[BioC] How do I background correct an Illumina eset without using lumiB?

[Emma Bell [guest]]

Hello,

I'm doing some work with publicly available microarray data sets that I've downloaded from GEO. I'm having some trouble using the lumi package to process Illumina BeadArray data.

My understanding is that, normally when using the lumi package you would use lumiR to convert your data to a lumiBatch object, which you could then use lumiB on to background correct. I believe lumiR requires bead standard errors in order to create a lumiBatch object, in their absence it creates an expression set and that lumiB requires the input to be a lumiBatch object. The data sets that I've downloaded only list mean intensity values for each probe and in some cases an associated P-value. Therefore I can't turn my data into lumiBatch object and thus can't background correct with lumiB.

The data sets that I'm trying to use are:
GSE31978
GSE30670
GSE22427
GSE13674
GSE20381

I've been using lumiR as follows:

>library(lumi)
>GSEXXXXX.lumi <- lumiR("GSEXXXXX_Raw_Data.txt",lib.mapping="lumiHumanIDMapping")

I would really appreciate any suggestions on how to background correct these expression sets. Apologies if I've phrased this unhelpfully or left out important information, I'm very new to both R and asking questions to a mailing list like this.

Thanks,

Emma

 -- output of sessionInfo(): 

> sessionInfo()
R version 2.15.3 (2013-03-01)
Platform: x86_64-w64-mingw32/x64 (64-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3] LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] lumi_2.10.0        nleqslv_2.0        Biobase_2.18.0     BiocGenerics_0.4.0
[5] limma_3.14.4      

loaded via a namespace (and not attached):
 [1] affy_1.36.1           affyio_1.26.0         annotate_1.36.0      
 [4] AnnotationDbi_1.20.7  BiocInstaller_1.8.3   colorspace_1.2-2     
 [7] DBI_0.2-5             grid_2.15.3           IRanges_1.16.6       
[10] KernSmooth_2.23-8     lattice_0.20-13       MASS_7.3-23          
[13] Matrix_1.0-11         methylumi_2.4.0       mgcv_1.7-22          
[16] nlme_3.1-108          parallel_2.15.3       preprocessCore_1.20.0
[19] RSQLite_0.11.2        stats4_2.15.3         XML_3.96-1.1         
[22] xtable_1.7-1          zlibbioc_1.4.0       


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