[BioC] Remove samples and probes from Illumina 450K data

[James W. MacDonald]

Hi Donglei Hu,

On 7/24/2013 2:52 PM, Donglei Hu [guest] wrote:
> Hi,
>
> I have Illumina 450K data for 570 samples.  I have loaded IDAT files into R using minfi.  After I ran some QC steps, I'd like to remove sample outliers and probes with large detection P.  Is there a straightforward way to do so in minfi?  I have searched in Bioconductor mailing list but couldn't find a direct answer.  Thank you very much for the help!

All the objects in minfi are extensions of either ExpressionSet or 
SummarizedExperiment, which are both intended to 'do the right thing' 
under row selection. So all you need to do is

myminfi.object.filtered <- myoriginalminfiobject[rowsToKeep,]

And I would recommend upgrading R to 3.0.1. minfi in particular has seen 
quite a bit of development.

Best,

Jim


>
> Donglei Hu, Ph.D.
> Department of Medicine
> University of California, San Francisco
>
>   -- output of sessionInfo():
>
> R version 2.15.3 (2013-03-01)
> Platform: x86_64-pc-linux-gnu (64-bit)
>
> locale:
>   [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
>   [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
>   [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
>   [7] LC_PAPER=C                 LC_NAME=C
>   [9] LC_ADDRESS=C               LC_TELEPHONE=C
> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
>   [1] IlluminaHumanMethylation450kmanifest_0.4.0
>   [2] minfi_1.4.0
>   [3] Biostrings_2.26.3
>   [4] GenomicRanges_1.10.7
>   [5] IRanges_1.16.6
>   [6] reshape_0.8.4
>   [7] plyr_1.8
>   [8] lattice_0.20-15
>   [9] Biobase_2.18.0
> [10] BiocGenerics_0.4.0
>
> loaded via a namespace (and not attached):
>   [1] affyio_1.26.0         annotate_1.36.0       AnnotationDbi_1.20.7
>   [4] beanplot_1.1          BiocInstaller_1.8.3   bit_1.1-10
>   [7] codetools_0.2-8       crlmm_1.16.9          DBI_0.2-7
> [10] ellipse_0.3-8         ff_2.2-11             foreach_1.4.1
> [13] genefilter_1.40.0     grid_2.15.3           iterators_1.0.6
> [16] limma_3.14.4          MASS_7.3-23           Matrix_1.0-12
> [19] matrixStats_0.8.1     mclust_4.1            multtest_2.14.0
> [22] mvtnorm_0.9-9995      nor1mix_1.1-4         oligoClasses_1.20.0
> [25] parallel_2.15.3       preprocessCore_1.20.0 RColorBrewer_1.0-5
> [28] RcppEigen_0.3.1.2.1   R.methodsS3_1.4.4     RSQLite_0.11.4
> [31] siggenes_1.32.0       splines_2.15.3        stats4_2.15.3
> [34] survival_2.37-4       XML_3.98-1.1          xtable_1.7-1
> [37] zlibbioc_1.4.0
>
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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-- 
James W. MacDonald, M.S.
Biostatistician
University of Washington
Environmental and Occupational Health Sciences
4225 Roosevelt Way NE, # 100
Seattle WA 98105-6099