Comparison of murine and human microarray data
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@bas-van-gestel-6508
Last seen 9.6 years ago
Dear all, For a project I would like to compare the gene expression of different immune cells in both mouse and human. For the immune cells of interest, microarray data is available. The microarray data for the human immune cells have been generated with the same platform. The microarray data for the murine immune cells have been generated with the same platform, although with a different platform than used for the human immune cells. I performed RMA normalization using the rma function in the affy package separately for the human and the murine datasets. However, I would like to compare the gene expression levels of mouse and human immune cells. I therefore would like to ask you the following questions:What is the recommended way to normalize the RMA normalized datasets of human and mouse, so that I can compare/combine both datasets? Thanks a lot for your help. Kind regards, Bas [[alternative HTML version deleted]]
Microarray Normalization affy Microarray Normalization affy • 1.4k views
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@sylvainbrohee-4860
Last seen 9.6 years ago
Hi Bas, First of all, I would say that I would not recommend to to this kind of stuff as it seems kind of dirty to me. However, recently, I was 'kindly' asked to perform this type of analyze and I used the good old ComBat function from the sva package to remove the batch effect between human and mouse. In order to have a reliable matrix from the beginning, I used only those genes that had the same gene names (in capital letters for mouse). This is the code I used : Let's eset be the mouse dataset and human.exp.names.agg be the human dataset. Genes are in rows and experiments in columns. row.names(eset) <- toupper(row.names(eset)) human.mouse.complete <- merge(human.exp.names.agg, eset, by = 'row.names') row.names(human.mouse.complete) <- human.mouse.complete[,1] human.mouse.complete <- human.mouse.complete[,-1] pheno.mod0 <- data.frame(row.names = names(human.mouse.complete), fact.1 = rep(1, ncol(human.mouse.complete)), fact.2 = rep(2, ncol(human.mouse.complete))) mod0 <- model.matrix(~1, data = pheno.mod0) human.mouse.complete.combat <- ComBat(human.mouse.complete, c(rep(1, human.exp.nb), c(rep(2,22))), mod = mod0) It seemed to give satisfactory results. If there are more "clever" ways, I would be happy to hear about them! Cheers, Sylvain On 04/22/2014 04:04 PM, Bas van Gestel wrote: > Dear all, > For a project I would like to compare the gene expression of different immune cells in both mouse and human. For the immune cells of interest, microarray data is available. The microarray data for the human immune cells have been generated with the same platform. The microarray data for the murine immune cells have been generated with the same platform, although with a different platform than used for the human immune cells. I performed RMA normalization using the rma function in the affy package separately for the human and the murine datasets. However, I would like to compare the gene expression levels of mouse and human immune cells. I therefore would like to ask you the following questions:What is the recommended way to normalize the RMA normalized datasets of human and mouse, so that I can compare/combine both datasets? > Thanks a lot for your help. > Kind regards, Bas > [[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
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Bas, My (biased) recommendation would be fRMA + barcode. Comparing human and mouse microarray data is Figure 2 of the barcode paper: http://nar.oxfordjournals.org/content/39/suppl_1/D1011.full You still need to be careful with the orthologous gene mapping. Best, Matt On Tue, Apr 22, 2014 at 10:22 AM, Sylvain Broh?e <sbrohee at="" ulb.ac.be=""> wrote: > Hi Bas, > > First of all, I would say that I would not recommend to to this kind of > stuff as it seems kind of dirty to me. > > However, recently, I was 'kindly' asked to perform this type of analyze and > I used the good old ComBat function from the sva package to remove the batch > effect between human and mouse. In order to have a reliable matrix from the > beginning, I used only those genes that had the same gene names (in capital > letters for mouse). > > This is the code I used : > > Let's eset be the mouse