I am updating my analysis pipeline for Affymetrix arrays, and would like to use the library oligo for the analysis of all types/generations of arrays. Until now I used the functionality of the library affy to normalize the 1st generation of arrays (those arrays including MM probes), and usually I do this by the GC-RMA procedure. However, upon checking the documentation, I could not find whether GC-RMA normalization is possible with oligo. Any hints would be appreciated! Thanks, Guido

Example code using some 430a arrays from GEO (because this one of the few 'old' arrays for which a PdInfo library is availaible at BioC):
Source: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE20282


# Install required PdInfo package
> BiocManager::install("pd.moe430a")

# preferred way using library Oligo:
> library(oligo)

# Read in data 
> affy.data <- read.celfiles(filenames = list.celfiles(), pkgname = "pd.moe430a")
Loading required package: pd.moe430a
Loading required package: RSQLite
Loading required package: DBI
Platform design info loaded.
Reading in : GSM508528.CEL
Reading in : GSM508529.CEL
Reading in : GSM508530.CEL
Reading in : GSM508531.CEL
Reading in : GSM508532.CEL
Reading in : GSM508533.CEL
>

# RMA normalization works fine...:
> norm.data <- oligo::rma(affy.data)
Background correcting
Normalizing
Calculating Expression
> 

# ... also through an alternative route
> norm.data2 <- oligo::fitProbeLevelModel(affy.data)
Background correcting... OK
Normalizing... OK
Summarizing... OK
Extracting...
  Estimates... OK
  StdErrors... OK
  Weights..... OK
  Residuals... OK
  Scale....... OK
>
## convert oligoPLM object to ExpressionSet!
>  norm.data2 <- opset2eset(norm.data2)
>
#check
> norm.data
ExpressionSet (storageMode: lockedEnvironment)
assayData: 22690 features, 6 samples 
  element names: exprs 
protocolData
  rowNames: GSM508528.CEL GSM508529.CEL ... GSM508533.CEL (6 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  rowNames: GSM508528.CEL GSM508529.CEL ... GSM508533.CEL (6 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: pd.moe430a 
> 
>
> norm.data2
ExpressionSet (storageMode: lockedEnvironment)
assayData: 22690 features, 6 samples 
  element names: exprs, se.exprs 
protocolData
  sampleNames: GSM508528.CEL GSM508529.CEL ... GSM508533.CEL (6 total)
  varLabels: exprs dates
  varMetadata: labelDescription channel
phenoData
  sampleNames: GSM508528.CEL GSM508529.CEL ... GSM508533.CEL (6 total)
  varLabels: index
  varMetadata: labelDescription channel
featureData: none
experimentData: use 'experimentData(object)'
Annotation: pd.moe430a 
>
>
# ... but how to perform GC-RMA normalization...??


sessionInfo()
R version 3.5.1 Patched (2018-11-24 r75665)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

Matrix products: default

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

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] pd.moe430a_3.12.0   DBI_1.0.0           RSQLite_2.1.1      
 [4] oligo_1.46.0        Biostrings_2.50.2   XVector_0.22.0     
 [7] IRanges_2.16.0      S4Vectors_0.20.1    Biobase_2.42.0     
[10] oligoClasses_1.44.0 BiocGenerics_0.28.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0                  compiler_3.5.1             
 [3] BiocManager_1.30.4          GenomeInfoDb_1.18.1        
 [5] bitops_1.0-6                iterators_1.0.10           
 [7] tools_3.5.1                 zlibbioc_1.28.0            
 [9] digest_0.6.18               bit_1.1-14                 
[11] memoise_1.1.0               preprocessCore_1.44.0      
[13] lattice_0.20-38             ff_2.2-14                  
[15] pkgconfig_2.0.2             Matrix_1.2-15              
[17] foreach_1.4.4               DelayedArray_0.8.0         
[19] GenomeInfoDbData_1.2.0      affxparser_1.54.0          
[21] bit64_0.9-7                 grid_3.5.1                 
[23] BiocParallel_1.16.5         blob_1.1.1                 
[25] codetools_0.2-16            matrixStats_0.54.0         
[27] GenomicRanges_1.34.0        splines_3.5.1              
[29] SummarizedExperiment_1.12.0 RCurl_1.95-4.11            
[31] affyio_1.52.0              
>

The short answer is no, and any arrays for which GCRMA is reasonably possible can be processed using the gcrma package, so I don't see it ever happening.