getGEO and wilcox.test
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Voke AO ▴ 760
@voke-ao-4830
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Hi all, I am not quite sure how to use the expression set I get from getGEO(), say gds157, in wilcox.test(). Please help. Thanks. Avoks
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@vincent-j-carey-jr-4
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Please read the posting guide http://www.bioconductor.org/help/mailing-list/posting-guide/ before querying this list. You have not given any information on how you have used getGEO. To help you, I issued > library(GEOquery) Setting options('download.file.method.GEOquery'='auto') > gg = getGEO("GDS157") File stored at: /var/folders/4D/4DI98FkjGzq0K2niUTEHSE+++TM/-Tmp-//RtmpGnz9Cf/GDS157.s oft.gz > gg An object of class "GDS" channel_count [1] "1" dataset_id [1] "GDS157" "GDS157" description [1] "Analysis of gene expression in pooled vastus lateralis muscle samples from insulin-sensitive and insulin-resistant equally obese, non- diabetic Pima Indians. A search for susceptibility genes for type 2 diabetes. " ... > getClass("GDS") Class "GDS" [package "GEOquery"] Slots: Name: gpl dataTable header Class: GPL GEODataTable list Extends: "GEOData" > getClass("GEODataTable") Class "GEODataTable" [package "GEOquery"] Slots: Name: columns table Class: data.frame data.frame Here I am using R's self-describing capacities to learn about what the query returned. > gg@dataTable@columns sample metabolism 1 GSM2289 insulin resistant 2 GSM2294 insulin resistant 3 GSM2299 insulin resistant 4 GSM2304 insulin resistant 5 GSM2309 insulin resistant 6 GSM2313 insulin sensitive 7 GSM2318 insulin sensitive 8 GSM2323 insulin sensitive 9 GSM2328 insulin sensitive 10 GSM2333 insulin sensitive description 1 Value for GSM2289: insulin resistant sample pool 1 muscle on HuFL; src: muscle 2 Value for GSM2294: insulin resistant sample pool 2 muscle on HuFL; src: muscle 3 Value for GSM2299: insulin resistant sample pool 3 muscle on HuFL; src: muscle 4 Value for GSM2304: insulin resistant sample pool 4 muscle on HuFL; src: muscle 5 Value for GSM2309: insulin resistant sample pool 5 muscle on HuFL; src: muscle 6 Value for GSM2313: insulin sensitive sample pool 1 muscle on HuFL; src: muscle 7 Value for GSM2318: insulin sensitive sample pool 2 muscle on HuFL; src: muscle 8 Value for GSM2323: insulin sensitive sample pool 3 muscle on HuFL; src: muscle 9 Value for GSM2328: insulin sensitive sample pool 4 muscle on HuFL; src: muscle 10 Value for GSM2333: insulin sensitive sample pool 5 muscle on HuFL; src: muscle Now I start to see that the collection of samples may be viewed as falling into two classes. If you want to use wilcox.test to address a two- sample problem arising from this experiment, you will have to use the information shown above to distinguish numerical values on gene expression into the classes. There is more than enough information in the above to begin this process; for biological interpretation you need to know a little more: you will need to know the GPL80 is documented in the package hu6800.db. On Tue, Mar 20, 2012 at 7:24 AM, Ovokeraye Achinike-Oduaran < ovokeraye@gmail.com> wrote: > Hi all, > > I am not quite sure how to use the expression set I get from getGEO(), > say gds157, in wilcox.test(). > > Please help. > > Thanks. > > Avoks > > _______________________________________________ > 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 > [[alternative HTML version deleted]]
