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Dear list, I am pre-processing Affymetrix Mouse Gene 1.0 ST Arrays and use the oligo package. I do not want to quantile normalize them all together, because my samples come from different polysome fractions or compartments of the cell, and therefore show consistent and biologically meaningful differences in signal distribution. For seperate probe summary by median polish, however, the groups are too small: The smallest group has only 3 microarrays, which leads to identical values within many probe sets across the three samples. My idea is to perform quantile normalization for the individual groups, but probe summary for all microarrays (30) together, to have a more reliable estimate of the probe effect and to avoid that I lose the variability of my samples when a group consists of only 3 microarrays. Is this reasonable, or is anyone aware of artifacts that I would introduce by performing median polish for probe summary on microarrays that have not been quantile normalized together? Here is some code to illustrate what I am doing: # I load the required packages: library("oligo") library("pd.mogene.1.0.st.v1") # the CEL files are opened twice, once in groups (here only group 1 as an example), and once all together: list_cel <- list.celfiles("group1") group1 <- read.celfiles(list_cel) list_cel <- list.celfiles("all_groups") all_groups <- read.celfiles(list_cel) # I perform background correction and quantile normalization for the pm values of the individual groups (here only group1): pms_group1 <- pm(group1) bg_group1 <- backgroundCorrect(pms_group1) norm_group1 <- normalize(bg_group1) # I replace the pm values in the GeneFeatureSet all_groups by the normalized values of group 1: exprs(all_groups)[pmindex(all_groups), 1] <- norm_group1[,1] exprs(all_groups)[pmindex(all_groups), 2] <- norm_group1[,2] exprs(all_groups)[pmindex(all_groups), 3] <- norm_group1[,3] # after having done this for ALL the groups, I perform only the probe summary on all_groups: pp_all <- rma(all_groups, background = F, normalize = F, target = "core") I guess that fRMA together with fRMAtools would be an alternative for pre-processing my microarrays in small groups? Thank you very much in advance for warning me if my idea is wrong! Johanna Schott -- output of sessionInfo(): R version 2.15.1 (2012-06-22) Platform: x86_64-pc-mingw32/x64 (64-bit) locale:  LC_COLLATE=German_Germany.1252 LC_CTYPE=German_Germany.1252 LC_MONETARY=German_Germany.1252 LC_NUMERIC=C LC_TIME=German_Germany.1252 attached base packages:  stats graphics grDevices utils datasets methods base other attached packages:  mogene10sttranscriptcluster.db_8.0.1 org.Mm.eg.db_2.7.1 AnnotationDbi_1.18.1 Biobase_2.16.0  BiocGenerics_0.2.0 pd.mogene.1.0.st.v1_3.6.0 RSQLite_0.11.1 DBI_0.2-5  oligo_1.20.4 oligoClasses_1.18.0 loaded via a namespace (and not attached):  affxparser_1.28.1 affyio_1.24.0 BiocInstaller_1.4.7 Biostrings_2.24.1 bit_1.1-8 codetools_0.2-8 ff_2.2-7 foreach_1.4.0  IRanges_1.14.4 iterators_1.0.6 preprocessCore_1.18.0 splines_2.15.1 stats4_2.15.1 tools_2.15.1 zlibbioc_1.2.0 -- Sent via the guest posting facility at bioconductor.org.