Description Usage Arguments Details Value Author(s) References See Also Examples
Generate random input seeds for the ISA.
1  generate.seeds (length, count = 100, method = c("uni"), sparsity=2)

length 
The length of the seeds, should be the number of rows in your input data for row seeds and the number of columns for column seeds. 
count 
The number of seeds to gnerate. 
method 
The method for generating the seeds. Currently only

sparsity 
A numeric scalar, an integer number giving the number of nonzero values in each seed vector. It will be recycled to have the same length as the number of seeds. 
This function can generate a 0/1 matrix whose columns are the seeds of
the ISA. The result can be use as the row.seeds
(or
col.seeds
) argument of the isa.iterate
function.
A numeric matrix with 0/1 values.
Gabor Csardi [email protected]
Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of largescale gene expression data Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1):031902. Epub 2003 Mar 11.
Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N: Revealing modular organization in the yeast transcriptional network Nat Genet. 2002 Aug;31(4):3707. Epub 2002 Jul 22
Ihmels J, Bergmann S, Barkai N: Defining transcription modules using largescale gene expression data Bioinformatics 2004 Sep 1;20(13):19932003. Epub 2004 Mar 25.
isa2package for a short introduction on the Iterative
Signature Algorithm. See isa
for an easy way of running
ISA.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  ## Just to get always the same result
set.seed(24)
## Create some random seeds with different sparseness
data < isa.in.silico()
sparsity < rep( c(1,5,25,125), length=100)
row.seeds < generate.seeds(length=nrow(data[[1]]), count=100,
sparsity=sparsity)
## Do ISA with the seeds
normed.data < isa.normalize(data[[1]])
isaresult < isa.iterate(normed.data, thr.row=1, thr.col=1,
row.seeds=row.seeds)
## Add the sparsity to the seed data
isaresult$seeddata$sparsity < sparsity
## Check which ones leed to higher robustness scores
rob < robustness(normed.data, isaresult$rows, isaresult$columns)
tapply(rob, sparsity, mean)
## About the same
## How many unique modules did we find for the different sparsity
isaresult.unique < isa.unique(normed.data, isaresult)
tapply(seq_len(ncol(isaresult.unique$rows)),
isaresult.unique$seeddata$sparsity, length)
## We usually find more modules with sparser seeds

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