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On Tue, Mar 20, 2012 at 7:56 AM, Vincent Carey <stvjc@channing.harvard.edu>wrote: > Please read the posting guide > http://www.bioconductor.org/help/mailing-list/posting-guide/ before > querying this list. > > You have not given any information on how you have used getGEO. To help > you, I issued > > > library(GEOquery) > Setting options('download.file.method.GEOquery'='auto') > > gg = getGEO("GDS157") > File stored at: > > /var/folders/4D/4DI98FkjGzq0K2niUTEHSE+++TM/-Tmp-//RtmpGnz9Cf/GDS157 .soft.gz > > gg > An object of class "GDS" > At this point, if you would like to work with an ExpressionSet instead of a GDS object, try: expset = GDS2eSet(gg) Sean > channel_count > [1] "1" > dataset_id > [1] "GDS157" "GDS157" > description > [1] "Analysis of gene expression in pooled vastus lateralis muscle samples > from insulin-sensitive and insulin-resistant equally obese, non- diabetic > Pima Indians. A search for susceptibility genes for type 2 diabetes. " > ... > > > getClass("GDS") > Class "GDS" [package "GEOquery"] > > Slots: > > Name: gpl dataTable header > Class: GPL GEODataTable list > > Extends: "GEOData" > > getClass("GEODataTable") > Class "GEODataTable" [package "GEOquery"] > > Slots: > > Name: columns table > Class: data.frame data.frame > > Here I am using R's self-describing capacities to learn about what the > query returned. > > > gg@dataTable@columns > sample metabolism > 1 GSM2289 insulin resistant > 2 GSM2294 insulin resistant > 3 GSM2299 insulin resistant > 4 GSM2304 insulin resistant > 5 GSM2309 insulin resistant > 6 GSM2313 insulin sensitive > 7 GSM2318 insulin sensitive > 8 GSM2323 insulin sensitive > 9 GSM2328 insulin sensitive > 10 GSM2333 insulin sensitive > > description > 1 Value for GSM2289: insulin resistant sample pool 1 muscle on HuFL; src: > muscle > 2 Value for GSM2294: insulin resistant sample pool 2 muscle on HuFL; src: > muscle > 3 Value for GSM2299: insulin resistant sample pool 3 muscle on HuFL; src: > muscle > 4 Value for GSM2304: insulin resistant sample pool 4 muscle on HuFL; src: > muscle > 5 Value for GSM2309: insulin resistant sample pool 5 muscle on HuFL; src: > muscle > 6 Value for GSM2313: insulin sensitive sample pool 1 muscle on HuFL; src: > muscle > 7 Value for GSM2318: insulin sensitive sample pool 2 muscle on HuFL; src: > muscle > 8 Value for GSM2323: insulin sensitive sample pool 3 muscle on HuFL; src: > muscle > 9 Value for GSM2328: insulin sensitive sample pool 4 muscle on HuFL; src: > muscle > 10 Value for GSM2333: insulin sensitive sample pool 5 muscle on HuFL; src: > muscle > > Now I start to see that the collection of samples may be viewed as falling > into two classes. If you want to use wilcox.test to address a two- sample > problem arising from this experiment, you will have to use the information > shown above to distinguish numerical values on gene expression into the > classes. There is more than enough information in the above to begin this > process; for biological interpretation you need to know a little more: you > will need to know the GPL80 is documented in the package hu6800.db. > > On Tue, Mar 20, 2012 at 7:24 AM, Ovokeraye Achinike-Oduaran < > ovokeraye@gmail.com> wrote: > > > Hi all, > > > > I am not quite sure how to use the expression set I get from getGEO(), > > say gds157, in wilcox.test(). > > > > Please help. > > > > Thanks. > > > > Avoks > > > > _______________________________________________ > > 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 > > > > [[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 > [[alternative HTML version deleted]]
