Hi Dario,
On 02/05/2013 05:00 PM, Dario Strbenac wrote:
Hello,
Would it be possible to include an option that firstly goes through all of the
strings and runs a sliding window along them, to find all the unique k-mers
present in the dataset ?
Finding the unique k-mers in the dataset can easily be done with:
library(Biostrings)
uniqueOligonucleotides <- function(x, width)
{
collapsed_freq <- oligonucleotideFrequency(x, width,
simplify.as="collapsed")
names(collapsed_freq)[which(collapsed_freq != 0L)]
}
This would avoid having a sparse matrix with many columns of all zero counts,
when a larger value of width is specified.
Sounds like a useful addition. Maybe we could support this thru
a 'drop' arg. When 'drop' is TRUE, it would do something like
this (building on top of uniqueOligonucleotides() and vcountPDict()):
oligonucleotideFrequency2 <- function(x, width)
{
kmers <- uniqueOligonucleotides(x, width)
pdict <- PDict(kmers)
ans <- t(vcountPDict(pdict, x))
colnames(ans) <- kmers
ans
}
Then:
> library(hgu95av2probe)
> probes <- DNAStringSet(hgu95av2probe)
> dim(freq1 <- oligonucleotideFrequency(head(probes), 5))
[1] 6 1024
> dim(freq2 <- oligonucleotideFrequency2(head(probes), 5))
[1] 6 99
> identical(freq2, freq1[ , colnames(freq2)])
[1] TRUE
> all(freq1[ , setdiff(colnames(freq1), colnames(freq2))] == 0L)
[1] TRUE
Added to my TODO list.
Thanks,
H.
--------------------------------------
Dario Strbenac
PhD Student
University of Sydney
Camperdown NSW 2050
Australia
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Hervé Pagès
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
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