Model 3 is completely illegal. Model 2 is sometimes used when there are few within subject observations (as here). However, I would not do that here. I would use an eBayes method such as limma to improve power. --Naomi At 11:34 AM 6/5/2007, shirley zhang wrote: >Dear Bioconductor, > >In a microarray data, there are 20 subjects grouped by Gender, each >subject has 2 tissues (normal vs. cancer). > >In fact, it is a 2-way anova (factors: Gender and tissue) with tissue >nested in subject. I've tried the following: > >Model 1: lme(response ~ tissue*Gender, random = ~1|subject) >Model 2: response ~ tissue*Gender + subject >Model 3: response ~ tissue*Gender > > >It seems like Model 1 is the correct one since my experiment design is >nested design. However, I got a few significant genes for Gender >effect from Model 1 so I want to use Model 2 or Model 3. Can anybody >tell me whether Model2 is >completely illegal? > >Thanks, > >_______________________________________________ >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 Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111

Dear Shirley: If I understood right your design, you had both 'normal' and 'cancer' tissues sampled in each subject, right? So in this case you do not have 'tissue nested in subject', like you say. Subjects are within gender, but crossed with tissues. You have, as you say, a 2-way anova (factors: Gender and tissue), but you have repeated measurements on each subject, i.e. the two tissues assayed in ach subject. In this case you can see subjects as a blocking factor, but actually you have two sampling levels, like a split-plot design. I would suggest a model like: y = mu + gender + subject(gender) + tissue + gender*tissue + e where gender, tissue and the interaction gender*tissue are considered as fixed effects; and subject within gender is random (which is actually the error term to compare the two genders). You may want to use the MAANOVA software to run such a model (http://www.jax.org/staff/churchill/labsite/software/Rmaanova/index.ht ml). LIMMA may not give you the flexibility to implement it. Cheers, Guilherme. -- Guilherme J. M. Rosa Assistant Professor Department of Dairy Science University of Wisconsin - Madison 460 Animal Science Building 1675 Observatory Dr. Madison, WI 53706 USA Phone: + 1 (608) 265-8617 Fax: + 1 (608) 263-9412 E-mail: grosa at wisc.edu http://www.wisc.edu/dysci/facstaff/Rosa/index.html ----- Original Message ----- From: "Naomi Altman" <naomi@stat.psu.edu> To: "shirley zhang" <shirley0818 at="" gmail.com="">; <bioconductor at="" stat.math.ethz.ch=""> Sent: Wednesday, June 06, 2007 9:28 AM Subject: Re: [BioC] Can subject be treated as fixed effect in linear model with microarray data > Model 3 is completely illegal. Model 2 is sometimes used when there > are few within subject observations (as here). However, I would not > do that here. I would use an eBayes method such as limma to improve > power. > > --Naomi > > At 11:34 AM 6/5/2007, shirley zhang wrote: >>Dear Bioconductor, >> >>In a microarray data, there are 20 subjects grouped by Gender, each >>subject has 2 tissues (normal vs. cancer). >> >>In fact, it is a 2-way anova (factors: Gender and tissue) with tissue >>nested in subject. I've tried the following: >> >>Model 1: lme(response ~ tissue*Gender, random = ~1|subject) >>Model 2: response ~ tissue*Gender + subject >>Model 3: response ~ tissue*Gender >> >> >>It seems like Model 1 is the correct one since my experiment design is >>nested design. However, I got a few significant genes for Gender >>effect from Model 1 so I want to use Model 2 or Model 3. Can anybody >>tell me whether Model2 is >>completely illegal? >> >>Thanks, >> >>_______________________________________________ >>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 > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > > _______________________________________________ > 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

Hi Naomi, Thank you so much for your help. I also remember you gave me some very helpful suggestions regarding Quantile Normalization aross different tissues a few days ago. Thanks again. Again for this issue, can limma handle mixed effect model like lme? I can only do paired test with Limma, but here each subject has 3 tissues. Thanks, Shirley On 6/6/07, Naomi Altman <naomi at="" stat.psu.edu=""> wrote: > Model 3 is completely illegal. Model 2 is sometimes used when there > are few within subject observations (as here). However, I would not > do that here. I would use an eBayes method such as limma to improve power. > > --Naomi > > At 11:34 AM 6/5/2007, shirley zhang wrote: > >Dear Bioconductor, > > > >In a microarray data, there are 20 subjects grouped by Gender, each > >subject has 2 tissues (normal vs. cancer). > > > >In fact, it is a 2-way anova (factors: Gender and tissue) with tissue > >nested in subject. I've tried the following: > > > >Model 1: lme(response ~ tissue*Gender, random = ~1|subject) > >Model 2: response ~ tissue*Gender + subject > >Model 3: response ~ tissue*Gender > > > > > >It seems like Model 1 is the correct one since my experiment design is > >nested design. However, I got a few significant genes for Gender > >effect from Model 1 so I want to use Model 2 or Model 3. Can anybody > >tell me whether Model2 is > >completely illegal? > > > >Thanks, > > > >_______________________________________________ > >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 > > Naomi S. Altman 814-865-3791 (voice) > Associate Professor > Dept. of Statistics 814-863-7114 (fax) > Penn State University 814-865-1348 (Statistics) > University Park, PA 16802-2111 > >