# 04 June 2011
# From the vignette for NMF in R

library(NMF)
library(Biobase)

data(esGolub)
esGolub

# perform 10 runs for each value of r in range 2:6, verbose on, .pbackend=2 uses 2 cores
res.multirun.100 <- nmfEstimateRank(esGolub, range=2:6, nrun=100, .opt='v' )
plot(res.multirun)

# estimate the factorization rank
# perform 10 runs for each value of r in range 2:6 
estim.r.100 <- nmfEstimateRank(esGolub, range=2:6, nrun=100, .opt='v')
plot(estim.r.100)


# In the case of multiple runs method metaHeatmap plots the consensus matrix,
# i.e. the average connectivity matrix across the runs (see results Figures 6
# and 7 for a consensus matrix obtained with 100 runs of Brunet’s algorithm on
# Golub dataset):
# The cell type is used to label rows and columns 
res.multirun.f3 <- nmfEstimateRank( esGolub, 3, nrun=100, .opt='v' )
names(res.multirun.f3)
# [1] "measures"  "consensus"
res.multirun$measures
#  rank sparseness.basis sparseness.coef         rss      evar
#1    2        0.6331268       0.6978966 71070149177 0.6795901
#2    3        0.6428015       0.7784392 58798560362 0.7349149
#3    4        0.6668170       0.6937394 51614523220 0.7673031
#4    5        0.6809725       0.6998722 46659623165 0.7896416
#5    6        0.6851090       0.7015529 43396922317 0.8043510
#  residuals niter    cpu cpu.all nrun cophenetic dispersion
#1  16272331   480  3.434  34.558   10  0.9987404  0.9640997
#2  13806547   700  6.806  51.920   10  0.9979653  0.9751801
#3  12453739   860 10.949  91.446   10  0.9961595  0.9677562
#4  11338479   750 11.274 119.114   10  0.9761625  0.8920776
#5  10434760  1450 25.715 188.580   10  0.9846909  0.9168975


metaHeatmap(res.multirun.f3, labRow=esGolub$Cell, labCol=esGolub$Cell)
#Error in function (classes, fdef, mtable)  : 
#  unable to find an inherited method for function "metaHeatmap", for signature "NMF.rank"