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 nonnull and merges those below a certain cutoff.
1 2 3 4 5 6 7 8 9 10 11  ## 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"), 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"),
labelType = c("colorblock", "name", "ids"), plot = TRUE, ...)

x 
data to perform the test on. It can be a matrix or a

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 
mergeMethod 
method for calculating proportion of nonnull 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 nonnull 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). 
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 
eraseOld 
logical. Only relevant if input 
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 
labelType 
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). 
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.
"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 pvalues (method "BH") and a cutoff of 0.05.
If mergeMethod
is not equal to 'none' then the plotting will
indicate where the clusters will be merged (assuming plotInfo
is not
'none'). Note setting both 'mergeMethod' and 'plotInfo' to 'none' will
cause function to stop, because nothing is asked to be done. If you just
want plot of the dendrogram, with no merging performed or 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.
If 'x' is a matrix, it returns (invisibly) a list with elements
clustering
a vector of length equal to ncol(x)
giving the integervalued 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.
propDE
A table of
the proportions that are DE on each node.
originalClusterDendro
The dendrogram on which the merging
was based (based on the original clustering).
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.
Ji and Cai (2007), "Estimating the Null and the Proportion of Nonnull Effects in LargeScale Multiple Comparisons", JASA 102: 495906.
Efron (2004) <e2><80><9c>Largescale simultaneous hypothesis testing: the choice of a null hypothesis,<e2><80><9d> JASA, 99: 96<e2><80><93>104.
Meinshausen and Buhlmann (2005) "Lower bounds for the number of false null hypotheses for multiple testing of associations", Biometrika 92(4): 893907.
Storey (2002) "A direct approach to false discovery rates", J. R. Statist. Soc. B 64 (3)": 479<e2><80><93>498.
Pounds and Cheng (2004). "Improving false discovery rate estimation." Bioinformatics 20(11): 17371745.
makeDendrogram, plotDendrogram, getBestFeatures
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25  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",label="name")
#compare merged to original
table(primaryCluster(cl), primaryCluster(merged))

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