mergeClusters: Merge clusters based on dendrogram
In clusterExperiment: Compare Clusterings for Single-Cell Sequencing

Description Usage Arguments Details Value References See Also Examples

Takes an input of hierarchical clusterings of clusters and returns estimates of number of proportion of non-null and merges those below a certain cutoff.

## S4 method for signature 'matrixOrHDF5'
mergeClusters(
  x,
  cl,
  dendro = NULL,
  mergeMethod = c("none", "Storey", "PC", "adjP", "locfdr", "MB", "JC"),
  plotInfo = "none",
  nodePropTable = NULL,
  calculateAll = TRUE,
  showWarnings = FALSE,
  cutoff = 0.05,
  plot = TRUE,
  DEMethod,
  logFCcutoff = 0,
  weights = NULL,
  ...
)

## S4 method for signature 'ClusterExperiment'
mergeClusters(
  x,
  eraseOld = FALSE,
  mergeMethod = "none",
  plotInfo = "all",
  clusterLabel = "mergeClusters",
  leafType = c("samples", "clusters"),
  plotType = c("colorblock", "name", "ids"),
  plot = TRUE,
  whichAssay = 1,
  forceCalculate = FALSE,
  weights = if ("weights" %in% assayNames(x)) "weights" else NULL,
  DEMethod,
  ...
)

## S4 method for signature 'ClusterExperiment'
nodeMergeInfo(x)

## S4 method for signature 'ClusterExperiment'
mergeCutoff(x)

## S4 method for signature 'ClusterExperiment'
mergeMethod(x)

## S4 method for signature 'ClusterExperiment'
mergeClusterIndex(x)

## S4 method for signature 'ClusterExperiment'
eraseMergeInfo(x)

## S4 method for signature 'ClusterExperiment'
getMergeCorrespond(x, by = c("merge", "original"))

`x`	data to perform the test on. It can be a matrix or a `ClusterExperiment`.
`cl`	A numeric vector with cluster assignments to compare to. “-1” indicates the sample was not assigned to a cluster.
`dendro`	dendrogram providing hierarchical clustering of clusters in cl. If x is a matrix, then the default is `dendro=NULL` and the function will calculate the dendrogram with the given (x, cl) pair using `makeDendrogram`. If x is a `ClusterExperiment` object, the dendrogram in the slot `dendro_clusters` will be used. In this case, this means that `makeDendrogram` needs to be called before `mergeClusters`.
`mergeMethod`	method for calculating proportion of non-null that will be used to merge clusters (if 'none', no merging will be done). See details for description of methods.
`plotInfo`	what type of information about the merging will be shown on the dendrogram. If 'all', then all the estimates of proportion non-null will be plotted at each node of the dendrogram; if 'mergeMethod', then only the value used in the `mergeClusters` command is plotted at each node. If 'none', then no proportions will be added to the dendrogram, though the dendrogram will be drawn. 'plotInfo' can also be one of the valid input to `mergeMethod` (even if that method is not the method chosen in `mergeMethod` argument). `plotInfo` can also show the information corresponding to "adjP" with a fold-change cutoff, by giving a value to this argument in the form of "adjP_2.0", for example.
`nodePropTable`	Only for matrix version. Matrix of results from previous run of `mergeClusters` as returned by matrix version of `mergeClusters`. Useful if just want to change the cutoff. Not generally intended for user but used internally by package.
`calculateAll`	logical. Whether to calculate the estimates for all methods. This reduces computation costs for any future calls to `mergeClusters` since the results can be passed to future calls of `mergeClusters` (and for `ClusterExperiment` objects this is done automatically).
`showWarnings`	logical. Whether to show warnings given by the methods. The 'locfdr' method in particular frequently spits out warnings (which may indicate that its estimates are not reliable). Setting `showWarnings=FALSE` will suppress all warnings from all methods (not just "locfdr"). By default this is set to `showWarnings=FALSE` by default to avoid large number of warnings being produced by "locfdr", but users may want to be more careful to check the warnings for themselves.
`cutoff`	minimimum value required for NOT merging a cluster, i.e. two clusters with the proportion of DE below cutoff will be merged. Must be a value between 0, 1, where lower values will make it harder to merge clusters.
`plot`	logical as to whether to plot the dendrogram with the merge results
`DEMethod`	character vector describing how the differential expression analysis should be performed that will be used in the estimation of the percentage DE per node. See getBestFeatures for current options. See details.
`logFCcutoff`	Relevant only if the `mergeMethod` selected is "adjP", in which case the calculation of the proportion of individual tests significant will also require that the estimated log-fold change of the features to be at least this large in absolute value. Value will be rounded to nearest tenth of an integer via `round(logFCcutoff,digits=1)`. For any other method, this parameter is ignored. Note that the logFC is based on `log2` (the results of `getBestFeatures`)
`weights`	weights to use in by edgeR. If `x` is a matrix, then weights should be a matrix of weights, of the same dimensions as `x`. If `x` is a `ClusterExperiment` object `weights` can be a either a matrix, as previously described, or a character or numeric index to an assay in `x` that contains the weights. We recommend that weights be stored as an assay with name `"weights"` so that the weights will also be used with `mergeClusters`, and this is the default. Setting `weights=NULL` ensures that weights will NOT be used, and only the standard edgeR.
`...`	for signature `matrix`, arguments passed to the `plot.phylo` function of `ape` that plots the dendrogram. For signature `ClusterExperiment` arguments passed to the method for signature `matrix` and then if do not match those arguments, will be passed onto `plot.phylo`.
`eraseOld`	logical. Only relevant if input `x` is of class `ClusterExperiment`. If TRUE, will erase existing workflow results (clusterMany as well as mergeClusters and makeConsensus). If FALSE, existing workflow results will have "`_i`" added to the clusterTypes value, where `i` is one more than the largest such existing workflow clusterTypes.
`clusterLabel`	a string used to describe the type of clustering. By default it is equal to "mergeClusters", to indicate that this clustering is the result of a call to mergeClusters (only if x is a ClusterExperiment object)
`leafType`	if plotting, whether the leaves should be the clusters or the samples. Choosing 'samples' allows for visualization of how many samples are in the merged clusters (only if x is a ClusterExperiment object), which is the main difference between choosing "clusters" and "samples", particularly if `plotType="colorblock"`
`plotType`	if plotting, then whether leaves of dendrogram should be labeled by rectangular blocks of color ("colorblock") or with the names of the leaves ("name") (only if x is a ClusterExperiment object).
`whichAssay`	numeric or character specifying which assay to use. See `assay` for details.
`forceCalculate`	This forces the function to erase previously saved merge results and recalculate the merging.
`by`	indicates whether output from `getMergeCorrespond` should be a vector/list with elements corresponding to merge cluster ids or elements corresponding to the original clustering ids. See return value for details.

