Hello everybody, I have a table with some microarray experiments which look like that: my "genelist.txt" Probe Id NAME FC_set1 FC_set2 FC_set3 FC_set4 A_51_P100021 Hivep3 1.048368 -1.085207 -1.013457 1.032816 A_51_P100034 Mif4gd -1.049719 -1.077773 -1.084012 -1.004941 A_51_P100052 Slitrk2 1.339832 1.063053 -1.157675 -1.003128 A_51_P100063 Lnx1 1.073604 1.010892 -1.058375 1.063377 A_51_P100084 Unknown 1.084544 -1.258876 -1.092571 -1.058791 ... the Probe Ids are from the Agilent expression arrays. I extracted the names using BiomaRt and now I would like to find whether there are some overrepresented gene sets in the differentially regulated genes. For once I would like to see if there are any GO terms which are overrepresented in these gene lists for each of the columns (gene sets). Secondly i would like to search for accumulations of other gene sets of differentially regulated genes in these lists (for example kinases, transcription factors, but also localization, protein domain etc.) I would like your help in creating the gene sets of either GO terms or the other parameters. I know I can extract the data from BiomaRt to each and every gen. for example: mart <- useMart("ensembl") ensembl <- useDataset("mmusculus_gene_ensembl", mart = mart) test <- read.delim("genelist.txt") geneset1 <- read.delim("geneset1_all_signal.txt") genes <- as.character(geneset1[,1]) geneNames <- getBM(attributes = c("go_biological_process_id", "name_1006", "agilent_wholegenome", "external_gene_id", "ensembl_gene_id", "entrezgene"), filter = c("agilent_wholegenome"), values = geneset1, mart = ensembl) > geneNames go_biological_process_id name_1006 1 GO:0007409 axonogenesis 2 GO:0006511 ubiquitin- dependent protein catabolic process 3 GO:0051260 protein homooligomerization 4 GO:0042787 protein ubiquitination during ubiquitin- dependent protein catabolic process 5 GO:0006417 regulation of translation 6 GO:0016070 RNA metabolic process 7 GO:0016070 RNA metabolic process agilent_wholegenome external_gene_id ensembl_gene_id entrezgene 1 A_51_P100052 Slitrk2 ENSMUSG00000036790 245450 2 A_51_P100063 Lnx1 ENSMUSG00000029228 16924 3 A_51_P100063 Lnx1 ENSMUSG00000029228 16924 4 A_51_P100063 Lnx1 ENSMUSG00000029228 16924 5 A_51_P100034 Mif4gd ENSMUSG00000020743 69674 6 A_51_P100034 Mif4gd ENSMUSG00000020743 69674 7 A_51_P100034 Mif4gd ENSMUSG00000020743 NA Now I would like to create the gene sets according to these GO categories. I would like to get something like that: GO:0007409 A_51_P100052 ... the rest of the genes from this category in the list on one line GO:0016070 A_51_P100034 ... GO:0006417 A_51_P100034 ... THX for the help Assa [[alternative HTML version deleted]]

Hello, Did you try to just use DAVID as an alternative to BioC? http://david.abcc.ncifcrf.gov/summary.jsp It can do overrepresentation analysis for GO and much more, you should check it out. Just paste your list of gene, choose Agilent gene ID and you`re pretty much set. Of course, it`s a web interface and that doesn't help you if you want this as part of a bioC pipeline. Cheers, Michael On 01/07/2010 5:58 AM, Assa Yeroslaviz wrote: > 00029228 16924 > 3 A_51_P100063 Lnx1 ENSMUSG000

Hi Assa, It really sounds like you should look at the vignette titled "Hypergeometric Tests Using GOstats" from using the GOstats package. http://www.bioconductor.org/packages/release/bioc/html/GOstats.html Marc On 07/01/2010 02:58 AM, Assa Yeroslaviz wrote: > Hello everybody, > > I have a table with some microarray experiments which look like that: > my "genelist.txt" > Probe Id NAME FC_set1 FC_set2 FC_set3 FC_set4 > A_51_P100021 Hivep3 1.048368 -1.085207 -1.013457 1.032816 > A_51_P100034 Mif4gd -1.049719 -1.077773 -1.084012 -1.004941 > A_51_P100052 Slitrk2 1.339832 1.063053 -1.157675 -1.003128 > A_51_P100063 Lnx1 1.073604 1.010892 -1.058375 1.063377 > A_51_P100084 Unknown 1.084544 -1.258876 -1.092571 -1.058791 > ... > > the Probe Ids are from the Agilent expression arrays. I extracted the names > using BiomaRt and now I would like to find whether there are some > overrepresented gene sets in the differentially regulated genes. > For once I would like to see if there are any GO terms which are > overrepresented in these gene lists for each of the columns (gene sets). > Secondly i would like to search for accumulations of other gene sets of > differentially regulated genes in these lists (for example kinases, > transcription factors, but also localization, protein domain etc.) > > I would like your help in creating the gene sets of either GO terms or the > other parameters. > > I know I can extract the data from BiomaRt to each and every gen. for > example: > > mart <- useMart("ensembl") > ensembl <- useDataset("mmusculus_gene_ensembl", mart = mart) > > test <- read.delim("genelist.txt") > geneset1 <- read.delim("geneset1_all_signal.txt") > genes <- as.character(geneset1[,1]) > > geneNames <- getBM(attributes = c("go_biological_process_id", "name_1006", > "agilent_wholegenome", "external_gene_id", "ensembl_gene_id", "entrezgene"), > filter = c("agilent_wholegenome"), values = geneset1, mart = ensembl) > > >> geneNames >> > go_biological_process_id > name_1006 > 1 > GO:0007409 > axonogenesis > 2 GO:0006511 ubiquitin- dependent > protein catabolic process > 3 GO:0051260 > protein homooligomerization > 4 GO:0042787 protein ubiquitination during ubiquitin- dependent > protein catabolic process > 5 GO:0006417 > regulation of translation > 6 > GO:0016070 RNA > metabolic process > 7 > GO:0016070 RNA > metabolic process > agilent_wholegenome external_gene_id ensembl_gene_id entrezgene > 1 A_51_P100052 Slitrk2 ENSMUSG00000036790 245450 > 2 A_51_P100063 Lnx1 ENSMUSG00000029228 16924 > 3 A_51_P100063 Lnx1 ENSMUSG00000029228 16924 > 4 A_51_P100063 Lnx1 ENSMUSG00000029228 16924 > 5 A_51_P100034 Mif4gd ENSMUSG00000020743 69674 > 6 A_51_P100034 Mif4gd ENSMUSG00000020743 69674 > 7 A_51_P100034 Mif4gd ENSMUSG00000020743 NA > > Now I would like to create the gene sets according to these GO categories. I > would like to get something like that: > > GO:0007409 A_51_P100052 ... the rest of the genes from this category in the > list on one line > GO:0016070 A_51_P100034 ... > GO:0006417 A_51_P100034 ... > > THX for the help > > Assa > > [[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 > >