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Question: LIMMA: MA, design, and contrasts
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gravatar for Tiandao Li
9.6 years ago by
Tiandao Li260
Tiandao Li260 wrote:
Hello, I am using limma for 2-color microarray data analysis. I have some questions regarding MA, design, and contrasts. 1. I have one MA file including all my experiments. Rows of MA correspond to spots and columns to individual experimental file. Columns were listed as the increasing order of file names, since I used barcodes as file names. Then after reading in the target file and creating design matrix, I used the following to caluculate correlation between duplicates. corfit <- duplicateCorrelation(MA,design,ndups=4) # A slow computation! corfit$consensus.correlation However if columns of MA2 are listed following the order of target$FileName, corfit2$consensus.correlation value is different from one of MA. Which one should I use in the next anlysis? 2. I have one MA file including all my experiments, and also an all- in-one contrast matrix including different contrasts (related or un-related). Should I use this all-in-one contrast matrix for linear model to find the differentially expressed genes? This doesn't sound right. Or I use subset of MA for and only for related one or more contrasts, and use all-in- one MA only necessary. Which one is better? Thanks in advance, Tiandao
ADD COMMENTlink modified 9.6 years ago by Jenny Drnevich2.2k • written 9.6 years ago by Tiandao Li260
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gravatar for Jenny Drnevich
9.6 years ago by
Jenny Drnevich2.2k
Jenny Drnevich2.2k wrote:
Hi Tiandao, A quick answer to your first question: >corfit <- duplicateCorrelation(MA,design,ndups=4) # A slow computation! The order of the arrays in the columns of MA **MUST** match the order of the arrays in the rows of design, else your design matrix is not correct for your MA object. >2. I have one MA file including all my experiments, and also an all- in-one >contrast matrix including different contrasts (related or un- related). >Should I use this all-in-one contrast matrix for linear model to find the >differentially expressed genes? This doesn't sound right. Or I use subset >of MA for and only for related one or more contrasts, and use all-in- one >MA only necessary. Which one is better? I don't quite understand your second question... each contrast in your contrast matrix will be estimated separately using only those columns of MA that are indicated from the design matrix and the contrast matrix. Perhaps if you could explain your question in more detail with example code, we could better answer it. Cheers, Jenny >Thanks in advance, > >Tiandao > >_______________________________________________ >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 uiuc.edu
ADD COMMENTlink written 9.6 years ago by Jenny Drnevich2.2k
Hello Jenny, I created a composite design matrix with some common reference arrays and arrays from some loop designs, such as loop design 1 and loop design 2. I have a big MA with all normalized arrays, and a big contrast matrix to find the differentially expressed genes in different treatments only existed in loop design 1. Or, I can build a small MA, a small design matrix using the same reference, and contrast matrix for some contrasts only for arrays in loop design 1. Which way can I get more reliable result? Or I will get the same result. Thanks, Tiandao On Thu, 20 Sep 2007, Jenny Drnevich wrote: Hi Tiandao, A quick answer to your first question: >corfit <- duplicateCorrelation(MA,design,ndups=4) # A slow computation! The order of the arrays in the columns of MA **MUST** match the order of the arrays in the rows of design, else your design matrix is not correct for your MA object. >2. I have one MA file including all my experiments, and also an all- in-one >contrast matrix including different contrasts (related or un- related). >Should I use this all-in-one contrast matrix for linear model to find the >differentially expressed genes? This doesn't sound right. Or I use subset >of MA for and only for related one or more contrasts, and use all-in- one >MA only necessary. Which one is better? I don't quite understand your second question... each contrast in your contrast matrix will be estimated separately using only those columns of MA that are indicated from the design matrix and the contrast matrix. Perhaps if you could explain your question in more detail with example code, we could better answer it. Cheers, Jenny >Thanks in advance, > >Tiandao > >_______________________________________________ >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 uiuc.edu _______________________________________________ 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
ADD REPLYlink written 9.6 years ago by Tiandao Li260
Hi Tiandao, It sounds like you are combining arrays from different experiments - one a reference design, another a loop design, and a third that's a different loop design. In general, it's NOT good to combine arrays from such vastly different experiments, because there will be batch effects between the experiments and the differences in the type of design (ref. vs. loop) necessitate different analysis methods. You should probably keep arrays from each experiment completely separate, from the normalization and pre-processing to the statistical analysis. If there is a compelling reason to try to combine these arrays, then I suggest you find a local statistician or experienced microarray analyst to help you, because the chances of messing up somewhere along the line are very high!!! Jenny At 04:00 PM 9/20/2007, Tiandao Li wrote: >Hello Jenny, > >I created a composite design matrix with some common reference arrays and >arrays from some loop designs, such as loop design 1 and loop design 2. I >have a big MA with all normalized arrays, and a big contrast matrix to >find the differentially expressed genes in different treatments only >existed in loop design 1. Or, I can build a small MA, a small design >matrix using the same reference, and contrast matrix for some contrasts >only for arrays in loop design 1. Which way can I get more reliable >result? Or I will get the same result. > >Thanks, > >Tiandao > > >On Thu, 20 Sep 2007, Jenny Drnevich wrote: > >Hi Tiandao, > >A quick answer to your first question: > > >corfit <- duplicateCorrelation(MA,design,ndups=4) # A slow computation! > >The order of the arrays in the columns of MA **MUST** match the order >of the arrays in the rows of design, else your design matrix is not >correct for your MA object. > > > >2. I have one MA file including all my experiments, and also an all-in-one > >contrast matrix including different contrasts (related or un- related). > >Should I use this all-in-one contrast matrix for linear model to find the > >differentially expressed genes? This doesn't sound right. Or I use subset > >of MA for and only for related one or more contrasts, and use all- in-one > >MA only necessary. Which one is better? > >I don't quite understand your second question... each contrast in >your contrast matrix will be estimated separately using only those >columns of MA that are indicated from the design matrix and the >contrast matrix. Perhaps if you could explain your question in more >detail with example code, we could better answer it. > >Cheers, >Jenny > > > > >Thanks in advance, > > > >Tiandao > > > >_______________________________________________ > >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 uiuc.edu > >_______________________________________________ >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 uiuc.edu
ADD REPLYlink written 9.6 years ago by Jenny Drnevich2.2k
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