dataset and human.exp.names.agg be the human > dataset. Genes are in rows and experiments in columns. > > row.names(eset) <- toupper(row.names(eset)) > human.mouse.complete <- merge(human.exp.names.agg, eset, by = 'row.names') > row.names(human.mouse.complete) <- human.mouse.complete[,1] > human.mouse.complete <- human.mouse.complete[,-1] > pheno.mod0 <- data.frame(row.names = names(human.mouse.complete), fact.1 = > rep(1, ncol(human.mouse.complete)), fact.2 = rep(2, > ncol(human.mouse.complete))) > mod0 <- model.matrix(~1, data = pheno.mod0) > human.mouse.complete.combat <- ComBat(human.mouse.complete, c(rep(1, > human.exp.nb), c(rep(2,22))), mod = mod0) > > It seemed to give satisfactory results. > > If there are more "clever" ways, I would be happy to hear about them! > > Cheers, > > Sylvain > > > > On 04/22/2014 04:04 PM, Bas van Gestel wrote: >> >> Dear all, >> For a project I would like to compare the gene expression of different >> immune cells in both mouse and human. For the immune cells of interest, >> microarray data is available. The microarray data for the human immune cells >> have been generated with the same platform. The microarray data for the >> murine immune cells have been generated with the same platform, although >> with a different platform than used for the human immune cells. I performed >> RMA normalization using the rma function in the affy package separately for >> the human and the murine datasets. However, I would like to compare the gene >> expression levels of mouse and human immune cells. I therefore would like to >> ask you the following questions:What is the recommended way to normalize the >> RMA normalized datasets of human and mouse, so that I can compare/combine >> both datasets? >> Thanks a lot for your help. >> Kind regards, Bas >> [[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 > > > _______________________________________________ > 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 -- Matthew N McCall, PhD 112 Arvine Heights Rochester, NY 14611 Cell: 202-222-5880
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Hi Matt, I just wonder is it possible for you to update the hgu133plus2frmavecs packages to use the most recent customCDF v18? Thanks a lot, Ying > Date: Tue, 22 Apr 2014 10:40:33 -0400 > From: mccallm@gmail.com > To: sbrohee@ulb.ac.be > CC: basvgestel@hotmail.com; bioconductor@r-project.org > Subject: Re: [BioC] Comparison of murine and human microarray data > > Bas, > > My (biased) recommendation would be fRMA + barcode. Comparing human > and mouse microarray data is Figure 2 of the barcode paper: > http://nar.oxfordjournals.org/content/39/suppl_1/D1011.full > > You still need to be careful with the orthologous gene mapping. > > Best, > Matt > > > On Tue, Apr 22, 2014 at 10:22 AM, Sylvain Brohée <sbrohee@ulb.ac.be> wrote: > > Hi Bas, > > > > First of all, I would say that I would not recommend to to this kind of > > stuff as it seems kind of dirty to me. > > > > However, recently, I was 'kindly' asked to perform this type of analyze and > > I used the good old ComBat function from the sva package to remove the batch > > effect between human and mouse. In order to have a reliable matrix from the > > beginning, I used only those genes that had the same gene names (in capital > > letters for mouse). > > > > This is the code I used : > > > > Let's eset be the mouse dataset and human.exp.names.agg be the human > > dataset. Genes are in rows and experiments in columns. > > > > row.names(eset) <- toupper(row.names(eset)) > > human.mouse.complete <- merge(human.exp.names.agg, eset, by = 'row.names') > > row.names(human.mouse.complete) <- human.mouse.complete[,1] > > human.mouse.complete <- human.mouse.complete[,-1] > > pheno.mod0 <- data.frame(row.names = names(human.mouse.complete), fact.1 = > > rep(1, ncol(human.mouse.complete)), fact.2 = rep(2, > > ncol(human.mouse.complete))) > > mod0 <- model.matrix(~1, data = pheno.mod0) > > human.mouse.complete.combat <- ComBat(human.mouse.complete, c(rep(1, > > human.exp.nb), c(rep(2,22))), mod = mod0) > > > > It seemed to give satisfactory results. > > > > If there are more "clever" ways, I would be happy to hear about them! > > > > Cheers, > > > > Sylvain > > > > > > > > On 04/22/2014 04:04 PM, Bas van Gestel wrote: > >> > >> Dear all, > >> For a project I would like to compare the gene expression of different > >> immune cells in both mouse and human. For