Single Channel Approach for Agilent Arrays
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@gaj-stan-bigcat-1591
Last seen 10.2 years ago
Dear BioConductor-user, I've recently tested out Chapter 9 in the Limma user documentation concerning applying single channel approach on Agilent arrays. My experimental setup consists of two different (pooled) food interventions in dye flip using the same control sample (n=4 - For each experiment, 1 array + dye-flip). I assume that my ultimate single-channel design matrix would look like this fControl fFood1 fFood2 (1) 1 0 0 (2) 0 1 0 (3) 0 1 0 (4) 1 0 0 (5) 1 0 0 (6) 0 0 1 (7) 0 0 1 (8) 1 0 0 Where: Food1_vs_Control.Cy3 (1) Food1_vs_Control.Cy5 (2) Control_vs_Food1.Cy3 (3) Control_vs_Food1.Cy5 (4) Food2_vs_Control.Cy3 (5) Food2_vs_Control.Cy5 (6) Control_vs_Food2.Cy3 (7) Control_vs_Food2.Cy5 (8) Question 1: Am I missing any information here? Because if I do this as suggested in the manual, I get a matrix with some extra (and empty) attributes such as assign (1,1,1), contrasts (NULL) and contrasts$f ("contr.treatment"). Are these attributes necessary for further steps? Question 2: In my normal two-channel analysis approach I added an extra column in my design file that includes the DyeEffect in the statistical analysis (Ebayes) afterwards. Do I need to include them here as well? The next part is calculating the intraspotCorrelation, based upon the design file above. When executing, I get the following error: "Missing or Infinite Values found in M or A". This error should indeed occur, since individual spots of bad quality were flagged as NA before normalization, resulting in no M or A value (missing value). Is there a way to omit these spots from the intraspotCorrelation function (By changing them to 0 it might have an effect on the average correlation factor?) or does something else need to be done to solve this? Any suggestions would be greatly appreciated! Best wishes, Stan --------------------------------------- Stan Gaj, MSc PhD Student Dept. of Human Biology / BiGCaT Bioinformatics / Nutrigenomics Consortium PO BOX 616 UNS 50 - Box 28 University Maastricht 6200MD Maastricht the Netherlands ? Tel:? +31 (0)43 3882913 Fax: +31 (0)43 3670976 ?
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 3.6 years ago
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While I always use the single channel approach for loop designs, I see no compelling reason to use it for a reference design, which is what you have here. Since you appear to have technical replicates for the dye swap, I would use the 2-channel approach, and save the "blocks" for the technical replications. --Naomi At 09:17 AM 10/2/2006, Gaj Stan (BIGCAT) wrote: >Dear BioConductor-user, > >I've recently tested out Chapter 9 in the Limma user documentation >concerning applying single channel approach on Agilent arrays. My >experimental setup consists of two different (pooled) food >interventions in dye flip using the same control sample (n=4 - For >each experiment, 1 array + dye-flip). I assume that my ultimate >single-channel design matrix would look like this > > fControl fFood1 fFood2 >(1) 1 0 0 >(2) 0 1 0 >(3) 0 1 0 >(4) 1 0 0 >(5) 1 0 0 >(6) 0 0 1 >(7) 0 0 1 >(8) 1 0 0 > >Where: >Food1_vs_Control.Cy3 (1) >Food1_vs_Control.Cy5 (2) >Control_vs_Food1.Cy3 (3) >Control_vs_Food1.Cy5 (4) >Food2_vs_Control.Cy3 (5) >Food2_vs_Control.Cy5 (6) >Control_vs_Food2.Cy3 (7) >Control_vs_Food2.Cy5 (8) > >Question 1: Am I missing any information here? Because if I do this >as suggested in the manual, I get a matrix with some extra (and >empty) attributes such as assign (1,1,1), contrasts (NULL) and >contrasts$f ("contr.treatment"). Are these attributes necessary for >further steps? > >Question 2: In my normal two-channel analysis approach I added an >extra column in my design file that includes the DyeEffect in the >statistical analysis (Ebayes) afterwards. Do I need to include them >here as well? > >The next part is calculating the intraspotCorrelation, based upon >the design file above. When executing, I get the following error: >"Missing or Infinite Values found in M or A". This error should >indeed occur, since individual spots of bad quality were flagged as >NA before normalization, resulting in no M or A value (missing >value). Is there a way to omit these spots from the >intraspotCorrelation function (By changing them to 0 it might have >an effect on the average correlation factor?) or does something else >need to be done to solve this? > >Any suggestions would be greatly appreciated! > >Best wishes, > > Stan > > >--------------------------------------- >Stan Gaj, MSc >PhD Student >Dept. of Human Biology / BiGCaT Bioinformatics / Nutrigenomics Consortium >PO BOX 616 >UNS 50 - Box 28 >University Maastricht >6200MD Maastricht >the Netherlands > >Tel: +31 (0)43 3882913 >Fax: +31 (0)43 3670976 > > >_______________________________________________ >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
