Hi everyone i did a GO enrichment analysis using GOstat package for
around
112 genes and got 80 go terms enriched in BP ontology ... but the
results
are not showing what are the genes that are associated with a
particular GO
term ... command used are as followd
library("hgu133plus2.db")
allg <- get("hgu133plus2ENTREZID")
allg <- as.data.frame(unlist(as.list(allg)))
entrez.ids <- unique(allg[rownames(dat.s),])
params <- new("GOHyperGParams", geneIds=entrez.ids,
annotation=c("hgu133plus2"), ontology="BP", pvalueCutoff=0.05,
conditional=FALSE, testDirection="over")
resultBP<-hyperGTest(params)
please help to find out the genes associated with the go terms
Rohit
--
Rohit Farmer
M.Tech Bioinformatics
Department of Computational Biology and Bioinformatics
Jacob School of Biengineering and Biotechnology
Sam Higginbottom Institute of Agriculture, Technology and Sciences
(Formerly known as Allahabad Agricultural Institute - Deemed
University)
Allahabad, UP, INDIA - 211 007
Ph. No. 9839845093, 9415261403
e-Mail rohit.farmer@gmail.com
Blog http://rohitsspace.blogspot.com
[[alternative HTML version deleted]]
Hi Rohit,
Rohit Farmer wrote:
> Hi everyone i did a GO enrichment analysis using GOstat package for
around
> 112 genes and got 80 go terms enriched in BP ontology ... but the
results
> are not showing what are the genes that are associated with a
particular GO
> term ... command used are as followd
>
> library("hgu133plus2.db")
> allg <- get("hgu133plus2ENTREZID")
> allg <- as.data.frame(unlist(as.list(allg)))
> entrez.ids <- unique(allg[rownames(dat.s),])
>
> params <- new("GOHyperGParams", geneIds=entrez.ids,
> annotation=c("hgu133plus2"), ontology="BP", pvalueCutoff=0.05,
> conditional=FALSE, testDirection="over")
> resultBP<-hyperGTest(params)
probesets <- probeSetSummary(resultBP)
See ?probeSetSummary for more info.
Best,
Jim
>
> please help to find out the genes associated with the go terms
>
> Rohit
--
James W. MacDonald, M.S.
Biostatistician
Douglas Lab
University of Michigan
Department of Human Genetics
5912 Buhl
1241 E. Catherine St.
Ann Arbor MI 48109-5618
734-615-7826
**********************************************************
Electronic Mail is not secure, may not be read every day, and should
not be used for urgent or sensitive issues
Hi,
On Thu, Jun 3, 2010 at 9:03 AM, James W. MacDonald
<jmacdon at="" med.umich.edu=""> wrote:
> Hi Rohit,
>
> Rohit Farmer wrote:
>>
>> Hi everyone i did a GO enrichment analysis using GOstat package for
around
>> 112 genes and got 80 go terms enriched in BP ontology ... but the
results
>> are not showing what are the genes that are associated with a
particular
>> GO
>> term ... command used are as followd
>>
>> library("hgu133plus2.db")
>> allg <- get("hgu133plus2ENTREZID")
>> allg <- as.data.frame(unlist(as.list(allg)))
>> entrez.ids <- unique(allg[rownames(dat.s),])
>>
>> params <- new("GOHyperGParams", geneIds=entrez.ids,
>> annotation=c("hgu133plus2"), ontology="BP", pvalueCutoff=0.05,
>> conditional=FALSE, testDirection="over")
>> resultBP<-hyperGTest(params)
>
> probesets <- probeSetSummary(resultBP)
>
> See ?probeSetSummary for more info.
Also, in some cases `geneIdsByCategory` could be useful as well. You
can use it to extract the entrez ids of the genes that you find
enriched from your HyperGResult, eg. assuming you found "GO:0010468"
being enriched in your test:
R> regulate.gene.expression <- geneIdsByCategory(resultBP,
'GO:0010468')
will provide you with which genes those are.
I thought I'd just mention it, since "knowing is half the battle" ...
and it's not mentioned in the "See Also" section of probeSetSummary
(shouldn't it be?), so you might not find it straight away.
-steve
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
Dear BioC,
Could you please let me know, how can I find out from given list of
ProbeIDs:
Which Probe's location is close to 3 Prime END of DNA?
How its different....if location is mapped to POSITIVE or Negative
Strand?
Thank you so much in advance,
Saurin
Hi,
>Could you please let me know, how can I find out from given list of
ProbeIDs:
>
> Which Probe's location is close to 3 Prime END of DNA?
If you don't mind doing a little programming, you could also:
1. get the probe sequences for your array (there are bioconductor
packages for these too)
2. realign them
3. check where the land on your "genes" by getting familiar with the
GenomicFeatures package.
> How its different....if location is mapped to POSITIVE or Negative
Strand?
In all likelihood it probably won't matter since I'm pretty sure most
array protocols require (at some point) amplification of the material
that will be hybridized to the chip, which will lose any strand info
of the molecules in your sample.
I could be mistaken, though, so you might want to read up on the
details of your experiment, or perhaps wait for others to chime in.
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
Thanks Steve and Kevin,
so, I got coordinates of probe sequence alignments then if strand is
(+) then take largest "start" number and if strand is (-) then take
smallest start number which gives me closest position of probes on 3
prime end.
