[BioC] Single sample normalization of single-channel Agilent microarrays

[Ryan]

Well, if you have a large training set, one option is to use frmaTools 
to generate a fRMA normalization for your dataset. Then you can use 
this normalization on the individual samples in the test/validation set.

http://bioconductor.org/packages/release/bioc/html/frmaTools.html

Also, I know there was another similar method for freezing 
normalization and other parameters based on a training set, but I can't 
remember the name of it at all, so I can't find it on Google.

On Tue Aug 26 09:42:45 2014, Gabriele Zoppoli [guest] wrote:
> Dear BioConductor community,
>
> when faced with the concept of generating a microarray-based classifier for a clinical condition (say responder vs non-responder to a treatment), I have issues understaing how, after a model is built from a training set, it can be applied prospectively in a serial way in a prospective trial. It is my understanding that most normalization methods depend, at some point, on the information derived from the microarray batch which a given sample is normalized with. Few methods circumvent this issue, such as fRMA (in case one has the possibility to use Affy HGU133 Plus 2.0 arrays) or SCAN.UPC, which would be suitable for most Affy arrays and even dual-channel Agilent arrays. What about single-channel Agilent arrays? And which were the methods used in all the works published before those methods were published? Thanks in advance, I hope this is not too general a question
>
>   -- output of sessionInfo():
>
> R version 3.1.0 (2014-04-10)
> Platform: x86_64-pc-linux-gnu (64-bit)
>
> locale:
>   [1] LC_CTYPE=en_US.UTF-8          LC_NUMERIC=C                  LC_TIME=de_BE.UTF-8           LC_COLLATE=en_US.UTF-8        LC_MONETARY=de_BE.UTF-8
>   [6] LC_MESSAGES=en_US.UTF-8       LC_PAPER=de_BE.UTF-8          LC_NAME=de_BE.UTF-8           LC_ADDRESS=de_BE.UTF-8        LC_TELEPHONE=de_BE.UTF-8
> [11] LC_MEASUREMENT=de_BE.UTF-8    LC_IDENTIFICATION=de_BE.UTF-8
>
> attached base packages:
> [1] parallel  splines   stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
>   [1] frma_1.16.0          SCAN.UPC_2.6.3       sva_3.10.0           mgcv_1.8-1           nlme_3.1-117         corpcor_1.6.6        foreach_1.4.2
>   [8] affyio_1.32.0        affy_1.42.3          GEOquery_2.30.1      oligo_1.28.2         Biostrings_2.32.1    XVector_0.4.0        IRanges_1.22.9
> [15] oligoClasses_1.26.0  Biobase_2.24.0       BiocGenerics_0.10.0  BiocInstaller_1.14.2 xlsx_0.5.5           xlsxjars_0.6.0       rJava_0.9-6
> [22] ggplot2_1.0.0        aod_1.3              survcomp_1.14.0      prodlim_1.4.3        survival_2.37-7      limma_3.20.8
>
> loaded via a namespace (and not attached):
>   [1] affxparser_1.36.0     bit_1.1-12            bootstrap_2014.4      codetools_0.2-8       colorspace_1.2-4      DBI_0.2-7             digest_0.6.4
>   [8] ff_2.2-13             GenomeInfoDb_1.0.2    GenomicRanges_1.16.3  grid_3.1.0            gtable_0.1.2          iterators_1.0.7       KernSmooth_2.23-12
> [15] lattice_0.20-29       lava_1.2.6            MASS_7.3-33           Matrix_1.1-4          munsell_0.4.2         plyr_1.8.1            preprocessCore_1.26.1
> [22] proto_0.3-10          Rcpp_0.11.2           RCurl_1.95-4.1        reshape2_1.4          rmeta_2.16            scales_0.2.4          stats4_3.1.0
> [29] stringr_0.6.2         SuppDists_1.1-9.1     survivalROC_1.0.3     tools_3.1.0           XML_3.98-1.1          zlibbioc_1.10.0
>
> --
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>
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