers = NA,

+  swap = FALSE,

+  countFilterThreshold = 5000

+)

 }

 \arguments{

 \item{gs}{a \code{GatingSet} or \code{GatingSetList}}

@@ -4,8 +4,13 @@

 \alias{COMPASSfitToCountsTable}

 \title{Extract a table of counts from a COMPASSResult object}

 \usage{

-COMPASSfitToCountsTable(x, idcol = NULL, parent = NULL, drop = NULL,

-  stimName = NULL)

+COMPASSfitToCountsTable(

+  x,

+  idcol = NULL,

+  parent = NULL,

+  drop = NULL,

+  stimName = NULL

+)

 }

 \arguments{

 \item{x}{\code{COMPASSResult}}

@@ -8,8 +8,7 @@

 \usage{

 PolyfunctionalityScore(x, degree, n, markers = NULL)

-\method{PolyfunctionalityScore}{COMPASSResult}(x, degree, n,

-  markers = NULL)

+\method{PolyfunctionalityScore}{COMPASSResult}(x, degree, n, markers = NULL)

 \method{PolyfunctionalityScore}{default}(x, degree, n, markers = NULL)

 }

@@ -5,9 +5,9 @@

 \alias{Response.COMPASSResult}

 \title{Compute a response probability from COMPASS mcmc samples.}

 \usage{

-Response(x, markers, degree)

+Response(x, markers, degree, max.prob, at_least_n)

-\method{Response}{COMPASSResult}(x, markers = NULL, degree = 1)

+\method{Response}{COMPASSResult}(x, markers = NULL, degree = 1, max.prob = FALSE, at_least_n = NULL)

 }

 \arguments{

 \item{x}{a \code{COMPASSResult} object.}

@@ -15,6 +15,10 @@ Response(x, markers, degree)

 \item{markers}{a \code{vector} of marker names.}

 \item{degree}{the \code{numeric} degree of functionality to test.}

+

+\item{max.prob}{\code{logical} Use the max probability rather than the average across subsets. Defaults to FALSE.}

+

+\item{at_least_n}{\code{logical} response of degree x or greater with at_least_n subsets responding.}

 }

 \value{

 A \code{vector} of response probabilities for each subject.

@@ -28,5 +32,6 @@ i.e., the probability of at least \code{degree} markers exhibiting an antigen sp

 The response is computed from the sampled Gamma matrix, subsetting on the selected markers, and

 }

 \examples{

+

 Response(CR, markers = c("M1","M2","M3"), degree = 2)

 }

@@ -4,8 +4,15 @@

 \alias{SimpleCOMPASS}

 \title{Fit the discrete COMPASS Model}

 \usage{

-SimpleCOMPASS(n_s, n_u, meta, individual_id, iterations = 10000,

-  replications = 8, verbose = TRUE)

+SimpleCOMPASS(

+  n_s,

+  n_u,

+  meta,

+  individual_id,

+  iterations = 10000,

+  replications = 8,

+  verbose = TRUE

+)

 }

 \arguments{

 \item{n_s}{The cell counts for stimulated cells.}

new file mode 100644

@@ -0,0 +1,46 @@

+% Generated by roxygen2: do not edit by hand

+% Please edit documentation in R/Rhat.R

+\name{checkCOMPASSConvergence}

+\alias{checkCOMPASSConvergence}

+\title{Check the convergence of multiple COMPASS models fit with different seeds.}

+\usage{

+checkCOMPASSConvergence(mlist, ncores = 1)

+}

+\arguments{

+\item{mlist}{A list of COMPASSResult models. Each should be fit to the same data, but with different seeds.}

+

+\item{ncores}{The number of cores to use, if supported on the system.}

+}

+\value{

+A list of COMPASSResult models that are consistent / have converged.

+}

+\description{

+Computes Rhat for all nsubjects x nsubsets parameters across the list of models, treated as separate chains.

+Flags cell populations and subjects with Rhat > 1.01. 

