Hi All, I have an AffyBatch object generated with createAB() function from ExiMiR package, and when I try to do the spike-in normalization, as described in vignette, I get following message: The intensity resolution of the spike-in probe sets is too coarse (8.56 > 1) to guarantee a good performance of spike-in normalization Using median normalization... and normalization method switches to "median". My question is: how can I "force" the execution of spikein normalization (however inappropriate/suboptimal it my be for my data)? Which particular parameter in normalize.param list should I modify (and how) to get any form of spike-in normalization, since I need it for illustration purposes only...? Here is the code I've used: >library(limma) >library(ExiMiR) >targets <- readTargets() >MiljRNA <- read.maimages(targets, source="agilent", green.only=TRUE) >MiljRNA.batch <- createAB(MiljRNA) >spikein.set <- grep("^spike", featureNames(MiljRNA.batch), value=TRUE) >MiljRNA.spike <- NormiR(MiljRNA.batch, background.correct=FALSE, normalize.method="spikein", normalize.param=list(probeset.list=spikein.set), summary.method="medianpolish", verbose=TRUE) Maybe I should add that intensity distributions of last 4 spikein probesets are very similar in shape, while others (6 more) show no common pattern... still using only subset of those 4 spikein probesets didn't get me anywhere... And, if it of any use, my data came from miRCURY LNAmicroRNA Array v.11 (Exiqon A/S, Vedbaek, Denmark) chip , as processed with Agilent FE software. Any suggestion would be highly appreciated :-) Best Svetlana -- output of sessionInfo(): sessionInfo() R version 3.0.3 (2014-03-06) Platform: i386-w64-mingw32/i386 (32-bit) 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] parallel stats graphics grDevices utils [6] datasets methods base other attached packages: [1] ExiMiR_2.4.0 affycoretools_1.34.0 [3] KEGG.db_2.10.1 GO.db_2.10.1 [5] RSQLite_0.11.4 DBI_0.2-7 [7] AnnotationDbi_1.24.0 preprocessCore_1.24.0 [9] limma_3.18.13 vsn_3.30.0 [11] affy_1.40.0 GenomicRanges_1.14.4 [13] XVector_0.2.0 GEOquery_2.28.0 [15] Biobase_2.22.0 IRanges_1.20.7 [17] BiocGenerics_0.8.0 loaded via a namespace (and not attached): [1] affyio_1.30.0 annaffy_1.34.0 [3] annotate_1.40.1 AnnotationForge_1.4.4 [5] BiocInstaller_1.12.1 biomaRt_2.18.0 [7] Biostrings_2.30.1 biovizBase_1.10.8 [9] bit_1.1-12 bitops_1.0-6 [11] BSgenome_1.30.0 Category_2.28.0 [13] caTools_1.17 cluster_1.15.2 [15] codetools_0.2-8 colorspace_1.2-4 [17] DESeq2_1.2.10 dichromat_2.0-0 [19] digest_0.6.4 edgeR_3.4.2 [21] ff_2.2-13 foreach_1.4.2 [23] Formula_1.1-1 gcrma_2.34.0 [25] gdata_2.13.3 genefilter_1.44.0 [27] GenomicFeatures_1.14.5 ggbio_1.10.16 [29] ggplot2_0.9.3.1 GOstats_2.28.0 [31] gplots_2.13.0 graph_1.40.1 [33] grid_3.0.3 gridExtra_0.9.1 [35] GSEABase_1.24.0 gtable_0.1.2 [37] gtools_3.4.0 Hmisc_3.14-4 [39] hwriter_1.3 iterators_1.0.7 [41] KernSmooth_2.23-12 lattice_0.20-29 [43] latticeExtra_0.6-26 locfit_1.5-9.1 [45] MASS_7.3-33 Matrix_1.1-3 [47] MmPalateMiRNA_1.12.0 munsell_0.4.2 [49] oligoClasses_1.24.0 PFAM.db_2.10.1 [51] plyr_1.8.1 pROC_1.7.2 [53] proto_0.3-10 R.methodsS3_1.6.1 [55] R.oo_1.18.0 R.utils_1.32.4 [57] R2HTML_2.2.1 RBGL_1.38.0 [59] RColorBrewer_1.0-5 Rcpp_0.11.1 [61] RcppArmadillo_0.4.320.0 RCurl_1.95-4.1 [63] ReportingTools_2.2.0 reshape2_1.4 [65] Rsamtools_1.14.3 rtracklayer_1.22.7 [67] scales_0.2.4 splines_3.0.3 [69] stats4_3.0.3 stringr_0.6.2 [71] survival_2.37-7 tools_3.0.3 [73] VariantAnnotation_1.8.13 XML_3.98-1.1 [75] xtable_1.7-3 zlibbioc_1.8.0 -- Sent via the guest posting facility at bioconductor.org.

