Description Usage Arguments Details Value Author(s) References Examples

This function estimates the number of clusters in e.g., microarray data using an iterative process proposed by Asa Ben-Hur.

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`object` |
Either a matrix or |

`freq` |
The proportion of samples to use. This should be somewhere between 0.6 - 0.8 for best results. |

`upper` |
The upper limit for number of clusters. |

`seednum` |
A value to pass to |

`linkmeth` |
Linkage method to pass to |

`distmeth` |
The distance method to use. Valid values include "euclidean" and "pearson" where pearson implies 1-pearson correlation. |

`iterations` |
The number of iterations to use. The default of 100 is a reasonable number. |

This function may be used to estimate the number of true clusters that
exist in a set of microarray data. This estimate can be used to as
input for `clusterComp`

to estimate the stability of the clusters.

The primary output from this function is a set of histograms that show for each cluster size how often similar clusters are formed from subsets of the data. As the number of clusters increases, the pairwise similarity of cluster membership will decrease. The basic idea is to choose the histogram corresponding to the largest number of clusters in which the majority of the data in the histogram is concentrated at or near 1.

If overlay is set to `TRUE`

, an additional CDF plot will be
produced. This can be used in conjunction with the histograms to
determine at which cluster number the data are no longer concentrated
at or near 1.

The output from this function is an object of class `benhur`

. See
the `benhur-class`

man page for more information.

Originally written by Mark Smolkin <marksmolkin@hotmail.com> further modifications by James W. MacDonald <jmacdon@u.washington.edu>

A. Ben-Hur, A. Elisseeff and I. Guyon. A stability based method for discovering structure in clustered data. Pacific Symposium on Biocomputing, 2002. Smolkin, M. and Ghosh, D. (2003). Cluster stability scores for microarray data in cancer studies . BMC Bioinformatics 4, 36 - 42.

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```
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, sd, var, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, basename, cbind, colMeans, colSums, colnames,
dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
intersect, is.unsorted, lapply, lengths, mapply, match, mget,
order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
union, unique, unsplit, which, which.max, which.min
Welcome to Bioconductor
Vignettes contain introductory material; view with
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")', and for packages 'citation("pkgname")'.
```

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