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 -- Sent via the guest posting facility at bioconductor.org.

Hi Emma By default, lumiB estimates the background level based on the negative control probes if they were included in the controlData slot. Because the beads of each probe are randomly distributed over the chip surface, the spatial effects are ignored during the background adjustment. By default, the lumiB function basically makes a global shift of probe intensity to adjust the background. So if no negative control probe information is available (I guess that's your case), you can visually check the density plot of the probe intensity. If the background level (the first mode) is not very high, you can directly normalize the data. Without doing background adjustment will shrink the fold-change estimation in the low intensity range, this is similar as what VST does. Pan On Wed, May 22, 2013 at 3:08 AM, Emma Bell [guest] <guest@bioconductor.org>wrote: > > 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 > > > -- > Sent via the guest posting facility at bioconductor.org. > [[alternative HTML version deleted]]