+The most frequently flagged population is passed for further diagnostis to compute Rhat between all pairs of models and

+to try and identify the model or models that are outliers.

+The outliers are removed and a list of good models is returned.

+}

+\note{

+Uses foreach and doMC, so it won't work on windows.

+}

+\examples{

+data(COMPASS)

+set.seed(100)

+fit <- COMPASS(CC,

+  category_filter=NULL,

+  treatment=trt == "Treatment",

+  control=trt == "Control",

+  verbose=FALSE,

+  iterations=100 ## set higher for a real analysis

+)

+set.seed(200)

+fit2 <- COMPASS(CC,

+  category_filter=NULL,

+  treatment=trt == "Treatment",

+  control=trt == "Control",

+  verbose=FALSE,

+  iterations=100 ## set higher for a real analysis

+)

+checkCOMPASSConvergence(list(fit,fit2))

+}

@@ -8,8 +8,14 @@

 \usage{

 melt_(data, ...)

-\method{melt_}{data.frame}(data, id.vars, measure.vars,

-  variable.name = "variable", ..., value.name = "value")

+\method{melt_}{data.frame}(

+  data,

+  id.vars,

+  measure.vars,

+  variable.name = "variable",

+  ...,

+  value.name = "value"

+)

 \method{melt_}{matrix}(data, ...)

 }

@@ -4,24 +4,50 @@

 \alias{pheatmap}

 \title{A function to draw clustered heatmaps.}

 \usage{

-pheatmap(mat, color = colorRampPalette(rev(brewer.pal(n = 7, name =

-  "RdYlBu")))(100), kmeans_k = NA, breaks = NA,

-  border_color = "grey60", cellwidth = NA, cellheight = NA,

-  scale = "none", cluster_rows = TRUE, cluster_cols = TRUE,

+pheatmap(

+  mat,

+  color = colorRampPalette(rev(brewer.pal(n = 7, name = "RdYlBu")))(100),

+  kmeans_k = NA,

+  breaks = NA,

+  border_color = "grey60",

+  cellwidth = NA,

+  cellheight = NA,

+  scale = "none",

+  cluster_rows = TRUE,

+  cluster_cols = TRUE,

   clustering_distance_rows = "euclidean",

   clustering_distance_cols = "euclidean",

-  clustering_method = "complete", treeheight_row = ifelse(cluster_rows,

-  50, 0), treeheight_col = ifelse(cluster_cols, 50, 0), legend = TRUE,

-  legend_breaks = NA, legend_labels = NA, annotation = NA,

-  annotation_colors = NA, annotation_legend = TRUE,

-  drop_levels = TRUE, show_rownames = TRUE, show_colnames = TRUE,

-  main = NA, fontsize = 10, fontsize_row = fontsize,

-  fontsize_col = fontsize, display_numbers = FALSE,

-  number_format = "\%.2f", fontsize_number = 0.8 * fontsize,

-  filename = NA, width = NA, height = NA, row_annotation = NA,

-  row_annotation_legend = TRUE, row_annotation_colors = NA,

-  cytokine_annotation = NA, headerplot = NA, polar = FALSE,

-  order_by_max_functionality = TRUE, ...)

+  clustering_method = "complete",

+  treeheight_row = ifelse(cluster_rows, 50, 0),

+  treeheight_col = ifelse(cluster_cols, 50, 0),

+  legend = TRUE,

+  legend_breaks = NA,

+  legend_labels = NA,

+  annotation = NA,

+  annotation_colors = NA,

+  annotation_legend = TRUE,

+  drop_levels = TRUE,

+  show_rownames = TRUE,

+  show_colnames = TRUE,

+  main = NA,

+  fontsize = 10,

+  fontsize_row = fontsize,

+  fontsize_col = fontsize,

+  display_numbers = FALSE,

+  number_format = "\%.2f",

+  fontsize_number = 0.8 * fontsize,

+  filename = NA,

+  width = NA,

+  height = NA,

+  row_annotation = NA,

+  row_annotation_legend = TRUE,

+  row_annotation_colors = NA,

+  cytokine_annotation = NA,

+  headerplot = NA,

+  polar = FALSE,

+  order_by_max_functionality = TRUE,

+  ...