the immune cells of interest, > >> microarray data is available. The microarray data for the human immune cells > >> have been generated with the same platform. The microarray data for the > >> murine immune cells have been generated with the same platform, although > >> with a different platform than used for the human immune cells. I performed > >> RMA normalization using the rma function in the affy package separately for > >> the human and the murine datasets. However, I would like to compare the gene > >> expression levels of mouse and human immune cells. I therefore would like to > >> ask you the following questions:What is the recommended way to normalize the > >> RMA normalized datasets of human and mouse, so that I can compare/combine > >> both datasets? > >> Thanks a lot for your help. > >> Kind regards, Bas > >> [[alternative HTML version deleted]] > >> > >> _______________________________________________ > >> 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 > > > > > > _______________________________________________ > > 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 > > > > -- > Matthew N McCall, PhD > 112 Arvine Heights > Rochester, NY 14611 > Cell: 202-222-5880 > > _______________________________________________ > 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]]
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Ying, This is getting close to the top of the to-do list. I'll probably get to it in the next month. In the meantime, you can always make your own vectors using the frmaTools package. Best, Matt On Tue, Apr 22, 2014 at 2:07 PM, ying chen <ying_chen at="" live.com=""> wrote: > Hi Matt, > > I just wonder is it possible for you to update the hgu133plus2frmavecs > packages to use the most recent customCDF v18? > > Thanks a lot, > > Ying > >> Date: Tue, 22 Apr 2014 10:40:33 -0400 >> From: mccallm at gmail.com >> To: sbrohee at ulb.ac.be >> CC: basvgestel at hotmail.com; bioconductor at r-project.org >> Subject: Re: [BioC] Comparison of murine and human microarray data > >> >> Bas, >> >> My (biased) recommendation would be fRMA + barcode. Comparing human >> and mouse microarray data is Figure 2 of the barcode paper: >> http://nar.oxfordjournals.org/content/39/suppl_1/D1011.full >> >> You still need to be careful with the orthologous gene mapping. >> >> Best, >> Matt >> >> >> On Tue, Apr 22, 2014 at 10:22 AM, Sylvain Broh?e <sbrohee at="" ulb.ac.be=""> >> wrote: >> > Hi Bas, >> > >> > First of all, I would say that I would not recommend to to this kind of >> > stuff as it seems kind of dirty to me. >> > >> > However, recently, I was 'kindly' asked to perform this type of analyze >> > and >> > I used the good old ComBat function from the sva package to remove the >> > batch >> > effect between human and mouse. In order to have a reliable matrix from >> > the >> > beginning, I used only those genes that had the same gene names (in >> > capital >> > letters for mouse). >> > >> > This is the code I used : >> > >> > Let's eset be the mouse dataset and human.exp.names.agg be the human >> > dataset. Genes are in rows and experiments in columns. >> > >> > row.names(eset) <- toupper(row.names(eset)) >> > human.mouse.complete <- merge(human.exp.names.agg, eset, by = >> > 'row.names') >> > row.names(human.mouse.complete) <- human.mouse.complete[,1] >> > human.mouse.complete <- human.mouse.complete[,-1] >> > pheno.mod0 <- data.frame(row.names = names(human.mouse.complete), fact.1 >> > = >> > rep(1, ncol(human.mouse.complete)), fact.2 = rep(2, >> > ncol(human.mouse.complete))) >> > mod0 <- model.matrix(~1, data = pheno.mod0) >> > human.mouse.complete.combat <- ComBat(human.mouse.complete, c(rep(1, >> > human.exp.nb), c(rep(2,22))), mod = mod0) >> > >> > It seemed to give satisfactory results. >> > >> > If there are more "clever" ways, I would be happy to hear about them! >> > >> > Cheers, >> > >> > Sylvain >> > >> > >> > >> > On 04/22/2014 04:04 PM, Bas van Gestel wrote: >> >> >> >> Dear all, >> >> For a project I would like to compare the gene expression of different >> >> immune cells in both mouse and human. For the immune cells of interest, >> >> microarray data is available. The microarray data for the human immune >> >> cells >> >> have been generated with the same platform. The microarray data for the >> >> murine immune cells have been generated with the same platform, >> >> although >> >> with a different platform than