Hi
This is raw read counts extracted by htseq from STAR alignment
> head(counts[1:2,1:4])
Sample1 Sample2 Sample3
1 ENSG00000000003 115 437 380
2 ENSG00000000005 0 0 0
> dim(counts)
[1] 58735 17
>
By this code I tried to find matched gene symbol for ensmbl gene id
> ensembl = useMart("ensembl",dataset="hsapiens_gene_ensembl")
> values=rownames(counts)
> data <- getBM(attributes=c("ensembl_gene_id", "hgnc_symbol"), filters = "ensembl_gene_id", values = values, mart= ensembl)
> merged_data = merge(x = data, y = counts, by.x = colnames(data)[1], by.y = colnames(counts)[1], all = T)
But in resulted matrix for a lot of ensmbl gene id I don't have gene symbol, when I removed them I finished with a smaller matrix
> dim(new_counts)
[1] 25052 16
>
In your experience is it normal? what should I do then? Please help me to get the right matrix for down stream analysis
Thank you
2 answers
I prefer to use the bitr function in the clusterProfiler package for mapping gene symbols to ensembl IDs. It will tell you if there is 1:1 matching or many:1 matching, and will print the percentage of non-mapped IDs. https://yulab-smu.github.io/clusterProfiler-book/chapter14.html#bitr
It is certainly possible that the dimensions will change. Especially if the Ensembl IDs in your data set come from a different release of the Ensembl annotation used to map those IDs. Why are you mapping the gene symbols first? Are you doing a targeted analysis? If you are doing a genome wide analysis I would do your DESEq analysis using the ensembl IDs and filter the genes based on some expression threshold across your samples, then add gene annotation using something like BioMart.
> ids <- bitr(hg, fromType="ENSEMBL", toType=c("SYMBOL"), OrgDb="org.Hs.eg.db")
'select()' returned 1:many mapping between keys and columns
Warning message:
In bitr(hg, fromType = "ENSEMBL", toType = c("SYMBOL"), OrgDb = "org.Hs.eg.db") :
55.7% of input gene IDs are fail to map...
Also htseq returns gene name by --idattr=gene_name , again a lot of non coding genes like AL390728.6. I am not sure should I ignore them like I am doing with id conversion or not
Don't you think I am losing too much information by this conversion?
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See the answer here: What is the difference between transcript id and Ensembl gene id
Do you have transcript variants for genes in your list? That can explain the larger numbers you initially had.
Also see: ENSEMBL annotation file for quantification: which file to use?