Hi
I'm stupid, I've forgotten the "1:".
>> system.time({
> + cvg2 <- GenomeData( list() )
> + for( i in length(lanes) )
^^^^^^^^^^^^^
> + cvg2 <- cvg2 + coverage( lanes[[i]], width=seqlens )
> + })
> user system elapsed
> 56.551 9.050 65.634
No wonder that the for loop was so drastically faster; it was iterated
only once.
Here are the correct timings:
> system.time({
+ cvg2 <- GenomeData( list() )
+ for( i in seq_along(lanes) )
+ cvg2 <- cvg2 + coverage( lanes[[i]], width=seqlens )
+ })
user system elapsed
378.704 102.946 482.406
> system.time(
+ cvg1 <- Reduce( `+`, lapply( lanes, coverage, width=seqlens ) )
+ )
user system elapsed
377.510 101.509 484.885
So, there is no performance difference between 'Reduce' and 'for'. Sorry
for the nonsense.
Simon
_______________________________________________
Bioc-sig-sequencing mailing list
[email protected]
https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing
