macluster: Clustering analysis for Microarray experiment

In maanova: Tools for analyzing Micro Array experiments

Description

This function bootstraps K-means or hierarchical clusters and builds a consensus tree (consensus group for K-means) from the bootstrap result.

Usage

macluster(anovaobj, term, idx.gene, what = c("gene", "sample"), 
    method = c("hc", "kmean"), dist.method = "correlation",
    hc.method = "ward", kmean.ngroups, n.perm = 100)

Arguments

anovaobj	The result object for fitting ANOVA model.
term	The factor (in formula) used in clustering. The expression level for this term will be used in clustering. This term has to correspond to the gene list, e.g, idx.gene in this function. The gene list should be the significant hits in testing this term.
idx.gene	A vector indicating the list of differentially expressed genes. The expression level of these genes will be used to construct the cluster.
what	What to be clustered, either gene or sample.
method	The clustering method. Right now hierarchical clustering ("hc") and K-means ("kmean") are available.
dist.method	Distance measure to be used in hierarchical clustering. Besides the methods listed in dist, there is a new method "correlation" (default). The "correlation" distance equals to (1 - $r^2$), where r is the sample correlation between observations.
hc.method	The agglomeration method to be used in hierarchical clustering. See hclust for detail.
kmean.ngroups	The number of groups for K-means cluster.
n.perm	Number of bootstraps. If it is 1, this function will cluster the observed data. If it is bigger than 1, a bootstrap will be performed.

Details

Normally after the F test, user can select a list of differentially expressed genes. The next step is to investigate the relationship among these genes. Using the expression levels of these genes, the user can cluster the genes or the samples using either hierarchical or K-means clustering algorithm. In order to evaluate the stability of the relationship, this function bootstraps the data, re-fits the model and recluster the genes/samples. Then for a certain number of bootstrap iterations, say, 1000, we have 1000 cluster results. We can use consensus to build the consensus tree from these 1000 trees.

Note that if you have a large number (say, more than 100) of genes/samples to cluster, hierarchical clustering could be very unstable. A slight change in the data can result in a big change in the tree structure. In that case, K-means will give better results.

Value

An object of class macluster.

Author(s)

Hao Wu

See Also

hclust, kmeans, consensus

Examples

# load in data
data(abf1)
# fit the anova model
## Not run: 
fit.fix = fitmaanova(abf1,formula = ~Strain)
# test Strain effect 
test.fix = matest(abf1, fit.fix, term="Strain",n.perm= 1000)
# pick significant genes - pick the genes selected by Fs test
idx <- volcano(test.fix)$idx.Fs
# do k-means cluster on genes
gene.cluster <- macluster(fit.fix, term="Strain", idx, what="gene", 
   method="kmean", kmean.ngroups=5, n.perm=100)
# get the consensus group
consensus(gene.cluster, 0.5)

# HC cluster on samples
sample.cluster <- macluster(fit.fix, term="Strain", idx, what="sample",method="hc")
# get the consensus group
consensus(sample.cluster, 0.5)
## End(Not run)

Example output

Attaching package: 'maanova'

The following object is masked from 'package:base':

    norm

Warning message:
In any(parsed.formula$random) :
  coercing argument of type 'double' to logical
Doing F-test on observed data ...
Doing permutation. This may take a long time ... 
Finish permutation #  100 
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There were 50 or more warnings (use warnings() to see the first 50)
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The "ward" method has been renamed to "ward.D"; note new "ward.D2"
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maanova documentation built on Nov. 8, 2020, 8:21 p.m.