partcoef {Mfuzz} | R Documentation |
This function calculates partition coefficient for clusters within a range of cluster parameters. It can be used to determine the parameters which lead to uniform clustering.
partcoef(eset,crange=seq(4,32,4),mrange=seq(1.05,2,0.1),...)
eset |
object of class “ExpressionSet”. |
crange |
range of number of clusters c . |
mrange |
range of clustering paramter m . |
... |
additional arguments for underlying mfuzz . |
Introduced by Bezdek (1981), the partition coefficient F is defined as the sum of squares of values of the partition matrix divided by the number of values. It is maximal if the partition is hard and reaches a minimum for U=1/c when every gene is equally assigned to every cluster.
It is well-known that the partition coefficient tends to decrease monotonically with increasing n. To reduce this tendency we defined a normalized partition coefficient where the partition for uniform partitions are subtracted from the actual partition coefficients (Futschik and Kasabov,2002).
The function generates the matrix of partition coefficients for
a range of c
and m
values. It also produces a matrix of normalised
partition coefficients as well as a matrix with partition coefficient for uniform partitions.
Matthias E. Futschik (http://itb.biologie.hu-berlin.de/~futschik)
if (interactive()){ data(yeast) # Data pre-processing yeastF <- filter.NA(yeast) yeastF <- fill.NA(yeastF) yeastF <- standardise(yeastF) #### parameter selection yeastFR <- randomise(yeastF) cl <- mfuzz(yeastFR,c=20,m=1.1) mfuzz.plot(yeastFR,cl=cl,mfrow=c(4,5)) # shows cluster structures (non-uniform partition) tmp <- partcoef(yeastFR) # This might take some time. F <- tmp[[1]];F.n <- tmp[[2]];F.min <- tmp[[3]] # Which clustering parameters result in a uniform partition? F > 1.01 * F.min cl <- mfuzz(yeastFR,c=20,m=1.25) # produces uniform partion mfuzz.plot(yeastFR,cl=cl,mfrow=c(4,5)) # uniform coloring of temporal profiles indicates uniform partition }