[BioC] spike-in normalization with NormiR

Svetlana Bojic [guest] guest at bioconductor.org
Tue Aug 5 11:13:10 CEST 2014

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:

>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 :-)


 -- output of sessionInfo(): 

R version 3.0.3 (2014-03-06)
Platform: i386-w64-mingw32/i386 (32-bit)

[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  

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