[BioC] Why is *ply-ing over a GRangesList much slower than *ply-ing over an IRangesList?
Steve Lianoglou
mailinglist.honeypot at gmail.com
Wed Aug 25 04:31:43 CEST 2010
Hi,
Looping using any of the *ply (lapply, sapply, seqapply, etc.) seems
to be significantly slower when you are iterating over a GRangesList
vs. an IRangesList:
R> library(GenomicFeatures)
R> txdb <- loadFeatures(system.file("extdata", "UCSC_knownGene_sample.sqlite",
package="GenomicFeatures"))
R> xcripts <- transcriptsBy(txdb, 'gene')
R> system.time(l1 <- sapply(xcripts, length))
user system elapsed
2.298 0.003 2.302
irl <- IRangesList(lapply(xcripts, ranges))
system.time(l2 <- sapply(irl, length))
user system elapsed
0.047 0.001 0.049
R> identical(l1, l2)
[1] TRUE
I was curious if this is known/expected behavior and it's unavoidable, or .. ?
Thanks,
-steve
R> sessionInfo()
R version 2.12.0 Under development (unstable) (2010-08-21 r52791)
Platform: i386-apple-darwin10.4.0/i386 (32-bit)
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] org.Hs.eg.db_2.4.1 RSQLite_0.9-2 DBI_0.2-5
AnnotationDbi_1.11.4
[5] Biobase_2.9.0 GenomicFeatures_1.1.11 GenomicRanges_1.1.20
IRanges_1.7.21
loaded via a namespace (and not attached):
[1] BSgenome_1.17.6 Biostrings_2.17.29 RCurl_1.4-3 XML_3.1-1
biomaRt_2.5.1
[6] rtracklayer_1.9.7 tools_2.12.0
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
More information about the Bioconductor
mailing list