lmFit function
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@priscila-grynberg-3196
Last seen 10.2 years ago
Hi BioCs, I have a doubt about this function. I'm working with a two-channel dye-swap microarray experiments. After testing all normalization methods, I conclude that the robustspline method was the best for my data. After normalizing, I did the statistical analysis using the lmFit and eBayes functions. My commands were: fit <- lmFit(MA, design, ndups=2, spacing=12, cor=corfit$consensus) fit2 <- eBayes(fit) Everything worked just fine. However, I read the lmFit help and a doubt came up: lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,weig hts=NULL, method="ls",...) method: character string, "ls" for least squares or "robust" for robust regression Then, I repeat the same commands, but changing the parameter "method", since I used the robustspline method for normalization. I got different top 100 genes most differentially expressed. And now I really confused. I don't know what to do with this "method" parameter. I hope someone can explain to me! Thanks, Priscila -- Priscila Grynberg, B.Sc., M.Sc. Doutoranda em Bioinformática (Bioinformatics D.Sc student) Laboratório de Genética Bioquímica Universidade Federal de Minas Gerais Tel: +55 31 3409-2628 CV: http://lattes.cnpq.br/8808643075395963 [[alternative HTML version deleted]]
Microarray Normalization Microarray Normalization • 1.7k views
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Jenny Drnevich ★ 2.0k
@jenny-drnevich-2812
Last seen 5 months ago
United States
Hi Priscila, The robustspline method for normalization has nothing to do with the lmFit(method="robust"). lmFit can either fit the model using a least squares regression or a robust regression, which down-weights replicates that are different from the other replicates. Whether or not to use lmFit(method="robust") doesn't depend on which normalization method you use, but rather (IMO) how many replicates you have. If you have a relatively large number of replicates, say 6 or more, then the robust fitting of the model may help to remove true outliers from affecting the data. However, if you only have 3 replicates, as is usual for microarray experiments, using the robust estimation may remove real variation in your samples and lead to more false-positives. That's my take on the situation... Jenny At 10:49 AM 1/8/2009, Priscila Grynberg wrote: >Content-Type: text/plain >Content-Disposition: inline >Content-length: 1308 > >Hi BioCs, >I have a doubt about this function. > >I'm working with a two-channel dye-swap microarray experiments. After >testing all normalization methods, I conclude that the robustspline method >was the best for my data. After normalizing, I did the statistical analysis >using the lmFit and eBayes functions. > >My commands were: > >fit <- lmFit(MA, design, ndups=2, spacing=12, cor=corfit$consensus) > >fit2 <- eBayes(fit) > >Everything worked just fine. However, I read the lmFit help and a doubt came >up: > >lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,wei ghts=NULL, >method="ls",...) > >method: character string, "ls" for least squares or "robust" for robust >regression > >Then, I repeat the same commands, but changing the parameter "method", since >I used the robustspline method for normalization. I got different top 100 >genes most differentially expressed. And now I really confused. I don't know >what to do with this "method" parameter. > >I hope someone can explain to me! > >Thanks, > >Priscila > > > > >-- >Priscila Grynberg, B.Sc., M.Sc. >Doutoranda em Bioinform?tica (Bioinformatics D.Sc student) >Laborat?rio de Gen?tica Bioqu?mica >Universidade Federal de Minas Gerais >Tel: +55 31 3409-2628 >CV: http://lattes.cnpq.br/8808643075395963 > > [[alternative HTML version deleted]] > > >_______________________________________________ >Bioconductor mailing list >Bioconductor at stat.math.ethz.ch >https://stat.ethz.ch/mailman/listinfo/bioconductor >Search the archives: >http://news.gmane.org/gmane.science.biology.informatics.conductor Jenny Drnevich, Ph.D. Functional Genomics Bioinformatics Specialist W.M. Keck Center for Comparative and Functional Genomics Roy J. Carver Biotechnology Center University of Illinois, Urbana-Champaign 330 ERML 1201 W. Gregory Dr. Urbana, IL 61801 USA ph: 217-244-7355 fax: 217-265-5066 e-mail: drnevich at illinois.edu
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Thanks, it was really helpful. Priscila On Thu, Jan 8, 2009 at 3:58 PM, Jenny Drnevich <drnevich@illinois.edu>wrote: > Hi Priscila, > > The robustspline method for normalization has nothing to do with the > lmFit(method="robust"). lmFit can either fit the model using a least squares > regression or a robust regression, which down-weights replicates that are > different from the other replicates. Whether or not to use > lmFit(method="robust") doesn't depend on which normalization method you use, > but rather (IMO) how many replicates you have. If you have a relatively > large number of replicates, say 6 or more, then the robust fitting of the > model may help to remove true outliers from affecting the data. However, if > you only have 3 replicates, as is usual for microarray experiments, using > the robust estimation may remove real variation in your samples and lead to > more false-positives. > > That's my take on the situation... > Jenny > > At 10:49 AM 1/8/2009, Priscila Grynberg wrote: > >> Content-Type: text/plain >> Content-Disposition: inline >> Content-length: 1308 >> >> >> Hi BioCs, >> I have a doubt about this function. >> >> I'm working with a two-channel dye-swap microarray experiments. After >> testing all normalization methods, I conclude that the robustspline method >> was the best for my data. After normalizing, I did the statistical >> analysis >> using the lmFit and eBayes functions. >> >> My commands were: >> >> fit <- lmFit(MA, design, ndups=2, spacing=12, cor=corfit$consensus) >> >> fit2 <- eBayes(fit) >> >> Everything worked just fine. However, I read the lmFit help and a doubt >> came >> up: >> >> >> lmFit(object,design=NULL,ndups=1,spacing=1,block=NULL,correlation,w eights=NULL, >> method="ls",...) >> >> method: character string, "ls" for least squares or "robust" for robust >> regression >> >> Then, I repeat the same commands, but changing the parameter "method", >> since >> I used the robustspline method for normalization. I got different top 100 >> genes most differentially expressed. And now I really confused. I don't >> know >> what to do with this "method" parameter. >> >> I hope someone can explain to me! >> >> Thanks, >> >> Priscila >> >> >> >> >> -- >> Priscila Grynberg, B.Sc., M.Sc. >> Doutoranda em Bioinformática (Bioinformatics D.Sc student) >> Laboratório de Genética Bioquímica >> Universidade Federal de Minas Gerais >> Tel: +55 31 3409-2628 >> CV: http://lattes.cnpq.br/8808643075395963 >> >> [[alternative HTML version deleted]] >> >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > Jenny Drnevich, Ph.D. > > Functional Genomics Bioinformatics Specialist > W.M. Keck Center for Comparative and Functional Genomics > Roy J. Carver Biotechnology Center > University of Illinois, Urbana-Champaign > > 330 ERML > 1201 W. Gregory Dr. > Urbana, IL 61801 > USA > > ph: 217-244-7355 > fax: 217-265-5066 > e-mail: drnevich@illinois.edu > -- Priscila Grynberg, B.Sc., M.Sc. Doutoranda em Bioinformática (Bioinformatics D.Sc student) Laboratório de Genética Bioquímica Universidade Federal de Minas Gerais Tel: +55 31 3409-2628 CV: http://lattes.cnpq.br/8808643075395963 [[alternative HTML version deleted]]
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