Question: How does limma derives its logFC value in two colored arrays?
1
0
Entering edit mode
@gordon-smyth
Last seen 4 hours ago
WEHI, Melbourne, Australia
Dear Sunny, > Date: Mon, 27 Sep 2010 03:14:01 -0400 > From: Sunny Srivastava <research.baba at="" gmail.com=""> > To: bioconductor <bioconductor at="" stat.math.ethz.ch=""> > Subject: [BioC] Question: How does limma derives its logFC value in > two colored arrays? > > Hello Bioconductor Gurus, > > > I have the a data about gene expression from TWO COLORED Agilent array. I > wanted to check differential expression between p3 and wild strain of yeast. > In one array p3 is colored with Cy5 and wild is colored with Cy3 and in the > second array the dyes are swapped. Assuming I have normalized my data using > VSN and obtained M values for the two arrays, I now want to use limma to > derive the differentially expressed genes. > > My model matrix (say design) in this case will be > > p3 > 1 > -1 > > if wild type is the reference. > > If my understanding is correct about how limma analyzes differential > expression, then M value is the dependent variable, sample annotation > (whether p3 or wild, provided by design) is the independent (explanatory) > variable, and a linear model is fit per gene using the following equation. > > > lmFit( M , design) This is all correct. > As the data per gene is small, it is better to use eBayes method to obtain > genewise p-value. But the object obtained from eBayes (say fit3) doesn't > contain the value *logFC*. Yes it does--the logFC is fit3$coefficients. This terminology is because the logFCs are estimated as the coefficients of the linear model. Best wishes Gordon > When I use topTable to order the genes, then > logFC appears. > > The concept of logFC is clear to me in case of a Affy single colored array > (ie log (Int_trt/ Int_control) ), but somehow I am still confused how to > interpret this in two colored arrays. > > In my opinion M value (for each array) should represent logFC if color bias > is ignored. How does limma derives its logFC value in two colored arrays? Is > it based on the B statistics? Please enlighten me ! > > Thanks in advance for any help. > > Best Regards, > S. ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}
Yeast affy vsn limma Yeast affy vsn limma • 1.7k views
ADD COMMENT
0
Entering edit mode
@sunny-srivastava-3793
Last seen 9.6 years ago
Dear Dr. Smyth, Thank you very much for the answer. That clarifies a lot of things! I have one last question to make sure I understood you correctly - in the case of two colored arrays, even if we are regressing the M values on the treatment (sample annotation), we will call the coefficients as logFC. Thanks in advance for your help. Best Regards, S. On Mon, Sep 27, 2010 at 7:17 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote: > Dear Sunny, > > Date: Mon, 27 Sep 2010 03:14:01 -0400 >> From: Sunny Srivastava <research.baba@gmail.com> >> To: bioconductor <bioconductor@stat.math.ethz.ch> >> Subject: [BioC] Question: How does limma derives its logFC value in >> two colored arrays? >> >> Hello Bioconductor Gurus, >> >> >> I have the a data about gene expression from TWO COLORED Agilent array. I >> wanted to check differential expression between p3 and wild strain of >> yeast. >> In one array p3 is colored with Cy5 and wild is colored with Cy3 and in >> the >> second array the dyes are swapped. Assuming I have normalized my data >> using >> VSN and obtained M values for the two arrays, I now want to use limma to >> derive the differentially expressed genes. >> >> My model matrix (say design) in this case will be >> >> p3 >> 1 >> -1 >> >> if wild type is the reference. >> >> If my understanding is correct about how limma analyzes differential >> expression, then M value is the dependent variable, sample annotation >> (whether p3 or wild, provided by design) is the independent (explanatory) >> variable, and a linear model is fit per gene using the following equation. >> >> >> lmFit( M , design) >> > > This is all correct. > > As the data per gene is small, it is better to use eBayes method to obtain >> genewise p-value. But the object obtained from eBayes (say fit3) doesn't >> contain the value *logFC*. >> > > Yes it does--the logFC is fit3$coefficients. This terminology is because > the logFCs are estimated as the coefficients of the linear model. > > Best wishes > Gordon > > When I use topTable to order the genes, then >> logFC appears. >> >> The concept of logFC is clear to me in case of a Affy single colored array >> (ie log (Int_trt/ Int_control) ), but somehow I am still confused how to >> interpret this in two colored arrays. >> >> In my opinion M value (for each array) should represent logFC if color >> bias >> is ignored. How does limma derives its logFC value in two colored arrays? >> Is >> it based on the B statistics? Please enlighten me ! >> >> Thanks in advance for any help. >> >> Best Regards, >> S. >> > > ______________________________________________________________________ > The information in this email is confidential and inte...{{dropped:10}}
ADD COMMENT

Login before adding your answer.

Traffic: 586 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