lmFit function
1
0
Entering edit mode
@priscila-grynberg-3196
Last seen 9.6 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 • 1.6k views
ADD COMMENT
0
Entering edit mode
Jenny Drnevich ★ 2.0k
@jenny-drnevich-2812
Last seen 21 days 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
ADD COMMENT
0
Entering edit mode
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]]
ADD REPLY

Login before adding your answer.

Traffic: 624 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6