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Hi, Sorry about the vagueness. This is how I have retrieved my data from GEO. I'm trying to see the DE of the genes across the two conditions (IR and IS). I just couldn't figure out how to apply this info to wilcox.test() gds157dat = getGEO('GDS157',destdir=".") gds157eset = GDS2eSet(gds157dat, do.log2=TRUE) groups= pData(gds157eset)$metabolism groups=as.character(groups) groups[groups=="insulin sensitive"]= "IS" groups[groups=="insulin resistant"]= "IR" sessionInfo() R version 2.14.1 (2011-12-22) Platform: i386-pc-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=English_.1252 LC_CTYPE=English_.1252 [3] LC_MONETARY=English_.1252 LC_NUMERIC=C [5] LC_TIME=English_.1252 attached base packages: [1] stats4 splines stats graphics grDevices utils datasets [8] methods base other attached packages: [1] coin_1.0-21 modeltools_0.2-19 mvtnorm_0.9-9992 [4] survival_2.36-12 XML_3.9-4.1 RCurl_1.91-1.1 [7] bitops_1.0-4.1 puma_2.6.0 mclust_3.4.11 [10] limma_3.10.2 ArrayExpress_1.14.0 affy_1.32.1 [13] GEOquery_2.20.8 Biobase_2.14.0 loaded via a namespace (and not attached): [1] affyio_1.22.0 BiocInstaller_1.2.1 preprocessCore_1.16.0 [4] zlibbioc_1.0.0 > Regards, Avoks On Tue, Mar 20, 2012 at 2:15 PM, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > > On Tue, Mar 20, 2012 at 7:56 AM, Vincent Carey <stvjc at="" channing.harvard.edu=""> > wrote: >> >> Please read the posting guide >> http://www.bioconductor.org/help/mailing-list/posting-guide/ before >> querying this list. >> >> You have not given any information on how you have used getGEO. ?To help >> you, I issued >> >> > library(GEOquery) >> Setting options('download.file.method.GEOquery'='auto') >> > gg = getGEO("GDS157") >> File stored at: >> >> /var/folders/4D/4DI98FkjGzq0K2niUTEHSE+++TM/-Tmp-//RtmpGnz9Cf/GDS15 7.soft.gz >> > gg >> An object of class "GDS" > > > At this point, if you would like to work with an ExpressionSet instead of a > GDS object, try: > > expset = GDS2eSet(gg) > > Sean > >> >> channel_count >> [1] "1" >> dataset_id >> [1] "GDS157" "GDS157" >> description >> [1] "Analysis of gene expression in pooled vastus lateralis muscle samples >> from insulin-sensitive and insulin-resistant equally obese, non- diabetic >> Pima Indians. A search for susceptibility genes for type 2 diabetes. ? ?" >> ... >> >> > getClass("GDS") >> Class "GDS" [package "GEOquery"] >> >> Slots: >> >> Name: ? ? ? ? ? gpl ? ?dataTable ? ? ? header >> Class: ? ? ? ? ?GPL GEODataTable ? ? ? ? list >> >> Extends: "GEOData" >> > getClass("GEODataTable") >> Class "GEODataTable" [package "GEOquery"] >> >> Slots: >> >> Name: ? ? columns ? ? ?table >> Class: data.frame data.frame >> >> Here I am using R's self-describing capacities to learn about what the >> query returned. >> >> > gg at dataTable@columns >> ? ?sample ? ? ? ?metabolism >> 1 ?GSM2289 insulin resistant >> 2 ?GSM2294 insulin resistant >> 3 ?GSM2299 insulin resistant >> 4 ?GSM2304 insulin resistant >> 5 ?GSM2309 insulin resistant >> 6 ?GSM2313 insulin sensitive >> 7 ?GSM2318 insulin sensitive >> 8 ?GSM2323 insulin sensitive >> 9 ?GSM2328 insulin sensitive >> 10 GSM2333 insulin sensitive >> >> description >> 1 ?Value for GSM2289: insulin resistant sample pool 1 muscle on HuFL; src: >> muscle >> 2 ?Value for GSM2294: insulin resistant sample pool 2 muscle on HuFL; src: >> muscle >> 3 ?Value for GSM2299: insulin resistant sample pool 3 muscle on HuFL; src: >> muscle >> 4 ?Value for GSM2304: insulin resistant sample pool 4 muscle on HuFL; src: >> muscle >> 5 ?Value for GSM2309: insulin resistant sample pool 5 muscle on HuFL; src: >> muscle >> 6 ?Value for GSM2313: insulin sensitive sample pool 1 muscle on HuFL; src: >> muscle >> 7 ?Value for GSM2318: insulin sensitive sample pool 2 muscle on HuFL; src: >> muscle >> 8 ?Value for GSM2323: insulin sensitive sample pool 3 muscle on HuFL; src: >> muscle >> 9 ?Value for GSM2328: insulin sensitive sample pool 4 muscle on HuFL; src: >> muscle >> 10 Value for GSM2333: insulin sensitive sample pool 5 muscle on HuFL; src: >> muscle >> >> Now I start to see that the collection of samples may be viewed as falling >> into two classes. ?If you want to use wilcox.test to address a two- sample >> problem arising from this experiment, you will have to use the information >> shown above to distinguish numerical values on gene expression into the >> classes. ?There is more than enough information in the above to begin this >> process; for biological interpretation you need to know a little more: you >> will need to know the GPL80 is documented in the package hu6800.db. >> >> On Tue, Mar 20, 2012 at 7:24 AM, Ovokeraye Achinike-Oduaran < >> ovokeraye at gmail.com> wrote: >> >> > Hi all, >> > >> > I am not quite sure how to use the expression set I get from getGEO(), >> > say gds157, in wilcox.test(). >> > >> > Please help. >> > >> > Thanks. >> > >> > Avoks >> > >> > _______________________________________________ >> > 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 >> > >> >> ? ? ? ?[[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 > >
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On Tue, Mar 20, 2012 at 8:37 AM, Ovokeraye Achinike-Oduaran < ovokeraye@gmail.com> wrote: > Hi, > > Sorry about the vagueness. > > This is how I have retrieved my data from GEO. I'm trying to see the > DE of the genes across the two conditions (IR and IS). I just couldn't > figure out how to apply this info to wilcox.test() > > gds157dat = getGEO('GDS157',destdir=".") > gds157eset = GDS2eSet(gds157dat, do.log2=TRUE) > groups= pData(gds157eset)$metabolism > groups=as.character(groups) > groups[groups=="insulin sensitive"]= "IS" > groups[groups=="insulin resistant"]= "IR" > > First, wilcox.test works on a gene/probe at a time, so you'll need some type of looping structure (apply, for example). Second, you'll need to split your data into two vectors corresponding to the IR and IS subsets; these two vectors will be the x and y variables in wilcox.test. You might also look at the multtest package and consider using limma. Particularly since your data contain only 10 samples, rank-based methods are going to be of limited use. Sean > sessionInfo() > R version 2.14.1 (2011-12-22) > Platform: i386-pc-mingw32/i386 (32-bit) > > locale: > [1] LC_COLLATE=English_.1252 LC_CTYPE=English_.1252 > [3] LC_MONETARY=English_.1252 LC_NUMERIC=C > [5] LC_TIME=English_.1252 > > attached base packages: > [1] stats4 splines stats graphics grDevices utils datasets > [8] methods base > > other attached packages: > [1] coin_1.0-21 modeltools_0.2-19 mvtnorm_0.9-9992 > [4] survival_2.36-12 XML_3.9-4.1 RCurl_1.91-1.1 > [7] bitops_1.0-4.1 puma_2.6.0 mclust_3.4.11 > [10] limma_3.10.2 ArrayExpress_1.14.0 affy_1.32.1 > [13] GEOquery_2.20.8 Biobase_2.14.0 > > loaded via a namespace (and not attached): > [1] affyio_1.22.0 BiocInstaller_1.2.1 preprocessCore_1.16.0 > [4] zlibbioc_1.0.0 > > > > Regards, > > Avoks > > On Tue, Mar 20, 2012 at 2:15 PM, Sean Davis <sdavis2@mail.nih.gov> wrote: > > > > > > On Tue, Mar 20, 2012 at 7:56 AM, Vincent Carey < > stvjc@channing.harvard.edu> > > wrote: > >> > >> Please read the posting guide > >> http://www.bioconductor.org/help/mailing-list/posting-guide/ before > >> querying this list. > >> > >> You have not given any information on how you have used getGEO. To help > >> you, I issued > >> > >> > library(GEOquery) > >> Setting options('download.file.method.GEOquery'='auto') > >> > gg = getGEO("GDS157") > >> File stored at: > >> > >> > /var/folders/4D/4DI98FkjGzq0K2niUTEHSE+++TM/-Tmp-//RtmpGnz9Cf/GDS157 .soft.gz > >> > gg > >> An object of class "GDS" > > > > > > At this point, if you would like to work with an ExpressionSet instead > of a > > GDS object, try: > > > > expset = GDS2eSet(gg) > > > > Sean > > > >> > >> channel_count > >> [1] "1" > >> dataset_id > >> [1] "GDS157" "GDS157" > >> description > >> [1] "Analysis of gene expression in pooled vastus lateralis muscle > samples > >> from insulin-sensitive and insulin-resistant equally obese, non- diabetic > >> Pima Indians. A search for susceptibility genes for type 2 diabetes. > " > >> ... > >> > >> > getClass("GDS") > >> Class "GDS" [package "GEOquery"] > >> > >> Slots: > >> > >> Name: gpl dataTable header > >> Class: GPL GEODataTable list > >> > >> Extends: "GEOData" > >> > getClass("GEODataTable") > >> Class "GEODataTable" [package "GEOquery"] > >> > >> Slots: > >> > >> Name: columns table > >> Class: data.frame data.frame > >> > >> Here I am using R's self-describing capacities to learn about what the > >> query returned. > >> > >> > gg@dataTable@columns > >> sample metabolism > >> 1 GSM2289 insulin resistant > >> 2 GSM2294 insulin resistant > >> 3 GSM2299 insulin resistant > >> 4 GSM2304 insulin resistant > >> 5 GSM2309 insulin resistant > >> 6 GSM2313 insulin sensitive > >> 7 GSM2318 insulin sensitive > >> 8 GSM2323 insulin sensitive > >> 9 GSM2328 insulin sensitive > >> 10 GSM2333 insulin sensitive > >> > >> description > >> 1 Value for GSM2289: insulin resistant sample pool 1 muscle on HuFL; > src: > >> muscle > >> 2 Value for GSM2294: insulin resistant sample pool 2 muscle on HuFL; > src: > >> muscle > >> 3 Value for GSM2299: insulin resistant sample pool 3 muscle on HuFL; > src: > >> muscle > >> 4 Value for GSM2304: insulin resistant sample pool 4 muscle on HuFL; > src: > >> muscle > >> 5 Value for GSM2309: insulin resistant sample pool 5 muscle on HuFL; > src: > >> muscle > >> 6 Value for GSM2313: insulin sensitive sample pool 1 muscle on HuFL; > src: > >> muscle > >> 7 Value for GSM2318: insulin sensitive sample pool 2 muscle on HuFL; > src: > >> muscle > >> 8 Value for GSM2323: insulin sensitive sample pool 3 muscle on HuFL; > src: > >> muscle > >> 9 Value for GSM2328: insulin sensitive sample pool 4 muscle on HuFL; > src: > >> muscle > >> 10 Value for GSM2333: insulin sensitive sample pool 5 muscle on HuFL; > src: > >> muscle > >> > >> Now I start to see that the collection of samples may be viewed as > falling > >> into two classes. If you want to use wilcox.test to address a > two-sample > >> problem arising from this experiment, you will have to use the > information > >> shown above to distinguish numerical values on gene expression into the > >> classes. There is more than enough information in the above to begin > this > >> process; for biological interpretation you need to know a little more: > you > >> will need to know the GPL80 is documented in the package hu6800.db. > >> > >> On Tue, Mar 20, 2012 at 7:24 AM, Ovokeraye Achinike-Oduaran < > >> ovokeraye@gmail.com> wrote: > >> > >> > Hi all, > >> > > >> > I am not quite sure how to use the expression set I get from getGEO(), > >> > say gds157, in wilcox.test(). > >> > > >> > Please help. > >> > > >> > Thanks. > >> > > >> > Avoks > >> > > >> > _______________________________________________ > >> > 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 > >> > > >> > >> [[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 > > > > > > _______________________________________________ > 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 > [[alternative HTML version deleted]]
