rename to removeClusters

NAMESPACE

@@ -84,6 +84,7 @@ exportMethods(primaryCluster)
 exportMethods(primaryClusterIndex)
 exportMethods(primaryClusterNamed)
 exportMethods(removeClusterings)
+exportMethods(removeClusters)
 exportMethods(removeUnclustered)
 exportMethods(requiredArgs)
 exportMethods(setToCurrent)
@@ -93,7 +94,6 @@ exportMethods(subsampleClustering)
 exportMethods(tableClusters)
 exportMethods(transformData)
 exportMethods(transformation)
-exportMethods(unassignSamples)
 exportMethods(workflowClusterDetails)
 exportMethods(workflowClusterTable)
 exportMethods(workflowClusters)

NEWS

@@ -2,10 +2,10 @@ Changes in version 1.5.0-9012 ( Release date: )
 ==============
 
 Changes:
-* This version consists of a major update of how dimensionality reduction and filtering is done. The class has been updated to extend the new `SingleCellExperiment` class, which save the dimensionality reductions. Furthermore we extend that class to also save filtering statistics (`SingleCellFilter`). This has created a massive change under-the-hood in functions that allow dimensionality reduction and filtering. Changes to function names:
+* MAJOR CHANGE TO DEFINITION OF CLASS: This version consists of a major update of how dimensionality reduction and filtering is done. The class has been updated to extend the new `SingleCellExperiment` class, which save the dimensionality reductions. Furthermore, calculating of per-gene statistics, which are usually used for filtering, are stored in `colData` of the object and can be easily accessed and used for repeated filtering without recalculating. This has created a massive change under-the-hood in functions that allow dimensionality reduction and filtering. Changes to function names are the following:
 	- `transform` is now `transformData`
 	- New functions `makeReducedDims` and `makeFilterStats` will calculate (and thus store) dimensionality reductions and statistics for filtering the data.
-	- New function `filterData` will return a `SingleCellFilter` object with the data filtered based on a filtering criteria
+	- New function `filterData` will return the filtered data as a matrix
 	- New functions `listBuiltInReducedDims` and `listBuiltInFilterStats` give the list of currently available functions for dimensionality reduction and filtering statistics, respectively.
 	- Filtering on arbitrary statistics and user-defined dimensionality reduction can used in `clusterMany` and related functions, so long as they are saved in the appropriate slots of the object.
 * Changed the following functions/arguments to be consistent with SingleCellExperiment naming conventions and improve distinction between terminology of cluster and clustering.
@@ -16,14 +16,24 @@ Changes:
 	- `ndims` to `nDims` in `clusterSingle` and `makeDendrogram` to keep consistency.
 	- `plotDimReduce` to `plotReducedDims`
 	- Changed `nClusters` to `nClusterings` to better indicate purpose of function. `nClusters` now gives the number of clusters per clustering.
-	- `addClusters` to `addClusterings` and `removeClusters` to `removeClusterings`.
+	- `addClusters` to `addClusterings` and `removeClusters` to `removeClusterings`. New function `removeClusters` allows the user to actually ``remove" a cluster or clusters from a clustering by assigning samples in those clusters to `-1` value.
 	- `clusterInfo()` to `clusteringInfo()`
-* `getClusterManyParams` now uses saved `clusterInfo` rather than `clusterLabels` to get parameters. The resulting output is formatted somewhat differently.
-* removed `transformation` as a required argument to `ClusterExperiment`. Now sets with default of `function(x){x}`.
-* Adjusted color definitions of `seqPal2` and `seqPal4` to be completely symmetric.
-* Argument `existingColors` in `plotClustersWorkflow` now takes arguments `ignore`,`all`,`highlightOnly` similar to `plotClusters`
-* Argumnet `nodeColors` now available in `plotDendrogram`
-* Argument `contrastColors` now available in plotContrastHeatmap. Genes are now ordered by fold-change within each contrast.
+* In addition these structural changes, the following updates are also included in this release
+	- `getClusterManyParams` now uses saved `clusterInfo` rather than `clusterLabels` to get parameters. The resulting output is formatted somewhat differently.
+	- removed `transformation` as a required argument to `ClusterExperiment`. Now sets with default of `function(x){x}`.
+	- Adjusted color definitions of `seqPal2` and `seqPal4` to be completely symmetric around center.
+	- Argument `existingColors` in `plotClustersWorkflow` now takes arguments `ignore`,`all`,`highlightOnly` similar to `plotClusters`
+	- Argumnet `nodeColors` now available in `plotDendrogram`
+	- Argument `contrastColors` now available in plotContrastHeatmap. Genes are now ordered by fold-change within each contrast.
+	- New function `plotClusterLegend` that will plot a legend for a clustering.
+	- `plotClusters` argument `existingColors` now allows for the option `firstOnly` 
+	- makeDendrogram now allows option 'coCluster' to the argument `dimReduce` indicating use of the coClustering matrix to build the dendrogram.
+	- makeDendrogram now has a method for building a dendrogram from an arbitrary distance function
+	- `showBigPalette` has been replaced with `showPalette` and now can show any palette of colors.
+	- Changed defaults in `plotDendrogram` so default is to do colorblock of samples.
+	- The colors in `bigPalette` have been changed and shuffled to reduce similar colors and `massivePalette` has been created by adding the non-grey colors from `colors()` so that `plotClusters` will not run out of colors.
+	- `clusterMatrix` function now returns cluster matrix with rownames corresponding to sample names.
+	- `convertClusterLegend` now takes argument `whichClusters`
 
