Question: Should AgiMicroRna and GeneSpring quantile normalisation be the same ?
0
gravatar for Seyit Ali KAYIS
7.0 years ago by
Seyit Ali KAYIS30 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@selcuk.edu.tr, s_a_kayis@yahoo.com, s_a_kayis@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]]
ADD COMMENTlink modified 7.0 years ago by Paul Geeleher1.3k • written 7.0 years ago by Seyit Ali KAYIS30
Answer: Should AgiMicroRna and GeneSpring quantile normalisation be the same ?
0
gravatar for Paul Geeleher
7.0 years ago by
Paul Geeleher1.3k
Paul Geeleher1.3k wrote:
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]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor -- Paul Geeleher (PhD Student) School of Mathematics, Statistics and Applied Mathematics National University of Ireland Galway Ireland -- www.bioinformaticstutorials.com
ADD COMMENTlink written 7.0 years ago by Paul Geeleher1.3k
Hi, Paul, Thanks for the comments (BTW: Still waiting for comments, from AgiMicroRNA-GeneSpring users, if any one had similar experience and would like to share their solutions etc.). Meanwhile, I just checked correlation between quantile normalization results of GeneSpring and AgiMicroRna and wanted to share the results with you and others. Attached is the graph for correlation coefficients. 235 miRNA were expressed at least in one individual. Out of 235, 180 miRNA had a good correlation (between GeneSpring and AgiMicroRna quantile normalization results r>0/99). 10 miRNA had a correlation <0.90. Kind Regards Seyit Ali On 12/08/2012 2:12 PM, Paul Geeleher wrote: > 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]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > -- ---------------------------------------------------------------------- -- Dr. Seyit Ali KAYIS Selcuk University, Faculty of Agriculture Kampus/Konya, Turkey 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/ ---------------------------------------------------------------------- --
ADD REPLYlink written 7.0 years ago by Seyit Ali KAYIS30
Hi again, (I realized that attachment is not allowed. So I have copied my email and summarized the result in the table below Sorry for duplication). Thanks for the comments (BTW: Still waiting for comments, from AgiMicroRNA-GeneSpring users, if any one had similar experience and would like to share their solutions etc.). Meanwhile, I just checked correlation between quantile normalization results of GeneSpring and AgiMicroRna and wanted to share the results with you and others. 235 miRNA were expressed at least in one individual. Correlation N <0.5 3 0.5-0.9 6 0.9-0.989 46 0.99-1 179 1 1 Kind Regards Seyit Ali On 12/08/2012 2:12 PM, Paul Geeleher wrote: > 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]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > -- ---------------------------------------------------------------------- -- Dr. Seyit Ali KAYIS Selcuk University, Faculty of Agriculture Kampus/Konya, Turkey 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/
ADD REPLYlink written 7.0 years ago by Seyit Ali KAYIS30
On 08/13/2012 06:24 AM, Seyit Ali KAYIS wrote: > Hi again, > > (I realized that attachment is not allowed. So I have copied my email > and summarized the result in the table below Sorry for duplication). see http://bioconductor.org/help/mailing-list/posting-guide/ and The following attachment types are accepted: png, pdf, rda/Rdata. Total message size cannot exceed 1MB. If larger attachments are essential, post them to a publicly accessible location and include the link in your email. Also I'm cc'ing the packageDescription("AgiMicroRna")$Maintainer, as they are perhaps in the best position to provide specific answers. It sounds like you are on the way to identifying specific examples where the algorithms differ, which is the right direction for identifying simple reproducible examples that others can comment on. Martin > > Thanks for the comments (BTW: Still waiting for comments, from > AgiMicroRNA-GeneSpring users, if any one had similar experience and > would like to share their solutions etc.). > > Meanwhile, I just checked correlation between quantile normalization > results of GeneSpring and AgiMicroRna and wanted to share the results > with you and others. 235 miRNA were expressed at least in one individual. > > > Correlation N > <0.5 3 > 0.5-0.9 6 > 0.9-0.989 46 > 0.99-1 179 > 1 1 > > > Kind Regards > > Seyit Ali > > > > > On 12/08/2012 2:12 PM, Paul Geeleher wrote: >> Quantiles normalization is an incredibly simple algorithm, so I'm not >> sure why there should be differences. Perhaps one platform has ignored >> some of the probes (i.e. background probes or control probes)? >> Although this might not be documented. I would say though, that if the >> across sample correlations for each gene are very high (i.e. > .99) >> for most of the genes, then you probably don't have anything to worry >> about. >> >> Paul. >> >> On Sat, Aug 11, 2012 at 11:24 PM, Seyit Ali KAYIS >> <skayis at="" selcuk.edu.tr=""> wrote: >>> Dear All, >>> >>> I have microRNA microaaray expression data from Agilent platform >>> exported by >>> the Agilent Feature Extraction (AFE) image analysis software. >>> Initially, we >>> performed quantile normalisation using GeneSpring (version 12.1) >>> software >>> (demo version). Being an R user, I wanted to do rest of the analysis by >>> using R. So, I repeated quantile normalisation using "AgiMicroRna" >>> library. >>> Before further analysis, I compared raw ( gTotalProbeSignal(raw) from >>> GeneSpring and ddTGS$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]] >>> >>> _______________________________________________ >>> Bioconductor mailing list >>> Bioconductor at r-project.org >>> https://stat.ethz.ch/mailman/listinfo/bioconductor >>> Search the archives: >>> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> > > -- Computational Biology / Fred Hutchinson Cancer Research Center 1100 Fairview Ave. N. PO Box 19024 Seattle, WA 98109 Location: Arnold Building M1 B861 Phone: (206) 667-2793
ADD REPLYlink written 7.0 years ago by Martin Morgan ♦♦ 23k
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