used for the human immune cells. I >> >> performed >> >> RMA normalization using the rma function in the affy package separately >> >> for >> >> the human and the murine datasets. However, I would like to compare the >> >> gene >> >> expression levels of mouse and human immune cells. I therefore would >> >> like to >> >> ask you the following questions:What is the recommended way to >> >> normalize the >> >> RMA normalized datasets of human and mouse, so that I can >> >> compare/combine >> >> both datasets? >> >> Thanks a lot for your help. >> >> Kind regards, Bas >> >> [[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 >> > >> > >> > _______________________________________________ >> > 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 >> >> >> >> -- >> Matthew N McCall, PhD >> 112 Arvine Heights >> Rochester, NY 14611 >> Cell: 202-222-5880 >> >> _______________________________________________ >> 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 -- Matthew N McCall, PhD 112 Arvine Heights Rochester, NY 14611 Cell: 202-222-5880
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@stephen-piccolo-6761
Last seen 3.5 years ago
United States
Ying, You might also consider trying the SCAN.UPC package for this. It can normalize both types of microarrays, and it now has the ability to integrate with the ComBat function of the sva package. Regards, -Steve >>On Tue, Apr 22, 2014 at 10:22 AM, Sylvain Broh?e <sbrohee at="" ulb.ac.be=""> >>wrote: >>> Hi Bas, >>> >>> First of all, I would say that I would not recommend to to this kind of >>> stuff as it seems kind of dirty to me. >>> >>> However, recently, I was 'kindly' asked to perform this type of >>>analyze and >>> I used the good old ComBat function from the sva package to remove the >>>batch >>> effect between human and mouse. In order to have a reliable matrix >>>from the >>> beginning, I used only those genes that had the same gene names (in >>>capital >>> letters for mouse). >>> >>> This is the code I used : >>> >>> Let's eset be the mouse dataset and human.exp.names.agg be the human >>> dataset. Genes are in rows and experiments in columns. >>> >>> row.names(eset) <- toupper(row.names(eset)) >>> human.mouse.complete <- merge(human.exp.names.agg, eset, by = >>>'row.names') >>> row.names(human.mouse.complete) <- human.mouse.complete[,1] >>> human.mouse.complete <- human.mouse.complete[,-1] >>> pheno.mod0 <- data.frame(row.names = names(human.mouse.complete), >>>fact.1 = >>> rep(1, ncol(human.mouse.complete)), fact.2 = rep(2, >>> ncol(human.mouse.complete))) >>> mod0 <- model.matrix(~1, data = pheno.mod0) >>> human.mouse.complete.combat <- ComBat(human.mouse.complete, c(rep(1, >>> human.exp.nb), c(rep(2,22))), mod = mod0) >>> >>> It seemed to give satisfactory results. >>> >>> If there are more "clever" ways, I would be happy to hear about them! >>> >>> Cheers, >>> >>> Sylvain >>> >>> >>> >>> On 04/22/2014 04:04 PM, Bas van Gestel wrote: >>>> >>>> Dear all, >>>> For a project I would like to compare the gene expression of different >>>> immune cells in both mouse and human. For the immune cells of >>>>interest, >>>> microarray data is available. The microarray data for the human >>>>immune cells >>>> have been generated with the same platform. The microarray data for >>>>the >>>> murine immune cells have been generated with the same platform, >>>>although >>>> with a different platform than used for the human immune cells. I >>>>performed >>>> RMA normalization using the rma function in the affy package >>>>separately for >>>> the human and the murine datasets. However, I would like to compare >>>>the gene >>>> expression levels of mouse and human immune cells. I therefore would >>>>like to >>>> ask you the following questions:What is the recommended way to >>>>normalize the >>>> RMA normalized datasets of human and mouse, so that I can >>>>compare/combine >>>> both datasets? >>>> Thanks a lot for your help. >>>> Kind regards, Bas >>>> [[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 >>> >>> >>> _______________________________________________ >>> 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 >>-- >>Matthew N McCall, PhD >>112 Arvine Heights >>Rochester, NY 14611 >>Cell: 202-222-5880 >>_______________________________________________ >>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 > > [[alternative HTML version deleted]] > >
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