seqCluster: Program for sequentially clustering, removing cluster, and...

Description Usage Arguments Details Value References See Also Examples

View source: R/seqCluster.R


Given a data matrix, this function will call clustering routines, and sequentially remove best clusters, and iterate to find clusters.


seqCluster(x = NULL, diss = NULL, k0, clusterFunction = c("tight",
  "hierarchical01", "pam", "hierarchicalK"), subsample = TRUE, beta = 0.7,
  top.can = 15, remain.n = 30, k.min = 3, k.max = k0 + 10,
  verbose = TRUE, subsampleArgs = NULL, clusterDArgs = NULL)



p x n data matrix on which to run the clustering (samples in columns).


n x n data matrix of dissimilarities between the samples on which to run the clustering


the value of K at the first iteration of sequential algorithm, see details below or vignette.


passed to clusterDMat option 'clusterFunction' to indicate method of clustering, see clusterD.


logical as to whether to subsample via subsampleClustering to get the distance matrix at each iteration; otherwise the distance matrix is set by arguments to clusterD.


value between 0 and 1 to decide how stable clustership membership has to be before 'finding' and removing the cluster.


only the top.can clusters from clusterD (ranked by 'orderBy' argument given to clusterD) will be compared pairwise for stability. Making this very big will effectively remove this parameter and all pairwise comparisons of all clusters found will be considered. This might result in smaller clusters being found. Current default is fairly large, so probably will have little effect.


when only this number of samples are left (i.e. not yet clustered) then algorithm will stop.


each iteration of sequential detection of clustering will decrease the beginning K of subsampling, but not lower than k.min.


algorithm will stop if K in iteration is increased beyond this point.


whether the algorithm should print out information as to its progress.


list of arguments to be passed to subsampleClustering.


list of arguments to be passed to clusterD(which can include arguments to be passed to cluster01 or clusterK).


This code is adapted from the code of the tightClust package of Tseng and Wong

Each iteration of the algorithm will cluster the current set of samples. Depending on the method, the number of clusters resulting from clusterD may not be equal to the K used in the clustering of the (subsampled) data. The resulting clusters will then be compared to clusters found in the previous iteration that set the subsampling clustering to K-1. For computational (and other?) convenience, only the first top.can clusters of each iteration will be compared to the first top.can clusters of previous iteration for similarity (where top.can currently refers to ordering by size, so first top.can largest clusters).

If there is a cluster in the current iteration that has overlap similarity > beta to a cluster in the previous iteration, then the cluster with the largest such similarity will be identified as a 'final' cluster and the samples in it will be removed for future iterations. The algorithm will then continue to the next iteration, but without these samples. Furthermore, in this case K for the next iteration will NOT be set to K+1, but will be reset to kinit-1, where kinit was the first K used after the previous 'final' cluster was removed. If kinit-1<k.min, then K will be set to k.min.

If there is no cluster of the first top.can in the current iteration that has overlap similarity > beta to any in the previous iteration, then the algorithm will move to the next iteration (i.e. redo after increasing K to K+1).

If there are less than remain.n samples left after finding a cluster and removing its samples, the algorithm will stop, as subsampling is deamed to no longer be appropriate. If the K has to be increased to beyond k.max without finding any pair of clusters with overlap > beta, then the algorithm will stop. Any samples not found as part of a 'final' cluster after the algorithm stops, will be classified as unclustered (given a value of -1)

'subsample' controls what is the D (distance) matrix used for clustering at each iteration. If subsample=TRUE, D is given via subsampleClustering function with k=K (with additional arguments passed via subsampleArgs). If subsample=FALSE, D is dist(x), for the samples currently considered in the iteration and clusterFunction must be of the 'K' type (e.g. "pam", see clusterD) or an error will be produced. The nsample x nsample matrix D is then clustered via clusterD to find clusters. The option 'clusterFunction' is passed to the argument 'clusterFunction' of clusterD to control what method is used to cluster D.

If clusterFunction is of type 'K' (e.g. "pam", see clusterD) the 'k' argument of clusterK called by clusterD is set to the current iteration of K by the sequential iteration, so setting 'k=' in the list given to clusterDArgs will not do anything and will produce a warning to that effect.

Similarly, the current K of the iteration also determines the 'k' argument passed to subsampleClustering so setting 'k=' in the list given to the subsampleArgs will not do anything and will produce a warning to that effect.

If subsample=FALSE and 'findBestK=FALSE' is passed to clusterDArgs, then each iteration will run the clustering given by clusterFunction on dist(x) iterating over k. However, if subsample=FALSE, you should not set 'findBestK=TRUE' (otherwise clustering dist(x) will be essentially the same for iterating over different k and there is no method implemented to change the choice of how to remove a cluster other than similarity as you change k); an error message will be given if this combination of options are set.

However, if clusterFunction="pam" (or is of type 'K') and subsample=TRUE passing either 'findBestK=TRUE' or 'findBestK=FALSE' will function as expected. In particular, the iteration over K will set the number of clusters for clustering of each subsample. If findBestK=FALSE, that same K will be used for clustering of DMat. If findBestK=TRUE, then clusterD will search for best k; note that the default 'kRange' over which clusterD searches when findBestK=TRUE depends on the input value of 'k' (you can change this to a fixed set of values by setting 'kRange' explicitly in the clusterDArgs list).


A list with values


Tseng and Wong (2005), "Tight Clustering: A Resampling-Based Approach for Identifying Stable and Tight Patterns in Data", Biometrics, 61:10-16.

See Also



## Not run: 


clustSeqHier <- seqCluster(t(simData), k0=5, subsample=TRUE,
clusterFunction="hierarchical01", beta=0.8, subsampleArgs=list(resamp.n=100,
samp.p=0.7, clusterFunction="kmeans", clusterArgs=list(nstart=10)),

## End(Not run)

clusterExperiment documentation built on May 31, 2017, 2:45 p.m.