[Bioc-sig-seq] viewApplying Efficiently

Dario Strbenac D.Strbenac at garvan.org.au
Mon Feb 14 00:05:05 CET 2011


To clarify, samplingRL is a RangesList, not samplingRL[[1]].

---- Original message ----
>Date: Mon, 14 Feb 2011 10:00:15 +1100 (EST)
>From: bioc-sig-sequencing-bounces at r-project.org (on behalf of Dario Strbenac <D.Strbenac at garvan.org.au>)
>Subject: [Bioc-sig-seq] viewApplying Efficiently  
>To: bioc-sig-sequencing at r-project.org
>
>Hello,
>
>I have an RleList of about 17000 Rles and I'd like to get the regularly spaced values out of each one of them and have a list of vectors of numbers as the result.
>
>e.g. my views locations is 17000 of these :
>
>> samplingRL[[1]] # is a RangesList
>IRanges of length 101
>      start   end width
>[1]     501   501     1
>[2]    1001  1001     1
>[3]    1501  1501     1
>[4]    2001  2001     1
>[5]    2501  2501     1
>[6]    3001  3001     1
>[7]    3501  3501     1
>[8]    4001  4001     1
>[9]    4501  4501     1
>...     ...   ...   ...
>[93]  46501 46501     1
>[94]  47001 47001     1
>[95]  47501 47501     1
>[96]  48001 48001     1
>[97]  48501 48501     1
>[98]  49001 49001     1
>[99]  49501 49501     1
>[100] 50001 50001     1
>[101] 50501 50501     1
>
>and my RleList has data like :
>
>> rleList[[1]]
>'numeric' Rle of length 51001 with 38620 runs
>  Lengths:               501                 1 ...              1089
>  Values : 0.671728853793319 0.677726432845045 ... 0.224909214439609
>
>I do the following to get the sampling position values in one step, but it uses up over 20 GB RAM in a matter of seconds, and I have to kill the process.
>
>result <- viewApply(Views(rleList, samplingRL), function(samples) as.numeric(samples), simplify = TRUE)
>
>Is there a better way ?
>
>--------------------------------------
>Dario Strbenac
>Research Assistant
>Cancer Epigenetics
>Garvan Institute of Medical Research
>Darlinghurst NSW 2010
>Australia
>
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>Bioc-sig-sequencing at r-project.org
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--------------------------------------
Dario Strbenac
Research Assistant
Cancer Epigenetics
Garvan Institute of Medical Research
Darlinghurst NSW 2010
Australia



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