Saurin
--- On Tue, 6/8/10, Steve Lianoglou <mailinglist.honeypot at="" gmail.com=""> wrote:
> From: Steve Lianoglou <mailinglist.honeypot at="" gmail.com="">
> Subject: Re: [BioC] ProbeID: how to find which one is close to 3
prime end of DNA- help,
> To: saurin_jani at yahoo.com
> Cc: "bioconductor" <bioconductor at="" stat.math.ethz.ch="">
> Date: Tuesday, June 8, 2010, 11:16 AM
> Hi,
>
> >Could you please let me know, how can I find out from
> given list of ProbeIDs:
> >
> > Which Probe's location is close to 3 Prime END of
> DNA?
>
> If you don't mind doing a little programming, you could
> also:
>
> 1. get the probe sequences for your array (there are
> bioconductor
> packages for these too)
> 2. realign them
> 3. check where the land on your "genes" by getting familiar
> with the
> GenomicFeatures package.
>
> > How its different....if location is mapped to POSITIVE
> or Negative Strand?
>
> In all likelihood it probably won't matter since I'm pretty
> sure most
> array protocols require (at some point) amplification of
> the material
> that will be hybridized to the chip, which will lose any
> strand info
> of the molecules in your sample.
>
> I could be mistaken, though, so you might want to read up
> on the
> details of your experiment, or perhaps wait for others to
> chime in.
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
> | Memorial Sloan-Kettering Cancer Center
> | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>
> so, I got coordinates of probe sequence alignments then if strand is
(+) then take largest "start" number and if strand is (-) then take
smallest start number which gives me closest position of probes on 3
prime end.
I'm not sure if you are asking a question, but I can't really make
exact sense of what you're saying, since I'm left to guess at a few
things?
It sounds like you already have probeid <-> gene mappings?
And now you want to go gene-by-gene and look at where each probe for
that genes aligns to the genome?
Then you want to find the 3'-most probe in each such group?
You mention "if strand is (+)". Are you talking about the strand the
probe aligns to (then what you say is wrnog)? Or the strand of the
gene you are currently looking at (then what you say is maybe right)?
--
Steve Lianoglou
Graduate Student: Computational Systems Biology
| Memorial Sloan-Kettering Cancer Center
| Weill Medical College of Cornell University
Contact Info: http://cbio.mskcc.org/~lianos/contact
Hi, Rohit,
I wrote a short script to extract the gene associated with the
over-represented GO terms. Hope this would help. Let me know if it
doesn't work.
your code is:
> params <- new("GOHyperGParams", geneIds=entrez.ids,
> annotation=c("hgu133plus2"), ontology="BP", pvalueCutoff=0.05,
> conditional=FALSE, testDirection="over")
> resultBP<-hyperGTest(params)
>
> please help to find out the genes associated with the go terms
>
> Rohit
>
You can do the following:
p <- params
origGeneIds <- geneIds(p)
selected <- intersect(geneIds(p), universeGeneIds(p))
cat2Entrez <- categoryToEntrezBuilder(p)
## get the gene (Entrez ID) in the category
geneInCat <- lapply(as.list(summary(resultBP)[,1]),
function(goid) {
selected[selected %in% cat2Entrez[[goid]]]
} )
## if you want to convert the Entrez ID to manufacture id
x=revmap(as.list(hgu133plus2ENTREZID))
geneInCatName <- lapply(geneInCat, function(geneid) {
unlist(lapply(as.list(geneid), function(id)
sel[sel %in% x[[id]] ] ))
})
names(geneInCatName) <- summary(hgOver$result)[,1]
## return
geneInCatName
--
Chao-Jen Wong
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Avenue N., M1-B514
PO Box 19024
Seattle, WA 98109
206.667.4485
cwon2 at fhcrc.org
Oh, never mind. James and Steve have suggested better ways to do it.
On 06/03/10 10:17, Chao-Jen Wong wrote:
> Hi, Rohit,
>
> I wrote a short script to extract the gene associated with the
> over-represented GO terms. Hope this would help. Let me know if it
> doesn't work.
>
> your code is:
>
>> params <- new("GOHyperGParams", geneIds=entrez.ids,
>> annotation=c("hgu133plus2"), ontology="BP", pvalueCutoff=0.05,
>> conditional=FALSE, testDirection="over")
>> resultBP<-hyperGTest(params)
>>
>> please help to find out the genes associated with the go terms
>>
>> Rohit
>>
>>
> You can do the following:
>
> p <- params
> origGeneIds <- geneIds(p)
> selected <- intersect(geneIds(p), universeGeneIds(p))
> cat2Entrez <- categoryToEntrezBuilder(p)
> ## get the gene (Entrez ID) in the category
> geneInCat <- lapply(as.list(summary(resultBP)[,1]),
> function(goid) {
> selected[selected %in% cat2Entrez[[goid]]]
> } )
>
> ## if you want to convert the Entrez ID to manufacture id
> x=revmap(as.list(hgu133plus2ENTREZID))
> geneInCatName <- lapply(geneInCat, function(geneid) {
> unlist(lapply(as.list(geneid), function(id)
> sel[sel %in% x[[id]] ] ))
> })
> names(geneInCatName) <- summary(hgOver$result)[,1]
> ## return
> geneInCatName
>
>
>
--
Chao-Jen Wong
Program in Computational Biology
Division of Public Health Sciences
Fred Hutchinson Cancer Research Center
1100 Fairview Avenue N., M1-B514
PO Box 19024
Seattle, WA 98109
206.667.4485
cwon2 at fhcrc.org