+)

 }

 \arguments{

 \item{mat}{numeric matrix of the values to be plotted.}

@@ -5,12 +5,24 @@

 \alias{plot}

 \title{Plot a COMPASSResult}

 \usage{

-\method{plot}{COMPASSResult}(x, y, subset = NULL, threshold = 0.01,

-  minimum_dof = 1, maximum_dof = Inf, must_express = NULL,

-  row_annotation, palette = colorRampPalette(brewer.pal(10,

-  "Purples"))(20), show_rownames = FALSE, show_colnames = FALSE,

-  measure = NULL, order_by = FunctionalityScore,

-  order_by_max_functionality = TRUE, markers = NULL, ...)

+\method{plot}{COMPASSResult}(

+  x,

+  y,

+  subset = NULL,

+  threshold = 0.01,

+  minimum_dof = 1,

+  maximum_dof = Inf,

+  must_express = NULL,

+  row_annotation,

+  palette = colorRampPalette(brewer.pal(10, "Purples"))(20),

+  show_rownames = FALSE,

+  show_colnames = FALSE,

+  measure = NULL,

+  order_by = FunctionalityScore,

+  order_by_max_functionality = TRUE,

+  markers = NULL,

+  ...

+)

 }

 \arguments{

 \item{x}{An object of class \code{COMPASSResult}.}

@@ -4,9 +4,20 @@

 \alias{plot2}

 \title{Plot a pair of COMPASSResults}

 \usage{

-plot2(x, y, subset, threshold = 0.01, minimum_dof = 1,

-  maximum_dof = Inf, must_express = NULL, row_annotation = NULL,

-  palette = NA, show_rownames = FALSE, show_colnames = FALSE, ...)

+plot2(

+  x,

+  y,

+  subset,

+  threshold = 0.01,

+  minimum_dof = 1,

+  maximum_dof = Inf,

+  must_express = NULL,

+  row_annotation = NULL,

+  palette = NA,

+  show_rownames = FALSE,

+  show_colnames = FALSE,

+  ...

+)

 }

 \arguments{

 \item{x}{An object of class \code{COMPASSResult}.}

@@ -4,10 +4,17 @@

 \alias{plotCOMPASSResultStack}

 \title{Plot multiple COMPASSResults}

 \usage{

-plotCOMPASSResultStack(x, threshold = 0.01, minimum_dof = 1,

-  maximum_dof = Inf, row_annotation, variable,

+plotCOMPASSResultStack(

+  x,

+  threshold = 0.01,

+  minimum_dof = 1,

+  maximum_dof = Inf,

+  row_annotation,

+  variable,

   palette = colorRampPalette(brewer.pal(9, "Purples"))(20),

-  show_rownames = FALSE, ...)

+  show_rownames = FALSE,

+  ...

+)

 }

 \arguments{

 \item{x}{A named list of objects of class \code{COMPASSResult}. The names are values of type \code{variable}}

@@ -4,10 +4,18 @@

 \alias{shinyCOMPASS}

 \title{Start a Shiny Application for Visualizing COMPASS Results}

 \usage{

-shinyCOMPASS(x, dir = NULL, meta.vars, facet1 = "None",

-  facet2 = "None", facet3 = "None",

+shinyCOMPASS(

+  x,

+  dir = NULL,

+  meta.vars,

+  facet1 = "None",

+  facet2 = "None",

+  facet3 = "None",

   main = "Heatmap of Ag-Specificity Posterior Probabilities",

-  stimulation = NULL, launch = TRUE, ...)

+  stimulation = NULL,

+  launch = TRUE,

+  ...

+)

 }

 \arguments{

 \item{x}{An object of class \code{COMPASSResult}.}