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Thanks Sean. I already used limma for my analyses. I was just trying to repeat the data analysis used in the original paper (GSE121). But I have an idea on how to proceed now. Thanks again. -Avoks On Tue, Mar 20, 2012 at 2:46 PM, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > > On Tue, Mar 20, 2012 at 8:37 AM, Ovokeraye Achinike-Oduaran > <ovokeraye at="" gmail.com=""> wrote: >> >> Hi, >> >> Sorry about the vagueness. >> >> This is how I have retrieved my data from GEO. I'm trying to see the >> DE of the genes across the two conditions (IR and IS). I just couldn't >> figure out how to apply this info to wilcox.test() >> >> gds157dat = getGEO('GDS157',destdir=".") >> gds157eset = GDS2eSet(gds157dat, do.log2=TRUE) >> groups= pData(gds157eset)$metabolism >> groups=as.character(groups) >> groups[groups=="insulin sensitive"]= "IS" >> groups[groups=="insulin resistant"]= "IR" >> > > First, wilcox.test works on a gene/probe at a time, so you'll need some type > of looping structure (apply, for example). ?Second, you'll need to split > your data into two vectors corresponding to the IR and IS subsets; these two > vectors will be the x and y variables in wilcox.test. > > You might also look at the multtest package and consider using limma. > ?Particularly since your data contain only 10 samples, rank-based methods > are going to be of limited use. > > Sean > > > > >> >> sessionInfo() >> R version 2.14.1 (2011-12-22) >> Platform: i386-pc-mingw32/i386 (32-bit) >> >> locale: >> [1] LC_COLLATE=English_.1252 ?LC_CTYPE=English_.1252 >> [3] LC_MONETARY=English_.1252 LC_NUMERIC=C >> [5] LC_TIME=English_.1252 >> >> attached base packages: >> [1] stats4 ? ?splines ? stats ? ? graphics ?grDevices utils ? ? datasets >> [8] methods ? base >> >> other attached packages: >> ?[1] coin_1.0-21 ? ? ? ? modeltools_0.2-19 ? mvtnorm_0.9-9992 >> ?[4] survival_2.36-12 ? ?XML_3.9-4.1 ? ? ? ? RCurl_1.91-1.1 >> ?[7] bitops_1.0-4.1 ? ? ?puma_2.6.0 ? ? ? ? ?mclust_3.4.11 >> [10] limma_3.10.2 ? ? ? ?ArrayExpress_1.14.0 affy_1.32.1 >> [13] GEOquery_2.20.8 ? ? Biobase_2.14.0 >> >> loaded via a namespace (and not attached): >> [1] affyio_1.22.0 ? ? ? ? BiocInstaller_1.2.1 ? preprocessCore_1.16.0 >> [4] zlibbioc_1.0.0 >> > >> >> Regards, >> >> Avoks >> >> On Tue, Mar 20, 2012 at 2:15 PM, Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: >> > >> > >> > On Tue, Mar 20, 2012 at 7:56 AM, Vincent Carey >> > <stvjc at="" channing.harvard.edu=""> >> > wrote: >> >> >> >> Please read the posting guide >> >> http://www.bioconductor.org/help/mailing-list/posting-guide/ before >> >> >> querying this list. >> >> >> >> You have not given any information on how you have used getGEO. ?To >> >> help >> >> you, I issued >> >> >> >> > library(GEOquery) >> >> Setting options('download.file.method.GEOquery'='auto') >> >> >> > gg = getGEO("GDS157") >> >> File stored at: >> >> >> >> >> >> /var/folders/4D/4DI98FkjGzq0K2niUTEHSE+++TM/-Tmp-//RtmpGnz9Cf/GD S157.soft.gz >> >> > gg >> >> An object of class "GDS" >> > >> > >> > At this point, if you would like to work with an ExpressionSet instead >> > of a >> > GDS object, try: >> > >> > expset = GDS2eSet(gg) >> > >> > Sean >> > >> >> >> >> channel_count >> >> [1] "1" >> >> dataset_id >> >> [1] "GDS157" "GDS157" >> >> description >> >> [1] "Analysis of gene expression in pooled vastus lateralis muscle >> >> samples >> >> from insulin-sensitive and insulin-resistant equally obese, >> >> non-diabetic >> >> Pima Indians. A search for susceptibility genes for type 2 diabetes. >> >> ?" >> >> ... >> >> >> >> > getClass("GDS") >> >> Class "GDS" [package "GEOquery"] >> >> >> >> Slots: >> >> >> >> Name: ? ? ? ? ? gpl ? ?dataTable ? ? ? header >> >> Class: ? ? ? ? ?GPL GEODataTable ? ? ? ? list >> >> >> >> Extends: "GEOData" >> >> > getClass("GEODataTable") >> >> Class "GEODataTable" [package "GEOquery"] >> >> >> >> Slots: >> >> >> >> Name: ? ? columns ? ? ?table >> >> Class: data.frame data.frame >> >> >> >> Here I am using R's self-describing capacities to learn about what the >> >> query returned. >> >> >> >> > gg at dataTable@columns >> >> ? ?sample ? ? ? ?metabolism >> >> 1 ?GSM2289 insulin resistant >> >> 2 ?GSM2294 insulin resistant >> >> 3 ?GSM2299 insulin resistant >> >> 4 ?GSM2304 insulin resistant >> >> 5 ?GSM2309 insulin resistant >> >> 6 ?GSM2313 insulin sensitive >> >> 7 ?GSM2318 insulin sensitive >> >> 8 ?GSM2323 insulin sensitive >> >> 9 ?GSM2328 insulin sensitive >> >> 10 GSM2333 insulin sensitive >> >> >> >> description >> >> 1 ?Value for GSM2289: insulin resistant sample pool 1 muscle on HuFL; >> >> src: >> >> muscle >> >> 2 ?Value for GSM2294: insulin resistant sample pool 2 muscle on HuFL; >> >> src: >> >> muscle >> >> 3 ?Value for GSM2299: insulin resistant sample pool 3 muscle on HuFL; >> >> src: >> >> muscle >> >> 4 ?Value for GSM2304: insulin resistant sample pool 4 muscle on HuFL; >> >> src: >> >> muscle >> >> 5 ?Value for GSM2309: insulin resistant sample pool 5 muscle on HuFL; >> >> src: >> >> muscle >> >> 6 ?Value for GSM2313: insulin sensitive sample pool 1 muscle on HuFL; >> >> src: >> >> muscle >> >> 7 ?Value for GSM2318: insulin sensitive sample pool 2 muscle on HuFL; >> >> src: >> >> muscle >> >> 8 ?Value for GSM2323: insulin sensitive sample pool 3 muscle on HuFL; >> >> src: >> >> muscle >> >> 9 ?Value for GSM2328: insulin sensitive sample pool 4 muscle on HuFL; >> >> src: >> >> muscle >> >> 10 Value for GSM2333: insulin sensitive sample pool 5 muscle on HuFL; >> >> src: >> >> muscle >> >> >> >> Now I start to see that the collection of samples may be viewed as >> >> falling >> >> into two classes. ?If you want to use wilcox.test to address a >> >> two-sample >> >> problem arising from this experiment, you will have to use the >> >> information >> >> shown above to distinguish numerical values on gene expression into the >> >> classes. ?There is more than enough information in the above to begin >> >> this >> >> process; for biological interpretation you need to know a little more: >> >> you >> >> will need to know the GPL80 is documented in the package hu6800.db. >> >> >> >> On Tue, Mar 20, 2012 at 7:24 AM, Ovokeraye Achinike-Oduaran < >> >> ovokeraye at gmail.com> wrote: >> >> >> >> > Hi all, >> >> > >> >> > I am not quite sure how to use the expression set I get from >> >> > getGEO(), >> >> > say gds157, in wilcox.test(). >> >> > >> >> > Please help. >> >> > >> >> > Thanks. >> >> > >> >> > Avoks >> >> > >> >> > _______________________________________________ >> >> > 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 >> >> > >> >> >> >> ? ? ? ?[[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 >> > >> > >> >> _______________________________________________ >> 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 > >
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