spike-in normalization with NormiR
1
0
Entering edit mode
Guest User ★ 13k
@guest-user-4897
Last seen 9.6 years ago
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.
Normalization GO probe Normalization GO probe • 1.1k views
ADD COMMENT
0
Entering edit mode
@james-w-macdonald-5106
Last seen 35 minutes ago
United States
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]]
ADD COMMENT

Login before adding your answer.

Traffic: 748 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6