Estimation of proportion non-null "Storey" refers to the method of Storey (2002). "PC" refers to the method of Pounds and Cheng (2004). "JC" refers to the method of Ji and Cai (2007), and implementation of "JC" method is copied from code available on Jiashin Ji's website, December 16, 2015 (http://www.stat.cmu.edu/~jiashun/Research/software/NullandProp/). "locfdr" refers to the method of Efron (2004) and is implemented in the package locfdr. "MB" refers to the method of Meinshausen and Buhlmann (2005) and is implemented in the package howmany. "adjP" refers to the proportion of genes that are found significant based on a FDR adjusted p-values (method "BH") and a cutoff of 0.05.

Control of Plotting If mergeMethod is not equal to 'none' then the plotting will indicate where the clusters will be merged by making dotted lines of edges that are merged together (assuming plotInfo is not 'none'). plotInfo controls simultaneously what information will be plotted on the nodes as well as whether the dotted lines will be shown for the merged cluster. Notice that the choice of plotInfo (as long as it is not 'none') has no effect on how the dotted edges are drawn – they are always drawn based on the mergeMethod. If you choose plotInfo to not be equal to the mergeMethod, then you will have a confusing picture where the dotted edges will be based on the clustering created by mergeMethod while the information on the nodes is based on a different method. Note that you can override plotInfo by setting show.node.label=FALSE (passed to plot.phylo), so that no information is plotted on the nodes, but the dotted edges are still drawn. If you just want plot of the dendrogram, with no merging performed nor demonstrated on the plot, see plotDendrogram.

Saving and Reusing of results By default, the function saves the results in the ClusterExperiment object and will not recalculate them if not needed. Note that by default calculateAll=TRUE, which means that regardless of the value of mergeMethod, all the methods will be calculated so that those results will be stored and if you change the mergeMethod, no additional calculations are needed. Since the computationally intensive step is the running the DE method on the genes, this is a big savings (all of the methods then calculate the proportion from those results). However, note that if calculateAll=TRUE and ANY of the methods returned NA for any value, the calculation will be redone. Thus if, for example, the locfdr function does not run successfully and returns NA, the function will always recalculate each time, even if you don't specifically want the results of locfdr. In this case, it makes sense to turn calculateAll=FALSE.

If the dendrogram was made with option unassignedSamples="cluster" (i.e. unassigned were clustered in with other samples), then you cannot choose the option leafType='samples'. This is because the current code cannot reliably link up the internal nodes of the sample dendrogram to the internal nodes of the cluster dendrogram when the unassigned samples are intermixed.

