[BioC] Should AgiMicroRna and GeneSpring quantile normalisation be the same ?

[Paul Geeleher]

Quantiles normalization is an incredibly simple algorithm, so I'm not
sure why there should be differences. Perhaps one platform has ignored
some of the probes (i.e. background probes or control probes)?
Although this might not be documented. I would say though, that if the
across sample correlations for each gene are very high (i.e. > .99)
for most of the genes, then you probably don't have anything to worry
about.

Paul.

On Sat, Aug 11, 2012 at 11:24 PM, Seyit Ali KAYIS <skayis at selcuk.edu.tr> wrote:
> Dear All,
>
> I have microRNA microaaray expression data from Agilent platform exported by
> the Agilent Feature Extraction (AFE) image analysis software. Initially, we
> performed quantile normalisation using GeneSpring (version 12.1) software
> (demo version). Being an R user, I wanted to do rest of the analysis by
> using R. So, I repeated quantile normalisation using "AgiMicroRna" library.
> Before further analysis, I compared raw ( gTotalProbeSignal(raw) from
> GeneSpring and ddTGS$TGS from AgiMicroRNA) and quantile normalised (
> gTotalProbeSignal(normalized)  from GeneSpring and ddNORM$TGS from
> AgiMicroRNA ) output of two softwares. Raw output of two softwares are the
> same. However, there are some differences in the some of the quantile
> normalised output, although majority of the output are very similar. I
> suppose GeneSpring and  "AgiMicroRna" are using the same algorithm and they
> should produce the same results. I was wondering whether I am doing
> something wrong during the procees? Does any one faced similar situation?
>
> My sessionInfo() and steps I am following in AgiMicroRna are below.
>
> Any comment, help deeply appreciated.
>
> Kind Regards
>
> Seyit Ali
>
> ============================================================================
> ==============
>
>> sessionInfo()
> R version 2.15.1 (2012-06-22)
> Platform: i386-pc-mingw32/i386 (32-bit)
>
> locale:
> [1] LC_COLLATE=English_Australia.1252  LC_CTYPE=English_Australia.1252
> LC_MONETARY=English_Australia.1252
> [4] LC_NUMERIC=C                       LC_TIME=English_Australia.1252
>
> attached base packages:
> [1] stats     graphics  grDevices utils     datasets  methods   base
>
> other attached packages:
>  [1] AgiMicroRna_2.6.0     affycoretools_1.28.0  KEGG.db_2.7.1
> GO.db_2.7.1           RSQLite_0.11.1
>  [6] DBI_0.2-5             AnnotationDbi_1.18.1  preprocessCore_1.18.0
> affy_1.34.0           limma_3.12.1
> [11] Biobase_2.16.0        BiocGenerics_0.2.0
>
> loaded via a namespace (and not attached):
>  [1] affyio_1.24.0       annaffy_1.28.0      annotate_1.34.1
> BiocInstaller_1.4.7 biomaRt_2.12.0      Biostrings_2.24.1
>  [7] Category_2.22.0     gcrma_2.28.0        genefilter_1.38.0
> GOstats_2.22.0      graph_1.34.0        grid_2.15.1
> [13] GSEABase_1.18.0     IRanges_1.14.4      lattice_0.20-6      RBGL_1.32.1
> RCurl_1.91-1.1      splines_2.15.1
> [19] stats4_2.15.1       survival_2.36-14    tools_2.15.1        XML_3.9-4.1
> xtable_1.7-0        zlibbioc_1.2.0
>
> ============================================================================
> ============
>
> "AgiMicroRna" steps
>
>
> library(AgiMicroRna)
>
> sessionInfo()
>
> AFE.TGS = TRUE
>
> half=  FALSE  # ddTGS signal with 'half method'
> offset=0
> makePLOT=FALSE
>
>
> # NORMALIZATION of ddTGS
> NORMmethod="quantile"
>  makePLOTpre=TRUE
>  makePLOTpost=TRUE
>
> # FILTERING PROBES
> control = TRUE
> IsGeneDetected = TRUE
> wellaboveNEG = FALSE
> limIsGeneDetected = 50
> limNEG = 25
> makePLOT = FALSE
>
> #  READING THE Target File
> targets=readTargets(infile="targets.txt",verbose=TRUE)
> # READING THE DATA (RGList)
> dd=readMicroRnaAFE(targets,verbose=TRUE)
> names(dd)
>
>
> # PRE-PROCESSING # USING AFE gTotalGeneSignal  &  # NORMALIZATION
> #   tgsMicroRna: creates an uRNAList object that contains the Total Gene
> Signal computed
> #   by the Agilent Feature Extraction algorithms
> #  tgsNormalization: creates an uRNAList object containing the Normalized
> Total
> #   Gene Signal in log 2 scale
>
> if(AFE.TGS) {
> message('pre-processing: AFE TGS')
>       cat('\n')
> ddTGS = tgsMicroRna(dd, half = FALSE, makePLOT = FALSE,verbose = FALSE)
> ddNORM = tgsNormalization(ddTGS, "quantile", makePLOTpre = FALSE,
> makePLOTpost = FALSE, targets, verbose = TRUE)
> }
>
> #  Obtaining raw data
>
> TGSTGS<-cbind(ddTGS$genes, ddTGS$TGS)
>
> # Obtaining quantile normalised data
>
> NormData<-cbind(ddNORM$genes, ddNORM$TGS)
>
> ============================================================================
> =====
>
>
>
> --------------------------------------------------------
> Dr. Seyit Ali KAYIS
> Selcuk University, Faculty of Agriculture
> Kampus/Konya, Turkey
>
> skayis at selcuk.edu.tr, s_a_kayis at yahoo.com, s_a_kayis at hotmail.com
>
> Tel: +90 332 223 2830 Mobile: +90 535 587 1139
>
> Greetings from Konya, Turkey
> http://www.ziraat.selcuk.edu.tr/skayis/
> --------------------------------------------------------
>
>
>
>         [[alternative HTML version deleted]]
>
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-- 
Paul Geeleher (PhD Student)
School of Mathematics, Statistics and Applied Mathematics
National University of Ireland
Galway
Ireland
--
www.bioinformaticstutorials.com