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Dear Naomi, Thanks for your initial response. My intention was to test out the single channel analysis (SCA) approach on a very simple and easy dataset. The reason behind choosing SCA for this dataset was based upon finding a larger amount of genes that reacted inconsistently (|FC| >1.4 and opposite direction on both the normal and the dye flip) during my two-channel analysis compared with the amount of genes that reacted consistently on both arrays. This could be explained as a possible dye-effect and I thought that a SCA approach might improve these type of 'weird' results, at least for that specific list of genes. I do understand that the SCA enables you to do indirect comparisons between different two-coloured arrays (using contrasts), and that that is not my main aim here. I'm trying to catch up on the subject, so do correct me if I'm wrong! (-; I do have other experimental Agilent data constructed in a more general loop-design (also dye-flipped) with the same issue at hand. However, if I use the SCA for that, then I'd still come across my 'pretty basic' questions mentioned below, i.e. take dye-effect into design-file and bypassing the 'error' in the intraspotCorrelation function (-: Best wishes, Stan -----Original Message----- From: Naomi Altman [mailto:naomi@stat.psu.edu] Sent: 03 October 2006 05:13 To: Gaj Stan (BIGCAT); bioconductor at stat.math.ethz.ch Subject: Re: [BioC] Single Channel Approach for Agilent Arrays While I always use the single channel approach for loop designs, I see no compelling reason to use it for a reference design, which is what you have here. Since you appear to have technical replicates for the dye swap, I would use the 2-channel approach, and save the "blocks" for the technical replications. --Naomi At 09:17 AM 10/2/2006, Gaj Stan (BIGCAT) wrote: >Dear BioConductor-user, > >I've recently tested out Chapter 9 in the Limma user documentation >concerning applying single channel approach on Agilent arrays. My >experimental setup consists of two different (pooled) food >interventions in dye flip using the same control sample (n=4 - For >each experiment, 1 array + dye-flip). I assume that my ultimate >single-channel design matrix would look like this > > fControl fFood1 fFood2 >(1) 1 0 0 >(2) 0 1 0 >(3) 0 1 0 >(4) 1 0 0 >(5) 1 0 0 >(6) 0 0 1 >(7) 0 0 1 >(8) 1 0 0 > >Where: >Food1_vs_Control.Cy3 (1) >Food1_vs_Control.Cy5 (2) >Control_vs_Food1.Cy3 (3) >Control_vs_Food1.Cy5 (4) >Food2_vs_Control.Cy3 (5) >Food2_vs_Control.Cy5 (6) >Control_vs_Food2.Cy3 (7) >Control_vs_Food2.Cy5 (8) > >Question 1: Am I missing any information here? Because if I do this >as suggested in the manual, I get a matrix with some extra (and >empty) attributes such as assign (1,1,1), contrasts (NULL) and >contrasts$f ("contr.treatment"). Are these attributes necessary for >further steps? > >Question 2: In my normal two-channel analysis approach I added an >extra column in my design file that includes the DyeEffect in the >statistical analysis (Ebayes) afterwards. Do I need to include them >here as well? > >The next part is calculating the intraspotCorrelation, based upon >the design file above. When executing, I get the following error: >"Missing or Infinite Values found in M or A". This error should >indeed occur, since individual spots of bad quality were flagged as >NA before normalization, resulting in no M or A value (missing >value). Is there a way to omit these spots from the >intraspotCorrelation function (By changing them to 0 it might have >an effect on the average correlation factor?) or does something else >need to be done to solve this? > >Any suggestions would be greatly appreciated! > >Best wishes, > > Stan > > >--------------------------------------- >Stan Gaj, MSc >PhD Student >Dept. of Human Biology / BiGCaT Bioinformatics / Nutrigenomics Consortium >PO BOX 616 >UNS 50 - Box 28 >University Maastricht >6200MD Maastricht >the Netherlands > >Tel: +31 (0)43 3882913 >Fax: +31 (0)43 3670976 > > >_______________________________________________ >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
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