help to proceed with the interpretation of an analysis

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Alberto Goldoni ▴ 420

@alberto-goldoni-3477

Last seen 9.6 years ago

Dear all, i have a doubt on the way to proceed in analyzing my data. Let me explain what i would like to obtain: i have 1000 genes obtained from the comparison 1 (A vs B) and i would like to obtain the Fold Change and pvalue of this 1000 genes in the comparison 2 (C vs B). After that what i'm gonna to do is to obtain a sort of "value" in order to estimate the "% change" of each gene from the comparison 2 respect the comparison 1 and i don't think that comparing the FC (experiment A) with the FC (experiment B) is enought! Has anyone any comments or suggestions about how to extract a value that allows me to evaluate the change of each gene obtained in the comparison 2 respect the comparison 1? Thanks to anyone who will help me. Best regards. -- ----------------------------------------------------- Dr. Alberto Goldoni Parma, Italy ----------------------------------------------------- [[alternative HTML version deleted]]

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ADD COMMENT • link updated 12.7 years ago by Samuel Wuest ▴ 330 • written 12.7 years ago by Alberto Goldoni ▴ 420

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Samuel Wuest ▴ 330

@samuel-wuest-2821

Last seen 9.6 years ago

Dear Alberto, I am not quite sure how your experimental design looks like in detail, but it seems to me that you have dual-label microarray data and an unconnected design with two factors (hence the two separate comparison?). If this is the case, then there is a very nice functionality in the limma package that is designed to make separate-channel analyses from unconnected designs, including appropriate between-array normalizations etc; thus, you can test any contrast you like and also interactions.... Everything is nicely described in the limma users guide; simply type: library(limma) limmaUsersGuide() and check Chapter 9 " Separate Channel Analysis of Two-Color Data". Would this answer the question? Hope it helps anyway, Best, Sam On 26 July 2011 10:12, Alberto Goldoni <alberto.goldoni1975 at="" gmail.com=""> wrote: > Dear all, > i have a doubt on the way to proceed in analyzing my data. > > Let me explain what i would like to obtain: i have 1000 genes obtained from > the comparison 1 (A vs B) and i would like to obtain the Fold Change and > pvalue of this 1000 genes in the comparison 2 (C vs B). > > After that what i'm gonna to do is to obtain a sort of "value" in order to > estimate the "% change" of each gene from the comparison 2 respect the > comparison 1 and i don't think that comparing the FC (experiment A) with the > FC (experiment B) is enought! > > Has anyone any comments or suggestions about how to extract a value > that allows me to evaluate the change of each gene obtained in the > comparison 2 respect the comparison 1? > > Thanks to anyone who will help me. > > Best regards. > > -- > ----------------------------------------------------- > Dr. Alberto Goldoni > Parma, Italy > ----------------------------------------------------- > > ? ? ? ?[[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > -- ----------------------------------------------------- Samuel Wuest Smurfit Institute of Genetics Trinity College Dublin Dublin 2, Ireland Phone: +353-1-896 2444 Web: http://www.tcd.ie/Genetics/wellmer-2/index.html Email: wuests at tcd.ie

ADD COMMENT • link 12.7 years ago Samuel Wuest ▴ 330

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Dear Samuel, i have affymetrix rattus norvegius chip. i have found 1000 interesting genes in the comparison 1 between "hypertensive rats (A)" vs "standard rats (B)" and i would like to understand the behaviour of these 1000 genes in the comparison 2 "hypertensive rats+omega3 (C)" vs "standard rats (B)" After that i would like to obtain a sort of "value" in order to estimate the "% change" of each gene from the comparison 2 (C vs B) respect the comparison 1 (A vs B). I hope I was more clear best regards 2011/7/26 Samuel Wuest <wuests@tcd.ie> > Dear Alberto, > > I am not quite sure how your experimental design looks like in detail, > but it seems to me that you have dual-label microarray data and an > unconnected design with two factors (hence the two separate > comparison?). If this is the case, then there is a very nice > functionality in the limma package that is designed to make > separate-channel analyses from unconnected designs, including > appropriate between-array normalizations etc; thus, you can test any > contrast you like and also interactions.... > > Everything is nicely described in the limma users guide; simply type: > > library(limma) > limmaUsersGuide() > > and check Chapter 9 " Separate Channel Analysis of Two-Color Data". > > Would this answer the question? Hope it helps anyway, > > Best, Sam > > On 26 July 2011 10:12, Alberto Goldoni <alberto.goldoni1975@gmail.com> > wrote: > > Dear all, > > i have a doubt on the way to proceed in analyzing my data. > > > > Let me explain what i would like to obtain: i have 1000 genes obtained > from > > the comparison 1 (A vs B) and i would like to obtain the Fold Change and > > pvalue of this 1000 genes in the comparison 2 (C vs B). > > > > After that what i'm gonna to do is to obtain a sort of "value" in order > to > > estimate the "% change" of each gene from the comparison 2 respect the > > comparison 1 and i don't think that comparing the FC (experiment A) with > the > > FC (experiment B) is enought! > > > > Has anyone any comments or suggestions about how to extract a value > > that allows me to evaluate the change of each gene obtained in the > > comparison 2 respect the comparison 1? > > > > Thanks to anyone who will help me. > > > > Best regards. > > > > -- > > ----------------------------------------------------- > > Dr. Alberto Goldoni > > Parma, Italy > > ----------------------------------------------------- > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > > > > > -- > ----------------------------------------------------- > Samuel Wuest > Smurfit Institute of Genetics > Trinity College Dublin > Dublin 2, Ireland > Phone: +353-1-896 2444 > Web: http://www.tcd.ie/Genetics/wellmer-2/index.html > Email: wuests@tcd.ie > ------------------------------------------------------ > -- ----------------------------------------------------- Dr. Alberto Goldoni Parma, Italy ----------------------------------------------------- [[alternative HTML version deleted]]

