identification of disease groups
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Abhilash Venu ▴ 340
@abhilash-venu-2680
Last seen 9.6 years ago
Hi All, I am trying to analyze expression array data (single color on Agilent platform) from a disease scenario where we have 5 normal samples (untreated) with 6 patients treated with drug A and 5 patients treated with drug A + B. The biologists question is there any serious perturbations of the genes between two groups (Treated with A only and treated with A+B). They are interested to represent the same by a cluster. I am just wondering, whether I should do separate analysis of those two groups using limma and cluster together by selecting genes using some parameters such as P value or fold value.? or Whether I should do analysis together using limma and cluster it by selecting genes by fold value of P value.? It will be of great help if any one of you could provide suggestions on the same. Thanks in advance. Regards, Abhilash [[alternative HTML version deleted]]
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@saroj-k-mohapatra-3419
Last seen 9.6 years ago
Hello Abhilash: > ... where we have 5 normal samples (untreated) > with 6 patients treated with drug A and 5 patients treated with drug A + > B. The biologists question is there any serious perturbations of the genes > between two groups (Treated with A only and treated with A+B). I agree with your choice: by applying a p.value/ratio threshold in limma one can find the genes affected by drug A (A-Control) and genes affected by drug A+B (AB-Control). Union of the two sets would have all the genes differentially expressed (d.e.) by EITHER treatment; intersection would have the genes d.e. in BOTH. > They are > interested to represent the same by a cluster. > Do you mean a heatmap from hierarchical clustering? You could select the genes and then get the expression matrix for all three groups of samples, order them by genes only (not samples) and the resulting heatmap would have, hopefully, regions of gene expression showing difference between control and treatments. Best, Saroj > I am just wondering, whether I should do separate analysis of those two > groups using limma and cluster together by selecting genes using some > parameters such as P value or fold value.? > or > Whether I should do analysis together using limma and cluster it by > selecting genes by fold value of P value.? > > It will be of great help if any one of you could provide suggestions on the > same. > > Thanks in advance. > > Regards, > Abhilash > > [[alternative HTML version deleted]] > > _______________________________________________ > 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 > >
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Hi Saroj, Thanks for your suggestions. Abhilash On Thu, Jun 11, 2009 at 8:19 PM, Saroj K Mohapatra <saroj@vt.edu> wrote: > Hello Abhilash: > >> ... where we have 5 normal samples (untreated) >> with 6 patients treated with drug A and 5 patients treated with drug A + >> B. The biologists question is there any serious perturbations of the >> genes >> between two groups (Treated with A only and treated with A+B). >> > I agree with your choice: by applying a p.value/ratio threshold in limma > one can find the genes affected by drug A (A-Control) and genes affected by > drug A+B (AB-Control). Union of the two sets would have all the genes > differentially expressed (d.e.) by EITHER treatment; intersection would have > the genes d.e. in BOTH. > >> They are >> interested to represent the same by a cluster. >> >> > Do you mean a heatmap from hierarchical clustering? You could select the > genes and then get the expression matrix for all three groups of samples, > order them by genes only (not samples) and the resulting heatmap would have, > hopefully, regions of gene expression showing difference between control and > treatments. > > Best, > > Saroj > >> I am just wondering, whether I should do separate analysis of those two >> groups using limma and cluster together by selecting genes using some >> parameters such as P value or fold value.? >> or >> Whether I should do analysis together using limma and cluster it by >> selecting genes by fold value of P value.? >> >> It will be of great help if any one of you could provide suggestions on >> the >> same. >> >> Thanks in advance. >> >> Regards, >> Abhilash >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> >> >> > > -- Regards, Abhilash [[alternative HTML version deleted]]
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