Dear list,
I have search extensively old postings but could not find an easy
explanation on how to link my gene list generated after a diferential
expression analysis with limma to a kegg pathway analysis. Any help
would be
greatly appreciated. Please keep in mind that I am a molecular
biologist
that can navigate through R , but it is always a challenge, therefore
details and or examples are very helpful!
cheers,
--
Marcos B. Pinho
Programa de Engenharia Química - PEQ
Laboratório de Engenharia de Cultivos Celulares- LECC
Universidade Federal do Rio de Janeiro - UFRJ
Instituto Nacional de Câncer - INCA
Rio de Janeiro - Brasil
[[alternative HTML version deleted]]
Hi Marcos,
I think that the most straightforward way to do this would be to use
the
Annotation Packages. You can read the vignette for AnnotationDbi to
see
more here:
http://www.bioconductor.org/packages/release/bioc/html/AnnotationDbi.h
tml
You didn't give me anything specific, so that makes it hard to answer
your question, but let's suppose that you are using the platform
hgu95av2 from Affy:
# 1st you would load the library for the corresponding annotation:
library(hgu95av2.db)
# then you would have some probes, lets call them yourProbes
yourProbes= c("1000_at", "1001_at", "1002_f_at")
# then you could look up the KEGG pathway IDs doing something like
this:
mget(yourProbes, hgu95av2PATH, ifnotfound=NA)
hope this helps,
Marc
Marcos Pinho wrote:
> Dear list,
>
> I have search extensively old postings but could not find an easy
> explanation on how to link my gene list generated after a
diferential
> expression analysis with limma to a kegg pathway analysis. Any help
would be
> greatly appreciated. Please keep in mind that I am a molecular
biologist
> that can navigate through R , but it is always a challenge,
therefore
> details and or examples are very helpful!
>
> cheers,
>
>
> --------------------------------------------------------------------
----
>
> _______________________________________________
> 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
On Thu, Feb 4, 2010 at 10:39 AM, Marcos Pinho
<pinho.microarray at="" gmail.com=""> wrote:
> Dear list,
>
> I have search extensively old postings but could not find an easy
> explanation on how to link my gene list generated after a
diferential
> expression analysis with limma to a kegg pathway analysis. Any help
would be
> greatly appreciated. Please keep in mind that I am a molecular
biologist
> that can navigate through R , but it is always a challenge,
therefore
> details and or examples are very helpful!
Hi, Marcos. What array type are you using? Also, when writing to the
list, it is always a good idea to include the output of sessionInfo(),
just so that we are all on the same page when answering.
Sean
Hi, Marcos
Please check attached sample pdf file. If that is what you want, you
can use
GeneAnswers to do that. The essential input is a genelist with
optional
values, like foldchange, p-value, etc. And GeneAnswers can run
enrichment
test and draw a concept-gene network with a concept-gene cross table,
if you
have an expression profile, to show how your genes are potentially
connected
to KEGG pathways. You can find examples and codes at
http://www.bioconductor.org/packages/release/bioc/html/GeneAnswers.htm
l
Also, this attached sample pdf file is interactively generated, which
means
you can adjust the layout for your purpose.
Let me know if you have any question.
Thanks
Gilbert
On 2/4/10 9:39 AM, "Marcos Pinho" <pinho.microarray at="" gmail.com="">
wrote:
> Dear list,
>
> I have search extensively old postings but could not find an easy
> explanation on how to link my gene list generated after a
diferential
> expression analysis with limma to a kegg pathway analysis. Any help
would be
> greatly appreciated. Please keep in mind that I am a molecular
biologist
> that can navigate through R , but it is always a challenge,
therefore
> details and or examples are very helpful!
>
> cheers,
Dear Gilbert,
thank you so much for your posting. I got really interested in
learning how
to use the package GeneAnswers, but still find somewhat challenging to
navigate in R from my gene list to geneanswer. Please see below my
sension
info. Any help would be greatly appreciated!
regards,
Marcos B. Pinho
Programa de Engenharia Química - PEQ
Laboratório de Engenharia de Cultivos Celulares- LECC
Universidade Federal do Rio de Janeiro - UFRJ
Instituto Nacional de Câncer - INCA
Rio de Janeiro - Brasil
Welcome to Bioconductor
Vignettes contain introductory material. To view, type
'openVignette()'. To cite Bioconductor, see
'citation("Biobase")' and for packages 'citation(pkgname)'.