Hi Svetlana, This is all covered in the help page for the NormiR function. From the Description section: "By default, it applies the spike-in probe-based method for the second step of normalization. In case the spike-in probe-based method cannot be applied, a median normalization is executed instead. Several options allow however to force the execution of the spike-in probe-based normalization and to fine-tune the resulting correction functions." And if you then look at the values you can pass under the 'normalize.param' list argument, you will see: "max.log2span Numeric. Default value is 1. Gives the maximal (log2) intensity interval allowed for the probes belonging to one spike-in probeset." Which I believe directly applies to your situation. Best, Jim James W. MacDonald, M.S. Biostatistician University of Washington Environmental and Occupational Health Sciences 4225 Roosevelt Way NE, # 100 Seattle WA 98105-6099 On Tue, Aug 5, 2014 at 5:13 AM, Svetlana Bojic [guest] < guest@bioconductor.org> wrote: > Hi All, > > I have an AffyBatch object generated with createAB() function from ExiMiR > package, and when I try to do the spike-in normalization, as described in > vignette, I get following message: > > The intensity resolution of the spike-in probe sets is too coarse (8.56 > > 1) to guarantee a good performance of spike-in normalization > Using median normalization... > > and normalization method switches to "median". > > My question is: how can I "force" the execution of spikein normalization > (however inappropriate/suboptimal it my be for my data)? Which particular > parameter in normalize.param list should I modify (and how) to get any form > of spike-in normalization, since I need it for illustration purposes > only...? > > Here is the code I've used: > >library(limma) > >library(ExiMiR) > > >targets <- readTargets() > >MiljRNA <- read.maimages(targets, source="agilent", green.only=TRUE) > >MiljRNA.batch <- createAB(MiljRNA) > >spikein.set <- grep("^spike", featureNames(MiljRNA.batch), value=TRUE) > >MiljRNA.spike <- NormiR(MiljRNA.batch, background.correct=FALSE, > normalize.method="spikein", > normalize.param=list(probeset.list=spikein.set), > summary.method="medianpolish", verbose=TRUE) > > > Maybe I should add that intensity distributions of last 4 spikein > probesets are very similar in shape, while others (6 more) show no common > pattern... still using only subset of those 4 spikein probesets didn't get > me anywhere... > > > And, if it of any use, my data came from miRCURY LNAmicroRNA Array v.11 > (Exiqon A/S, Vedbaek, Denmark) chip , as processed with Agilent FE software. > > > Any suggestion would be highly appreciated :-) > > Best > Svetlana > > > -- output of sessionInfo(): > > sessionInfo() > R version 3.0.3 (2014-03-06) > Platform: i386-w64-mingw32/i386 (32-bit) > > 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] parallel stats graphics grDevices utils > [6] datasets methods base > > other attached packages: > [1] ExiMiR_2.4.0 affycoretools_1.34.0 > [3] KEGG.db_2.10.1 GO.db_2.10.1 > [5] RSQLite_0.11.4 DBI_0.2-7 > [7] AnnotationDbi_1.24.0 preprocessCore_1.24.0 > [9] limma_3.18.13 vsn_3.30.0 > [11] affy_1.40.0 GenomicRanges_1.14.4 > [13] XVector_0.2.0 GEOquery_2.28.0 > [15] Biobase_2.22.0 IRanges_1.20.7 > [17] BiocGenerics_0.8.0 > > loaded via a namespace (and not attached): > [1] affyio_1.30.0 annaffy_1.34.0 > [3] annotate_1.40.1 AnnotationForge_1.4.4 > [5] BiocInstaller_1.12.1 biomaRt_2.18.0 > [7] Biostrings_2.30.1 biovizBase_1.10.8 > [9] bit_1.1-12 bitops_1.0-6 > [11] BSgenome_1.30.0 Category_2.28.0 > [13] caTools_1.17 cluster_1.15.2 > [15] codetools_0.2-8 colorspace_1.2-4 > [17] DESeq2_1.2.10 dichromat_2.0-0 > [19] digest_0.6.4 edgeR_3.4.2 > [21] ff_2.2-13 foreach_1.4.2 > [23] Formula_1.1-1 gcrma_2.34.0 > [25] gdata_2.13.3 genefilter_1.44.0 > [27] GenomicFeatures_1.14.5 ggbio_1.10.16 > [29] ggplot2_0.9.3.1 GOstats_2.28.0 > [31] gplots_2.13.0 graph_1.40.1 > [33] grid_3.0.3 gridExtra_0.9.1 > [35] GSEABase_1.24.0 gtable_0.1.2 > [37] gtools_3.4.0 Hmisc_3.14-4 > [39] hwriter_1.3 iterators_1.0.7 > [41] KernSmooth_2.23-12 lattice_0.20-29 > [43] latticeExtra_0.6-26 locfit_1.5-9.1 > [45] MASS_7.3-33 Matrix_1.1-3 > [47] MmPalateMiRNA_1.12.0 munsell_0.4.2 > [49] oligoClasses_1.24.0 PFAM.db_2.10.1 > [51] plyr_1.8.1 pROC_1.7.2 > [53] proto_0.3-10 R.methodsS3_1.6.1 > [55] R.oo_1.18.0 R.utils_1.32.4 > [57] R2HTML_2.2.1 RBGL_1.38.0 > [59] RColorBrewer_1.0-5 Rcpp_0.11.1 > [61] RcppArmadillo_0.4.320.0 RCurl_1.95-4.1 > [63] ReportingTools_2.2.0 reshape2_1.4 > [65] Rsamtools_1.14.3 rtracklayer_1.22.7 > [67] scales_0.2.4 splines_3.0.3 > [69] stats4_3.0.3 stringr_0.6.2 > [71] survival_2.37-7 tools_3.0.3 > [73] VariantAnnotation_1.8.13 XML_3.98-1.1 > [75] xtable_1.7-3 zlibbioc_1.8.0 > > -- > Sent via the guest posting facility at bioconductor.org. > > _______________________________________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]