When the input is a ClusterExperiment object, the function attempts to update the merge information in that object. This is done by checking that the existing dendrogram stored in the object (and run on the clustering stored in the slot dendro_index) is the same clustering that is stored in the slot merge_dendrocluster_index. For this reason, new calls to makeDendrogram will erase the merge information saved in the object.

If mergeClusters is run with mergeMethod="none", the function may still calculate the proportions per node if plotInfo is not equal to "none" or calculateAll=TRUE. If the input object was a ClusterExperiment object, the resulting information will be still saved, though no new clustering was created; if there was not an existing merge method, the slot merge_dendrocluster_index will be updated.

If 'x' is a matrix, it returns (invisibly) a list with elements

clustering a vector of length equal to ncol(x) giving the integer-valued cluster ids for each sample. "-1" indicates the sample was not clustered.
oldClToNew A table of the old cluster labels to the new cluster labels.
nodeProp A table of the proportions that are DE on each node.This table is saved in the merge_nodeProp slot of a ClusterExperiment object and can be accessed along with the nodeMerge info with the nodeMergeInfo function.
nodeMerge A table of indicating for each node whether merged or not and the cluster id in the new clustering that corresponds to the node. Note that a node can be merged and not correspond to a node in the new clustering, if its ancestor node is also merged. But there must be some node that corresponds to a new cluster id if merging has been done. This table is saved in the merge_nodeMerge slot of a ClusterExperiment object and can be accessed along with the nodeProp info with the nodeMergeInfo function.
updatedClusterDendro The dendrogram on which the merging was based (based on the original clustering).
cutoff The cutoff value for merging.

If 'x' is a ClusterExperiment, it returns a new ClusterExperiment object with an additional clustering based on the merging. This becomes the new primary clustering. Note that even if mergeMethod="none", the returned object will erase any old merge information, update the work flow numbering, and return the newly calculated merge information.

nodeMergeInfo returns information collected about the nodes during merging as a data.frame with the following entries:

Node Name of the node
ContrastThe contrast compared at each node, in terms of the cluster ids
isMerged Logical as to whether samples from that node which were merged into one cluster during merging
mergeClusterId If a node corresponds to a new, merged cluster, gives the cluster id it corresponds to. Otherwise NA
...The remaining columns give the estimated proportion of genes differentially expressed for each method. A column of NAs means that the method in question hasn't been calculated yet.

mergeCutoff returns the cutoff used for the current merging.

mergeMethod returns the method used for the current merge.

mergeClusterIndex returns the index of the clustering used for the current merge.

eraseMergeInfo returns object with all previously saved merge info removed.

getMergeCorrespond returns the correspondence between the merged cluster and its originating cluster. If by="original" returns a named vector, where the names of the vector are the cluster ids of the originating cluster and the values of the vector are the cluster ids of the merged cluster. If by="merge" the results returned are organized by the merged clusters. This will generally be a list, with the names of the list equal to the clusterIds of the merge clusters and the entries the clusterIds of the originating clusters. However, if there was no merging done (so that the clusters are identical) the output will be a vector like with by="original".

Ji and Cai (2007), "Estimating the Null and the Proportion of Nonnull Effects in Large-Scale Multiple Comparisons", JASA 102: 495-906.

Efron (2004) "Large-scale simultaneous hypothesis testing: the choice of a null hypothesis," JASA, 99: 96-104.

Meinshausen and Buhlmann (2005) "Lower bounds for the number of false null hypotheses for multiple testing of associations", Biometrika 92(4): 893-907.

Storey (2002) "A direct approach to false discovery rates", J. R. Statist. Soc. B 64 (3)": 479-498.

Pounds and Cheng (2004). "Improving false discovery rate estimation." Bioinformatics 20(11): 1737-1745.

makeDendrogram, plotDendrogram, getBestFeatures

data(simData)

#create a clustering, for 8 clusters (truth was 3)
cl<-clusterSingle(simData, subsample=FALSE,
sequential=FALSE, mainClusterArgs=list(clusterFunction="pam", clusterArgs=list(k=8)))

#give more interesting names to clusters:
newNames<- paste("Cluster",clusterLegend(cl)[[1]][,"name"],sep="")
clusterLegend(cl)[[1]][,"name"]<-newNames
#make dendrogram
cl <- makeDendrogram(cl)

#plot showing the before and after clustering
#(Note argument 'use.edge.length' can improve
#readability)
merged <- mergeClusters(cl, plotInfo="all",
mergeMethod="adjP", use.edge.length=FALSE, DEMethod="limma")

#Simpler plot with just dendrogram and single method
merged <- mergeClusters(cl, plotInfo="mergeMethod",
mergeMethod="adjP", use.edge.length=FALSE, DEMethod="limma",
leafType="clusters",plotType="name")

#compare merged to original
tableClusters(merged,whichClusters=c("mergeClusters","clusterSingle"))