consensus {maanova} | R Documentation |
This is the function to build the consensus tree from the bootstrap clustering analysis. If the clustering algorithm is hierarchical clustering, the majority rule consensus tree will be built based on the given significance level. If the clustering algorithm is K-means, a consensus K-means group will be built.
consensus(macluster, level = 0.8, draw=TRUE)
macluster |
An object of class macluster , which is the
output of macluster |
level |
The significance level for the consensus tree. This is a numeric number between 0.5 and 1. |
draw |
A logical value to indicate whether to draw the consensus tree on screen or not. |
An object of class consensus.hc
or consensus.kmean
according to the clustering method.
Hao Wu
# load data data(abf1) ## Not run: # fit the anova model 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)$idx.Fs # do k-means cluster on genes gene.cluster <- macluster(fit.fix, term="Strain", idx, what="gene", method="kmean", kmean.ngroups=5, n.perm=100) # get the consensus group genegroup = consensus(gene.cluster, 0.5) # get the gene names belonging to each group genegroupname = genegroup$groupname # 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)