mergeClusters: Merge clusters based on dendrogram

Description Usage Arguments Details Value References See Also Examples

Description

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.

Usage

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## S4 method for signature 'matrix'
mergeClusters(x, cl, dendro = NULL,
  mergeMethod = c("none", "Storey", "PC", "adjP", "locfdr", "MB", "JC"),
  plotInfo = c("none", "all", "Storey", "PC", "adjP", "locfdr", "MB", "JC",
  "mergeMethod"), nodePropTable = NULL, calculateAll = TRUE,
  showWarnings = FALSE, cutoff = 0.1, plot = TRUE, isCount = TRUE, ...)

## S4 method for signature 'ClusterExperiment'
mergeClusters(x, eraseOld = FALSE,
  isCount = FALSE, mergeMethod = "none", plotInfo = "all",
  clusterLabel = "mergeClusters", leafType = c("samples", "clusters"),
  plotType = c("colorblock", "name", "ids"), plot = TRUE, ...)

## 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'
getMergeCorrespond(x, by = c("merge",
  "original"))

Arguments

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 merging is plotted at each node. If 'none', then no proportions will be added to the dendrogram. 'plotInfo' can also be one of the mergeMethod choices (even if that method is not the method chosen in 'mergeMethod' options).

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

isCount

logical as to whether input data is a count matrix. See details.

...

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 combineMany). 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).

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.

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.

Count correction If isCount=TRUE, and the input is a matrix, log2(count + 1) will be used for makeDendrogram and the original data with voom correction will be used in getBestFeatures). If input is ClusterExperiment, then setting isCount=TRUE also means that the log2(1+count) will be used as the transformation, like for the matrix case as well as the voom calculation, and will NOT use the transformation stored in the object. If FALSE, then transform(x) will be given to the input and will be used for both makeDendrogram and getBestFeatures, with no voom correction.

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.

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.

Value

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

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.

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

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.

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".

References

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.

See Also

makeDendrogram, plotDendrogram, getBestFeatures

Examples

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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)

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

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

clusterExperiment documentation built on Nov. 17, 2017, 8:35 a.m.