use AverageExpression to stratify samples
I have a group of cancer patient samples for scRNA datasets. I need to stratify my samples based on the expression level of one gene (low vs high) in the tumor cells and examine their T cell components.
Is it reasonable to extract the tumor cell cluster and rank the gene expression level using AverageExpression function? I will choose the 1st and 4th quartile or the top and bottom half for following comparisons.
Not sure it is a bias method and it seems hard to find literatures with similar question.
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ndra1456 •
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You can do that, but more information would be necessary to explain your open-ended question more. Why do you think it biases analysis? As usual, scRNA-seq and bioinformatics in general is all but standardized, so you almost never find the one paper of vignette that 100% justifies what you want to do.
It concerns me because the AggregrateExpression function is also known for pseudobulk analysis and I am not sure which is the best way.
Also, considering the way of finding differentially expressed gene using FindMarkers, I am curious if I can rank the samples based on the percent expression of the gene in each sample, like they are clusters. That will give me another result too.