Question: GO enrichment for genome-wide analysis
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13.9 years ago by
burak kutlu200
burak kutlu200 wrote:
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ADD COMMENTlink modified 13.9 years ago by Ting-Yuan Liu FHCRC780 • written 13.9 years ago by burak kutlu200
Answer: GO enrichment for genome-wide analysis
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13.9 years ago by
burak kutlu200
burak kutlu200 wrote:
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ADD COMMENTlink written 13.9 years ago by burak kutlu200
Answer: GO enrichment for genome-wide analysis
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gravatar for James W. MacDonald
13.9 years ago by
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James W. MacDonald52k wrote:
burak kutlu wrote: > Hi, My understanding is that GOstats implements the GO term > enrichment analysis for studies using microarrays (where the lib > argument is passed to "GOHyperG" to define the microarray-specific > environment, therefore the gene universe). I was wondering if there > is a readilly-available function for determining GO term significance > that uses the GO terms from the whole genome rather than the GO terms > from the genes represented on an array. I don't think you will find this anywhere, because it doesn't make sense. The idea behind GOHyperG is similar to the canonical 'ball and urn' scenario used in basic stats to explain the Hypergeometric distribution. The goal is to determine the probability of reaching into an urn containing a certain number of black and white balls, removing x balls and having n of those balls be white. Your question is akin to asking the probability of reaching into an urn containing black and white balls, removing x balls and having n of those balls be white, but based on the relative proportion of black and white balls in the world, instead of the proportion of black and white balls in the urn. Since the proportion of black and white balls may be quite different in the urn as compared to the world, you cannot generalize like that. Best, Jim Please point me to this > function if I have failed to find it in the documentation. Thanks > -Burak Kutlu > > --------------------------------- > > Ring in the New Year with Photo Calendars. Add photos, events, > holidays, whatever. [[alternative HTML version deleted]] > > _______________________________________________ Bioconductor mailing > list Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald University of Michigan Affymetrix and cDNA Microarray Core 1500 E Medical Center Drive Ann Arbor MI 48109 734-647-5623 ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues.
ADD COMMENTlink written 13.9 years ago by James W. MacDonald52k
On 1/10/06 8:42 AM, "James W. MacDonald" <jmacdon at="" med.umich.edu=""> wrote: > burak kutlu wrote: >> Hi, My understanding is that GOstats implements the GO term >> enrichment analysis for studies using microarrays (where the lib >> argument is passed to "GOHyperG" to define the microarray-specific >> environment, therefore the gene universe). I was wondering if there >> is a readilly-available function for determining GO term significance >> that uses the GO terms from the whole genome rather than the GO terms >> from the genes represented on an array. > > I don't think you will find this anywhere, because it doesn't make > sense. The idea behind GOHyperG is similar to the canonical 'ball and > urn' scenario used in basic stats to explain the Hypergeometric > distribution. > > The goal is to determine the probability of reaching into an urn > containing a certain number of black and white balls, removing x balls > and having n of those balls be white. > > Your question is akin to asking the probability of reaching into an urn > containing black and white balls, removing x balls and having n of those > balls be white, but based on the relative proportion of black and white > balls in the world, instead of the proportion of black and white balls > in the urn. Since the proportion of black and white balls may be quite > different in the urn as compared to the world, you cannot generalize > like that. As Jim points out, it's a bad idea to do what you propose, but if you really want to, there ARE online and standalone applications that will allow you to do this. One example is at: http://david.niaid.nih.gov/david/ease.htm But there are several others. Sean
ADD REPLYlink written 13.9 years ago by Sean Davis21k
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ADD REPLYlink written 13.9 years ago by burak kutlu200
On 1/10/06 12:39 PM, "burak kutlu" <burak_kutlu at="" yahoo.com=""> wrote: > Thanks a lot for the answer! > In that case, most of the software cited on the GO site are probably flawed. > -burak > > Sean Davis <sdavis2 at="" mail.nih.gov=""> wrote: > > > On 1/10/06 8:42 AM, "James W. MacDonald" wrote: > >> burak kutlu wrote: >>> Hi, My understanding is that GOstats implements the GO term >>> enrichment analysis for studies using microarrays (where the lib >>> argument is passed to "GOHyperG" to define the microarray-specific >>> environment, therefore the gene universe). I was wondering if there >>> is a readilly-available function for determining GO term significance >>> that uses the GO terms from the whole genome rather than the GO terms >>> from the genes represented on an array. >> >> I don't think you will find this anywhere, because it doesn't make >> sense. The idea behind GOHyperG is similar to the canonical 'ball and >> urn' scenario used in basic stats to explain the Hypergeometric >> distribution. >> >> The goal is to determine the probability of reaching into an urn >> containing a certain number of black and white balls, removing x balls >> and having n of those balls be white. >> >> Your question is akin to asking the probability of reaching into an urn >> containing black and white balls, removing x balls and having n of those >> balls be white, but based on the relative proportion of black and white >> balls in the world, instead of the proportion of black and white balls >> in the urn. Since the proportion of black and white balls may be quite >> different in the urn as compared to the world, you cannot generalize >> like that. > > As Jim points out, it's a bad idea to do what you propose, but if you really > want to, there ARE online and standalone applications that will allow you to > do this. One example is at: > > http://david.niaid.nih.gov/david/ease.htm Note that if you do use the above link, it will typically ask for a "background" set; this is meant to be a prompt to the user to think about what the actual background set should be. I realize that I may have given the wrong impression that these online tools "do it wrong". They simply allow the user to do it wrong if he/she desires. Sean
ADD REPLYlink written 13.9 years ago by Sean Davis21k
(... snip...) > > > > http://david.niaid.nih.gov/david/ease.htm > > Note that if you do use the above link, it will typically ask for a > "background" set; this is meant to be a prompt to the user to think about > what the actual background set should be. I realize that I may have given > the wrong impression that these online tools "do it wrong". They simply > allow the user to do it wrong if he/she desires. > > Sean > There are scenarios where it makes sense to use the whole genome (proteome) as a population (background) set. One example is if you are examining the GO annotations for all proteins in the proteome that have a certain type of sequence pattern. User's need to think carefully about data and parameters for any bioinformatics analysis and regard the analysis in some way as an experiment that needs good planning to get a good answer. Peter
ADD REPLYlink written 13.9 years ago by peter robinson300
Answer: GO enrichment for genome-wide analysis
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gravatar for Ting-Yuan Liu FHCRC
13.9 years ago by
Ting-Yuan Liu FHCRC780 wrote:
Hi Burak, If you use the whole-genome annotation package (such as YEAST) in the GOHyperG function, you should get what you want. HTH, Ting-Yuan ______________________________________ Ting-Yuan Liu Program in Computational Biology Division of Public Health Sciences Fred Hutchinson Cancer Research Center Seattle, WA, USA ______________________________________ On Tue, 10 Jan 2006, burak kutlu wrote: > Hi, > My understanding is that GOstats implements the GO term enrichment analysis for studies using microarrays (where the lib argument is passed to "GOHyperG" to define the microarray-specific environment, therefore the gene universe). > I was wondering if there is a readilly-available function for determining GO term significance that uses the GO terms from the whole genome rather than the GO terms from the genes represented on an array. Please point me to this function if I have failed to find it in the documentation. > Thanks > -Burak Kutlu > > > --------------------------------- > > Ring in the New Year with Photo Calendars. Add photos, events, holidays, whatever. > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >
ADD COMMENTlink written 13.9 years ago by Ting-Yuan Liu FHCRC780
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