> library(tkWidgets)
Loading required package: widgetTools
Loading required package: tcltk
Loading Tcl/Tk interface ... done
Loading required package: DynDoc
> data=ReadAffy(widget=TRUE)
> library(gcrma)
Loading required package: matchprobes
Loading required package: splines
> eset=gcrma(data)
Adjusting for optical effect....Done.
Computing affinities.Done.
Adjusting for non-specific binding....Done.
Normalizing
Calculating Expression
> library(genefilter)
Loading required package: survival
> library (hgu133plus2.db)
Loading required package: AnnotationDbi
Loading required package: DBI
> esetF = nsFilter (eset, require.entrez=TRUE,remove.dupEntrez=TRUE,
feature.exclude="^AFFX",var.cutof=0.5)$eset
> design = model.matrix(~factor(rep(1:2,each=2)))
> colnames(design)=c("K562", "Lucena")
> design
K562 Lucena
1 1 0
2 1 0
3 1 1
4 1 1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$`factor(rep(1:2, each = 2))`
[1] "contr.treatment"
> library(limma)
> fit =lmFit (esetF, design)
> fit2=eBayes(fit)
> library(annotate)
Loading required package: xtable
Attaching package: 'xtable'
The following object(s) are masked from package:widgetTools :
label
> fit2$genes$Symbol=getSYMBOL(fit2$genes$ID, "hgu133plus2")
> fit2$genes$GeneName <- unlist(mget(fit$genes$ID,
hgu133plus2GENENAME))
> fit2$genes$EG <- getEG(fit2$genes$ID, "hgu133plus2")
> topTable(fit2, coef=2)
ID Symbol
5253 209993_at ABCB1
3378 1553436_at MUC19
9828 206488_s_at CD36
6995 222392_x_at PERP
8973 210603_at ARD1B
1412 235683_at SESN3
7573 216191_s_at TRD@
4013 202948_at IL1R1
5302 205934_at PLCL1
4061 205786_s_at ITGAM
GeneName
EG
5253 ATP-binding cassette, sub-family B (MDR/TAP), member 1
5243
3378 mucin 19, oligomeric
283463
9828 CD36 molecule (thrombospondin receptor)
948
6995 PERP, TP53 apoptosis effector
64065
8973 ARD1 homolog B (S. cerevisiae)
84779
1412 sestrin 3
143686
7573 T cell receptor delta locus
6964
4013 interleukin 1 receptor, type I
3554
5302 phospholipase C-like 1
5334
4061 integrin, alpha M (complement component 3 receptor 3 subunit)
3684
logFC AveExpr t P.Value adj.P.Val B
5253 8.167898 6.410285 28.41936 1.387084e-06 0.008649451 5.223275
3378 7.512443 6.097913 25.70956 2.249499e-06 0.008649451 4.992571
9828 7.318355 6.081049 22.79634 4.015521e-06 0.008649451 4.678934
6995 6.047929 6.736659 20.28712 7.035669e-06 0.008649451 4.336022
8973 6.088776 5.773605 20.23547 7.122355e-06 0.008649451 4.328102
1412 6.589716 5.620394 20.17661 7.222719e-06 0.008649451 4.319030
7573 -6.153962 5.640399 -19.71517 8.071494e-06 0.008649451 4.246141
4013 -6.231328 5.580407 -19.61010 8.281240e-06 0.008649451 4.229097
5302 5.836512 5.809832 19.53496 8.435274e-06 0.008649451 4.216804
4061 -6.788839 7.957034 -18.03301 1.238036e-05 0.008649451 3.951528
On Thu, Feb 4, 2010 at 4:47 PM, Gilbert Feng
<g-feng@northwestern.edu>wrote:
> Hi, Marcos
>
> Please check attached sample pdf file. If that is what you want, you
can
> use
> GeneAnswers to do that. The essential input is a genelist with
optional
> values, like foldchange, p-value, etc. And GeneAnswers can run
enrichment
> test and draw a concept-gene network with a concept-gene cross
table, if
> you
> have an expression profile, to show how your genes are potentially
> connected
> to KEGG pathways. You can find examples and codes at
> http://www.bioconductor.org/packages/release/bioc/html/GeneAnswers.h
tml
>
> Also, this attached sample pdf file is interactively generated,
which means
> you can adjust the layout for your purpose.