ADD REPLY • link 12.7 years ago Alberto Goldoni ▴ 420

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On Tue, Jul 26, 2011 at 5:50 AM, Alberto Goldoni <alberto.goldoni1975 at="" gmail.com=""> wrote: > Dear Samuel, > i have affymetrix rattus norvegius chip. > > i have found 1000 interesting genes in the comparison 1 between > "hypertensive rats (A)" vs "standard rats (B)" and i would like to > understand the behaviour of these 1000 genes in the comparison 2 > "hypertensive rats+omega3 (C)" vs "standard rats (B)" > > After that i would like to obtain a sort of "value" in order to estimate the > "% change" of each gene from the comparison 2 (C vs B) respect the > comparison 1 (A vs B). Hi, Alberto. It sounds like the biologic hypothesis that you want to test is that omega3 has an effect on some genes in hypertensive rats? If that is correct, I would suggest doing the comparison of C vs A using limma or some other package for differential expression (perhaps the same way you compared A with B and C with B). Sean > best regards > > 2011/7/26 Samuel Wuest <wuests at="" tcd.ie=""> > >> Dear Alberto, >> >> I am not quite sure how your experimental design looks like in detail, >> but it seems to me that you have dual-label microarray data and an >> unconnected design with two factors (hence the two separate >> comparison?). If this is the case, then there is a very nice >> functionality in the limma package that is designed to make >> separate-channel analyses from unconnected designs, including >> appropriate between-array normalizations etc; thus, you can test any >> contrast you like and also interactions.... >> >> Everything is nicely described in the limma users guide; simply type: >> >> library(limma) >> limmaUsersGuide() >> >> and check Chapter 9 " Separate Channel Analysis of Two-Color Data". >> >> Would this answer the question? Hope it helps anyway, >> >> Best, Sam >> >> On 26 July 2011 10:12, Alberto Goldoni <alberto.goldoni1975 at="" gmail.com=""> >> wrote: >> > Dear all, >> > i have a doubt on the way to proceed in analyzing my data. >> > >> > Let me explain what i would like to obtain: i have 1000 genes obtained >> from >> > the comparison 1 (A vs B) and i would like to obtain the Fold Change and >> > pvalue of this 1000 genes in the comparison 2 (C vs B). >> > >> > After that what i'm gonna to do is to obtain a sort of "value" in order >> to >> > estimate the "% change" of each gene from the comparison 2 respect the >> > comparison 1 and i don't think that comparing the FC (experiment A) with >> the >> > FC (experiment B) is enought! >> > >> > Has anyone any comments or suggestions about how to extract a value >> > that allows me to evaluate the change of each gene obtained in the >> > comparison 2 respect the comparison 1? >> > >> > Thanks to anyone who will help me. >> > >> > Best regards. >> > >> > -- >> > ----------------------------------------------------- >> > Dr. Alberto Goldoni >> > Parma, Italy >> > ----------------------------------------------------- >> > >> > ? ? ? ?[[alternative HTML version deleted]] >> > >> > _______________________________________________ >> > Bioconductor mailing list >> > Bioconductor at r-project.org >> > https://stat.ethz.ch/mailman/listinfo/bioconductor >> > Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > >> > >> >> >> >> -- >> ----------------------------------------------------- >> Samuel Wuest >> Smurfit Institute of Genetics >> Trinity College Dublin >> Dublin 2, Ireland >> Phone: +353-1-896 2444 >> Web: http://www.tcd.ie/Genetics/wellmer-2/index.html >> Email: wuests at tcd.ie >> ------------------------------------------------------ >> > > > > -- > ----------------------------------------------------- > Dr. Alberto Goldoni > Parma, Italy > ----------------------------------------------------- > > ? ? ? ?[[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor >

ADD REPLY • link 12.7 years ago Sean Davis 21k

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Thank you Sean, but i guess that the hypothesys is a little bit different. i would obtain 1000 genes from the first comparisono and then i would like to know which is the FC of the previous 1000 genes in the second comparison and then a value in order to compare the two different FC (the second FC respect the first FC). This is what i would like to obtain. Best regards. 2011/7/26 Sean Davis <sdavis2@mail.nih.gov> > On Tue, Jul 26, 2011 at 5:50 AM, Alberto Goldoni > <alberto.goldoni1975@gmail.com> wrote: > > Dear Samuel, > > i have affymetrix rattus norvegius chip. > > > > i have found 1000 interesting genes in the comparison 1 between > > "hypertensive rats (A)" vs "standard rats (B)" and i would like to > > understand the behaviour of these 1000 genes in the comparison 2 > > "hypertensive rats+omega3 (C)" vs "standard rats (B)" > > > > After that i would like to obtain a sort of "value" in order to estimate > the > > "% change" of each gene from the comparison 2 (C vs B) respect the > > comparison 1 (A vs B). > > Hi, Alberto. > > It sounds like the biologic hypothesis that you want to test is that > omega3 has an effect on some genes in hypertensive rats? If that is > correct, I would suggest doing the comparison of C vs A using limma or > some other package for differential expression (perhaps the same way > you compared A with B and C with B). > > Sean > > > best regards > > > > 2011/7/26 Samuel Wuest <wuests@tcd.ie> > > > >> Dear Alberto, > >> > >> I am not quite sure how your experimental design looks like in detail, > >> but it seems to me that you have dual-label microarray data and an > >> unconnected design with two factors (hence the two separate > >> comparison?). If this is the case, then there is a very nice > >> functionality in the limma package that is designed to make > >> separate-channel analyses from unconnected designs, including > >> appropriate between-array normalizations etc; thus, you can test any > >> contrast you like and also interactions.... > >> > >> Everything is nicely described in the limma users guide; simply type: > >> > >> library(limma) > >> limmaUsersGuide() > >> > >> and check Chapter 9 " Separate Channel Analysis of Two-Color Data". > >> > >> Would this answer the question? Hope it helps anyway, > >> > >> Best, Sam > >> > >> On 26 July 2011 10:12, Alberto Goldoni <alberto.goldoni1975@gmail.com> > >> wrote: > >> > Dear all, > >> > i have a doubt on the way to proceed in analyzing my data. > >> > > >> > Let me explain what i would like to obtain: i have 1000 genes obtained > >> from > >> > the comparison 1 (A vs B) and i would like to obtain the Fold Change > and > >> > pvalue of this 1000 genes in the comparison 2 (C vs B). > >> > > >> > After that what i'm gonna to do is to obtain a sort of "value" in > order > >> to > >> > estimate the "% change" of each gene from the comparison 2 respect the > >> > comparison 1 and i don't think that comparing the FC (experiment A) > with > >> the > >> > FC (experiment B) is enought! > >> > > >> > Has anyone any comments or suggestions about how to extract a value > >> > that allows me to evaluate the change of each gene obtained in the > >> > comparison 2 respect the comparison 1? > >> > > >> > Thanks to anyone who will help me. > >> > > >> > Best regards. > >> > > >> > -- > >> > ----------------------------------------------------- > >> > Dr. Alberto Goldoni > >> > Parma, Italy > >> > ----------------------------------------------------- > >> > > >> > [[alternative HTML version deleted]] > >> > > >> > _______________________________________________ > >> > Bioconductor mailing list > >> > Bioconductor@r-project.org > >> > https://stat.ethz.ch/mailman/listinfo/bioconductor > >> > Search the archives: > >> http://news.gmane.org/gmane.science.biology.informatics.conductor > >> > > >> > > >> > >> > >> > >> -- > >> ----------------------------------------------------- > >> Samuel Wuest > >> Smurfit Institute of Genetics > >> Trinity College Dublin > >> Dublin 2, Ireland > >> Phone: +353-1-896 2444 > >> Web: http://www.tcd.ie/Genetics/wellmer-2/index.html > >> Email: wuests@tcd.ie > >> ------------------------------------------------------ > >> > > > > > > > > -- > > ----------------------------------------------------- > > Dr. Alberto Goldoni > > Parma, Italy > > ----------------------------------------------------- > > > > [[alternative HTML version deleted]] > > > > _______________________________________________ > > Bioconductor mailing list > > Bioconductor@r-project.org > > https://stat.ethz.ch/mailman/listinfo/bioconductor > > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > > > -- ----------------------------------------------------- Dr. Alberto Goldoni Parma, Italy ----------------------------------------------------- [[alternative HTML version deleted]]

ADD REPLY • link 12.7 years ago Alberto Goldoni ▴ 420

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