| 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 in data
data(paigen)
# make data object with rep 2
paigen <- createData(paigen.raw, 2)
# make interactive model
model.int.fix <- makeModel(data=paigen,
formula=~Dye+Array+Strain+Diet+Strain:Diet)
# fit ANOVA model
anova.int <- fitmaanova(paigen, model.int.fix)
# test interaction effect
## Not run: test.int.fix <- matest(paigen, model.int.fix, term="Strain:Diet", n.perm=100)
# pick significant genes - pick the genes selected by Fs test
idx <- volcano(test.int.fix)$idx.Fs
# do k-means cluster on genes
gene.cluster <- macluster(anova.int, "Strain:Diet", idx, "gene",
"kmean", kmean.ngroups=5)
# get the consensus group
consensus(gene.cluster, 0.5)
# HC cluster on samples
sample.cluster <- macluster(anova.int, "Strain:Diet", idx, "sample","hc")
# get the consensus group
consensus(sample.cluster, 0.5)## End(Not run)