| Type: | Package |
| Title: | Tight Clustering |
| Version: | 1.1 |
| Date: | 2018-06-12 |
| Author: | George C. Tseng <ctseng@pitt.edu>, Wing H. Wong <whwong@stanford.edu> |
| Maintainer: | Chi Song <song.1188@osu.edu> |
| Depends: | R (≥ 2.10.1), base, utils, stats |
| Description: | The functions needed to perform tight clustering Algorithm. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Packaged: | 2018-06-12 20:50:14 UTC; csong |
| Repository: | CRAN |
| Date/Publication: | 2018-06-12 21:09:18 UTC |
Tight Clustering Package
Description
This package could perform tight clustering algorithm proposed by George C. Tseng and Wing H. Wong.
Details
| Package: | tightClust |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2012-08-28 |
| License: | GPL (>=2) |
Author(s)
George C. Tseng <ctseng@pitt.edu>, Wing H. Wong <whwong@stanford.edu>
Maintainer: Chi Song <chs108@pitt.edu>
References
George C. Tseng and Wing H. Wong. (2005) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16.
Plot tight cluster result
Description
A function to plot the heatmap of the tight cluster result.
Usage
## S3 method for class 'tight.clust'
plot(x, standardize.gene = TRUE, order.sample = FALSE, plot.noise=TRUE, ...)
Arguments
x |
Return value of the |
standardize.gene |
If standardize each gene vector to mean 0 and sd 1. |
order.sample |
It specifies whether to order samples (features) using the hierachical clustering method. |
plot.noise |
It specifies whether to plot the remaining noise genes (objects). |
... |
Arguments to |
Author(s)
Chi Song <chs108@pitt.edu>
References
George C. Tseng and Wing H. Wong. (2005) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16.
test data for tight clustering package
Description
Sample microarray data
Usage
data(tclust.test.data)Format
The data is a list of 3 items:
- GeneID
ID of each gene
- Annotation
Annotation information of each gene
- Data
Data matirx of gene expression: each row represent one gene; each column represent one sample
Tight Clustering
Description
This function could perform the tight clustering algorithm.
Usage
tight.clust(x, target, k.min, alpha = 0.1, beta = 0.6,
top.can = 7, seq.num = 2, resamp.num = 10,
samp.p = 0.7, nstart = 1, remain.p = 0.1,
k.stop = 5, standardize.gene=TRUE, random.seed=NULL)
Arguments
x |
Input data, should be |
target |
The total number of clusters that the user aims to find. |
k.min |
The starting point of k0. See 'Details' for more information. |
alpha |
The threshold of comembership index. Default value is suggested to be used. |
beta |
The threshold of clusters stably found in consecutive k0. Default value is suggested to be used. |
top.can |
The number of top (size) candidate clusters for a specific k0. Default value is suggested to be used. |
seq.num |
The number of subsequent k0 that finds the tight cluster. Default value is suggested to be used. |
resamp.num |
Total number of resampling to obtain comembership matrix. Default value is suggested to be used. |
samp.p |
Percentage of subsamples selected. Default value is suggested to be used. |
nstart |
Number of different random inital for K-means. Default value is suggested to be used. |
remain.p |
Stop searching when the percentage of remaining points <= |
k.stop |
Stop decreasing |
standardize.gene |
It specifies whether to standardize each gene vector to mean 0 and sd 1. Default value is suggested to be used. |
random.seed |
If |
Details
Tight clustering method is a resampling-evaluated clustering method that aims to directly identify tight clusters in a high-dimensional complex data set and allow a set of scattered objects without being clustered. The method was originally developed for gene cluster analysis in microarray data but can be applied in any complex data. The most important parameter is k.min. A large k.min results in smaller and tighter clusters. Normally k.min>=target+5 is suggested. All other parameters do not affect the quality of final clustering results too much and are suggested to remain unchanged.
Value
Returned value is a "tight.clust" object (list). The first element is the original data matrix. The second element is a vector representing the cluster identity (-1: scattered gene set; 1: the first cluster; 2: the second cluster; ...). The third element is a vector of the size of each tight cluster.
Author(s)
Chi Song <chs108@pitt.edu>
References
George C. Tseng and Wing H. Wong. (2005) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16.
Examples
## load the test dataset
data(tclust.test.data)
## find 10 tight clusters
ptm<-proc.time()
## k.min=25, tighter clusters will be found
## target=1 is used to save time, target=10 is recommended
tclust1<-tight.clust(tclust.test.data$Data, target=1, k.min=25, random.seed=12345)
proc.time()-ptm
## plot the heat map of cluster result
plot(tclust1)
## write the cluster result
write.tight.clust(tclust1)
ptm<-proc.time()
## k.min=10, looser clusters will be found
## target=1 is used to save time, target=5 is recommended
tclust2<-tight.clust(tclust.test.data$Data, target=1, k.min=10, random.seed=12345)
proc.time()-ptm
## plot the heat map of cluster result
plot(tclust2)
## write the cluster result
write.tight.clust(tclust2)
Write tight cluster result
Description
A function to print the tight cluster result to a file or connection.
Usage
write.tight.clust(x, ...)
Arguments
x |
Return value of the |
... |
Arguments to |
Author(s)
Chi Song <chs108@pitt.edu>
References
George C. Tseng and Wing H. Wong. (2005) Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data. Biometrics.61:10-16.