Is there any minimum number of counts in scRNAseq to consider that a gene is expressed?
0
1
Entering edit mode
3 months ago
ev97 ▴ 30

I am quite new in the scRNAseq field and I am wondering if there is any minimum number of counts of which we can consider that a gene is expressed too much or too little in a particular condition.

I am asking this particular question due that I am used to work with bulk RNAseq data and I usually consider a gene is worth to focus in if the expression is quite high (>=1000 counts for example if we have genes that they have < 100).

I am aware that I cannot make the same assumptions of expression in scRNAseq cause I am looking at particular cells and maybe a expression of 20 it is enough to infer that a X gene is expressed more in one cell than another. However, I don't know if there any guidelines or something that people usually do/consider in terms of expression (apart from that if you have cells with a expression = 0 you should remove them).

Does anybody can help me or give your insights about this?

Thanks in advance

scRNAseq single-cell seurat scanpy UMI • 386 views
ADD COMMENT
2
Entering edit mode

I am asking this particular question due that I am used to work with bulk RNAseq data and I usually consider a gene is worth to focus in if the expression is quite high (>=1000 counts for example if we have genes that they have < 100).

That is wildly too stringent to me, and also arbitrary. There is imho no reliable justification to make such a statement. Counts depend on total sequencing depth, and even after normalization longer genes in full length RNA-seq have more counts than shorter ones, so a simple (and large) cutoff like this is not getting you anywhere, other than enriching for genes with large counts.

In scRNA-seq one often says that "at least 1 count" qualifies as "expressed" for QC, e.g. total number of expressed (or rather detected) genes, but if you really want to infer biology this is as arbitrary as the above cutoff.

I try to avoid the term "expressed" if possible as you really cannot tell, and just do differential analysis between groups, and then call it "over", "under", "not differentially"-expressed.

Just do what you can justify and what serves your analysis.

ADD REPLY
0
Entering edit mode

Thanks a lot for the feedback! I will take it into account.

ADD REPLY

Login before adding your answer.

Traffic: 1154 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6