 Bugs:
 * converted automatic assignment of colors in `clusterLegend` to be based on `massivePalette` so won't run out on toy examples.
@@ -32,21 +42,6 @@ Bugs:
 	- will plot annotation labels when there is `NA` in the annotation
 	- no longer calls internal function `NMF:::vplayout` in making those labels, more robust
 * fixed bug in how `plotClustersWorkflow` handled existing colors.
-
-Changes in version 1.5.0-9002 ( Release date: )
-==============
-Changes:
-* New function `plotClusterLegend` that will plot a legend for a clustering.
-* `plotClusters` argument `existingColors` now allows for the option `firstOnly` 
-* makeDendrogram now allows option 'coCluster' to the argument `dimReduce` indicating use of the coClustering matrix to build the dendrogram.
-* makeDendrogram now has a method for building a dendrogram from an arbitrary distance function
-* `showBigPalette` has been replaced with `showPalette` and now can show any palette of colors.
-* Changed defaults in `plotDendrogram` so is colorblock of samples.
-* The colors in `bigPalette` have been changed and shuffled to reduce similar colors and `massivePalette` has been created by adding the non-grey colors from `colors()` so that `plotClusters` will not run out of colors.
-* `clusterMatrix` function now returns cluster matrix with rownames corresponding to sample names.
-* `convertClusterLegend` now takes argument `whichClusters`
-
-Bugs:
 * Fixed so `diss` now passed to subsampling in calls to clusterSingle/clusterMany
 * Fixed so `plotClusters` now will not give incomprehensible error if given duplicates of a color
 * Fixed `plotDendrogram` so will not create blank plot.

R/AllGenerics.R

@@ -320,8 +320,8 @@ setGeneric(
 )
 
 
-setGeneric( name = "unassignSamples", def = function(x, whichClusters,...) {
-    standardGeneric("unassignSamples")})
+setGeneric( name = "removeClusters", def = function(x, whichClusters,...) {
+    standardGeneric("removeClusters")})
 setGeneric( name = "addClusterings", def = function(x, y,...) {
     standardGeneric("addClusterings")})
 setGeneric(name = "removeClusterings", def = function(x, whichClusters,...) {

R/addClusters.R

@@ -166,13 +166,13 @@ setMethod(
 )
 
 
-#' @details \code{unassignSamples} unassigns samples in \code{clustersToRemove} and assigns them to -1 (unassigned) 
+#' @details \code{removeClusters} unassigns samples in \code{clustersToRemove} and assigns them to -1 (unassigned) 
 #' @param clustersToRemove numeric vector identifying the clusters to remove (whose samples will be reassigned to -1 value).
 #' @rdname addClusterings
-#' @aliases unassignSamples
+#' @aliases removeClusters
 #' @export
 setMethod(
-  f = "unassignSamples",
+  f = "removeClusters",
   signature = c("ClusterExperiment","numeric"),
   definition = function(x,whichClusters,clustersToRemove) {
 	  if(length(whichClusters)!=1) stop("whichClusters should identify a single clustering.")
@@ -190,10 +190,10 @@ setMethod(
 #' @rdname addClusterings
 #' @export
 setMethod(
-  f = "unassignSamples",
+  f = "removeClusters",
   signature = signature("ClusterExperiment","character"),
   definition = function(x, whichClusters,...) {
 	  whichClusters<-.TypeIntoIndices(x,whichClusters)
-	  unassignSamples(x,whichClusters,...)
+	  removeClusters(x,whichClusters,...)
   }
 )