>
> Let me know if you have any question.
>
> Thanks
>
> Gilbert
>
>
> On 2/4/10 9:39 AM, "Marcos Pinho" <pinho.microarray@gmail.com>
wrote:
>
> > Dear list,
> >
> > I have search extensively old postings but could not find an easy
> > explanation on how to link my gene list generated after a
diferential
> > expression analysis with limma to a kegg pathway analysis. Any
help would
> be
> > greatly appreciated. Please keep in mind that I am a molecular
biologist
> > that can navigate through R , but it is always a challenge,
therefore
> > details and or examples are very helpful!
> >
> > cheers,
>
>
>
>
>
--
[[alternative HTML version deleted]]
http://bioinformatics.iah.ac.uk/sample-code
-----Original Message-----
From: bioconductor-bounces@stat.math.ethz.ch [mailto:bioconductor-
bounces@stat.math.ethz.ch] On Behalf Of Marcos Pinho
Sent: 08 February 2010 13:22
To: Gilbert Feng; bioconductor at stat.math.ethz.ch
Subject: Re: [BioC] link to KEGG
Dear Gilbert,
thank you so much for your posting. I got really interested in
learning how
to use the package GeneAnswers, but still find somewhat challenging to
navigate in R from my gene list to geneanswer. Please see below my
sension
info. Any help would be greatly appreciated!
regards,
Marcos B. Pinho
Programa de Engenharia Qu?mica - PEQ
Laborat?rio de Engenharia de Cultivos Celulares- LECC
Universidade Federal do Rio de Janeiro - UFRJ
Instituto Nacional de C?ncer - INCA
Rio de Janeiro - Brasil
Welcome to Bioconductor
Vignettes contain introductory material. To view, type
'openVignette()'. To cite Bioconductor, see
'citation("Biobase")' and for packages 'citation(pkgname)'.
> library(tkWidgets)
Loading required package: widgetTools
Loading required package: tcltk
Loading Tcl/Tk interface ... done
Loading required package: DynDoc
> data=ReadAffy(widget=TRUE)
> library(gcrma)
Loading required package: matchprobes
Loading required package: splines
> eset=gcrma(data)
Adjusting for optical effect....Done.
Computing affinities.Done.
Adjusting for non-specific binding....Done.
Normalizing
Calculating Expression
> library(genefilter)
Loading required package: survival
> library (hgu133plus2.db)
Loading required package: AnnotationDbi
Loading required package: DBI
> esetF = nsFilter (eset, require.entrez=TRUE,remove.dupEntrez=TRUE,
feature.exclude="^AFFX",var.cutof=0.5)$eset
> design = model.matrix(~factor(rep(1:2,each=2)))
> colnames(design)=c("K562", "Lucena")
> design
K562 Lucena
1 1 0
2 1 0
3 1 1
4 1 1
attr(,"assign")
[1] 0 1
attr(,"contrasts")
attr(,"contrasts")$`factor(rep(1:2, each = 2))`
[1] "contr.treatment"
> library(limma)
> fit =lmFit (esetF, design)
> fit2=eBayes(fit)
> library(annotate)
Loading required package: xtable
Attaching package: 'xtable'
The following object(s) are masked from package:widgetTools :
label
> fit2$genes$Symbol=getSYMBOL(fit2$genes$ID, "hgu133plus2")
> fit2$genes$GeneName <- unlist(mget(fit$genes$ID,
hgu133plus2GENENAME))
> fit2$genes$EG <- getEG(fit2$genes$ID, "hgu133plus2")
> topTable(fit2, coef=2)
ID Symbol
5253 209993_at ABCB1
3378 1553436_at MUC19
9828 206488_s_at CD36
6995 222392_x_at PERP
8973 210603_at ARD1B
1412 235683_at SESN3
7573 216191_s_at TRD@
4013 202948_at IL1R1
5302 205934_at PLCL1
4061 205786_s_at ITGAM
GeneName
EG
5253 ATP-binding cassette, sub-family B (MDR/TAP), member 1
5243
3378 mucin 19, oligomeric
