[BioC] IRanges: Need help speeding up sliding window analysis
Martin Morgan
mtmorgan at fhcrc.org
Fri Sep 24 14:56:24 CEST 2010
On 09/24/2010 03:11 AM, Michael Dondrup wrote:
> Hi,
>
> I need some help with speeding up a sliding window analysis on an Rle object of length > 1 million.
> I am using functions 'successiveViews' with negative gap width and 'viewApply' vs. viewMeans.
> My goal is to apply a discrete differential operator that computes the difference between the 'left'
> half of the window and the 'right', aka. a cheap discrete numeric first order differentiation.
>
> What I found is: viewMeans(x) << viewApply(x, mean) << viewApply(x, diff.op) in terms of time, example below.
> Is there a way to pimp this code to make it work on the genome scale? I appreciate your input, I am confident there
> is a better way to do it.
maybe
win <- 30
diff(cumsum(rle), win) / win
for numeric (not integer) rle, though there might be rounding problems
if cumsum gets large. A strategy might be to break the Rle into regions
separated by islands of at least 'win' 0's (using runLength / runValue
to identify candidate break points), which allows one to reset the
cumsum. Some inspiration might come from
http://www.mail-archive.com/r-help@r-project.org/msg75280.html.
Also the end points might need fiddling (e.g., by padding rle with 'win'
trailing zeros, which is I think in effect what successiveViews does.
Martin
>
> Thank you very much
> Michael
>
> Code example:
>
> diff.op <- function(x, lrprop=1/2) {
> len = length(x)
> i = ceiling(len*lrprop)
> (sum(x[i:len]) - sum(x[1:i])) / len
> }
>
> sliding.window.apply <- function(object, width, fun, ...) {
> x <- trim(successiveViews(subject=object, width=rep(width, ceiling(length(object)) ), gap=-width+1))
> return (Rle(viewApply(x, fun, ...)))
> }
>
> sliding.window.mean <- function(object, width) {
> x <- trim(successiveViews(subject=object, width=rep(width, ceiling(length(object)) ), gap=-width+1))
> return (viewMeans(x))
> }
>
> rle <- Rle(1:10000)
>
>> system.time(sliding.window.mean(rle, 30))
> user system elapsed
> 0.036 0.004 0.098
>> system.time(sliding.window.apply(rle, 30, mean))
> user system elapsed
> 4.380 0.065 6.010
>> system.time(sliding.window.apply(rle, 30, diff.op))
> user system elapsed
> 38.857 0.204 39.127
>
>> sessionInfo()
> R version 2.11.1 (2010-05-31)
> x86_64-apple-darwin9.8.0
>
> locale:
> [1] en_US.UTF-8/en_US.UTF-8/C/C/en_US.UTF-8/en_US.UTF-8
>
> attached base packages:
> [1] stats graphics grDevices utils datasets methods base
>
> other attached packages:
> [1] IRanges_1.6.6
>
> loaded via a namespace (and not attached):
> [1] annotate_1.26.1 AnnotationDbi_1.10.2 Biobase_2.8.0 DBI_0.2-5 DESeq_1.0.4
> [6] genefilter_1.30.0 geneplotter_1.26.0 grid_2.11.1 RColorBrewer_1.0-2 RSQLite_0.9-1
> [11] splines_2.11.1 survival_2.35-8 xtable_1.5-6
>
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