I have a gene expression matrix , I would like to extract a submatrix of genes annotated by a GO term. I use the R code to have a list of genes annotated by this term:
mart <- biomaRt::useMart(biomart = "plants_mart", dataset = "athaliana_eg_gene", host = 'plants.ensembl.org')
GTOGO <- biomaRt::getBM(attributes = c( "ensembl_gene_id", "go_id"), mart = mart)
head (GTOGO)
geneList <- biomaRt::getBM(attributes = c( "ensembl_gene_id", "go_id"), filters = "go", values = "GO:......", mart = mart)
How to extract from my gene expression matrix (genes, conditions) the submatrix of these annotated genes, can I use merge as R function? Thank you
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mannoulag1
Hi mannoulag1, I think that this is what you want to do:
If you have an expression matrix (MyExpressionMatrix) with Ensembl gene IDs as rownames, then the following code will find the corresponding GO term for each gene (each gene is likely to have many associated GO terms):
geneList <- biomaRt::getBM(mart=mart, attributes=c("ensembl_gene_id", "go_id"), filter="ensembl_gene_id", values=rownames(MyExpressionMatrix), uniqueRows=TRUE)
If you then want to subset your expression matrix, it would be something like this:
MyExpressionMatrix[ which( rownames(MyExpressionMatrix) %in% geneList$ensembl_gene_id ) , ]
Hi Kevin, Thank you very much, my expression matrix has 'array element names' (....._at) as row names, how to convert it to ensembl gene id? any idea please?
I see, I believe that those are Affymetrix IDs. You may want to take a look here: Affymetrix Gene Id Format - What Does "At" Stand For?
Thanks for pasting your data and providing information about the prove IDs. Here is the solution:
This will get the Ensembl ID, GO term, and GO term evidence code for all of your genes.
biomaRt::getBM(mart=mart, attributes=c("affy_ath1_121501", "ensembl_gene_id", "go_id", "go_linkage_type"), filter="affy_ath1_121501", values=rownames(MyExpressionMatrix), uniqueRows=TRUE)
You can then filter your genes based on the GO terms. Pay close attention to the evidence codes, as these will help you to decide which GO term is more reliable. Take a further look here to understand these: Go annotation reliability ? also here: http://www.geneontology.org/page/guide-go-evidence-codes
You may also wish to consider adding external_gene_name to your output.
A full list of possible values can be obtained with listAttributes(mart)
thank you very much, with the following code I get a submatrix but with probe-ID, this can seem true?
geneList<- biomaRt::getBM(attributes = c( "ensembl_gene_id", "go_id", "affy_ath1_121501"), filters = "go", values = "GO:.....", mart = mart)
subMatrix<- MyExpressionMatrix[which(rownames(MyExpressionMatrix) %in% geneList$affy_ath1_121501 ) , ]
Hello,
If you want to replace your '_at' IDs to Ensembl IDs, then use this code:
mart <- biomaRt::useMart(biomart = "plants_mart", dataset = "athaliana_eg_gene", host = 'plants.ensembl.org')
geneList <- biomaRt::getBM(mart=mart, attributes=c("affy_ath1_121501", "ensembl_gene_id"), filter="affy_ath1_121501", values=rownames(MyExpressionMatrix), uniqueRows=TRUE)
rownames(MyExpressionMatrix)
[1] "244901_at" "244902_at" "244903_at" "244904_at" "244906_at"
[6] "244912_at" "244920_s_at" "244921_s_at" "244926_s_at" "244928_s_at"
rownames(MyExpressionMatrix) <- geneList[match(rownames(MyExpressionMatrix), geneList$affy_ath1_121501), "ensembl_gene_id"]
rownames(MyExpressionMatrix)
[1] "ATMG00640" "ATMG00650" "ATMG00660" "ATMG00670" "ATMG00690" "ATMG00830"
[7] "ATMG00990" "ATMG01000" "ATMG00520" "ATMG00570"
The use of the GO terms is complicated by the fact that each gene has multiple GO terms assigned to it.
Does this help?
Hi Kevin, yes , thank you very much
Let me know if you still need help with the GO terms.
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Unless I misunderstood OP, I think you should do the other way round. First subset genes of interest from the expression matrix, then do GO analysis and then make a final matrix (with expression values and GO terms).
Hi, thank you. but how to subset genes of interest? my idea is to extract the genes list according to the GO term.
Please post a few lines (10) from your expression matrix. In general, genes of statistical significance (with lowest adjusted p-values) will be taken for analysis and many people also consider fold change for differential expression. To select for top represented GO terms, you may need to filter your list on some criteria that prevents noise.
ok, my matrix is large, I poste here only 10 lignes (genes) and 4 conditions:
I have pasted an answer in my original answer (below).
They are not genes, they are Affymetrix probes. I guess you already extracted corresponding genes using either Affymetrix tools or BiomaRt (or any other tool). For probe definition look here: http://www.affymetrix.com/support/help/IVT_glossary/index.affx. If you have probes in a list, you can simply use bash solution or you can follow the R solution proposed below post by Kevin.
significant probes:
Expression matrix:
command and output: