generate.seeds | R Documentation |
Generate random input seeds for the ISA.
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 non-zero 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 Gabor.Csardi@unil.ch
Bergmann S, Ihmels J, Barkai N: Iterative signature algorithm for the analysis of large-scale 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):370-7. Epub 2002 Jul 22
Ihmels J, Bergmann S, Barkai N: Defining transcription modules using large-scale gene expression data Bioinformatics 2004 Sep 1;20(13):1993-2003. Epub 2004 Mar 25.
isa2-package for a short introduction on the Iterative
Signature Algorithm. See isa
for an easy way of running
ISA.
## 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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