283463
9828 CD36 molecule (thrombospondin receptor)
948
6995 PERP, TP53 apoptosis effector
64065
8973 ARD1 homolog B (S. cerevisiae)
84779
1412 sestrin 3
143686
7573 T cell receptor delta locus
6964
4013 interleukin 1 receptor, type I
3554
5302 phospholipase C-like 1
5334
4061 integrin, alpha M (complement component 3 receptor 3 subunit)
3684
logFC AveExpr t P.Value adj.P.Val B
5253 8.167898 6.410285 28.41936 1.387084e-06 0.008649451 5.223275
3378 7.512443 6.097913 25.70956 2.249499e-06 0.008649451 4.992571
9828 7.318355 6.081049 22.79634 4.015521e-06 0.008649451 4.678934
6995 6.047929 6.736659 20.28712 7.035669e-06 0.008649451 4.336022
8973 6.088776 5.773605 20.23547 7.122355e-06 0.008649451 4.328102
1412 6.589716 5.620394 20.17661 7.222719e-06 0.008649451 4.319030
7573 -6.153962 5.640399 -19.71517 8.071494e-06 0.008649451 4.246141
4013 -6.231328 5.580407 -19.61010 8.281240e-06 0.008649451 4.229097
5302 5.836512 5.809832 19.53496 8.435274e-06 0.008649451 4.216804
4061 -6.788839 7.957034 -18.03301 1.238036e-05 0.008649451 3.951528
On Thu, Feb 4, 2010 at 4:47 PM, Gilbert Feng <g-feng at="" northwestern.edu="">wrote:
> Hi, Marcos
>
> Please check attached sample pdf file. If that is what you want, you
can
> use
> GeneAnswers to do that. The essential input is a genelist with
optional
> values, like foldchange, p-value, etc. And GeneAnswers can run
enrichment
> test and draw a concept-gene network with a concept-gene cross
table, if
> you
> have an expression profile, to show how your genes are potentially
> connected
> to KEGG pathways. You can find examples and codes at
> http://www.bioconductor.org/packages/release/bioc/html/GeneAnswers.h
tml
>
> Also, this attached sample pdf file is interactively generated,
which means
> you can adjust the layout for your purpose.
>
> Let me know if you have any question.
>
> Thanks
>
> Gilbert
>
>
> On 2/4/10 9:39 AM, "Marcos Pinho" <pinho.microarray at="" gmail.com="">
wrote:
>
> > Dear list,
> >
> > I have search extensively old postings but could not find an easy
> > explanation on how to link my gene list generated after a
diferential
> > expression analysis with limma to a kegg pathway analysis. Any
help would
> be
> > greatly appreciated. Please keep in mind that I am a molecular
biologist
> > that can navigate through R , but it is always a challenge,
therefore
> > details and or examples are very helpful!
> >
> > cheers,
>
>
>
>
>
--
[[alternative HTML version deleted]]
Marcos Pinho wrote:
> Dear list,
>
> I have search extensively old postings but could not find an easy
> explanation on how to link my gene list generated after a
diferential
> expression analysis with limma to a kegg pathway analysis. Any help
would be
> greatly appreciated. Please keep in mind that I am a molecular
biologist
> that can navigate through R , but it is always a challenge,
therefore
> details and or examples are very helpful!
>
> cheers,
>
Have a look at the KEGGgraph package, the vignette has a complete
example of how to do this.
cheers Peter
--
Dr. med. Peter N. Robinson, MSc.
Institut f?r Medizinische Genetik
Charit? - Universit?tsmedizin Berlin
Humboldt-Universit?t
Augustenburger Platz 1
13353 Berlin
Germany
voice: 49-30-450566042
fax: 49-30-450569915
email: peter.robinson at charite.de
http://compbio.charite.de/http://www.human-phenotype-ontology.org
