Comments (2)
Hi Yu,
I think you're right with point 2.
For point 1, briefly, what happens in quantileNorm()
is that we label cells by the max factor loading (e.g. cluster 1 if factor 1 loads the most for a cell), and then align the factor loadings of cells assigned to the same "cluster". There is a step of NN searching where the graph is used to refine the previously described cluster voting before aligning cells with quantile normalization. So here the graph is indeed used for clustering cells, but this clustering is not the primary purpose of the whole thing, i.e. it is not what users usually look for when they want clustering labels for downstream analysis. Hope this explains, and I'll try to update the documentation in future versions so it causes less confusion.
Thanks,
Yichen
from liger.
Hi,
Thanks for your quick response! Yeah, I'm totaly clear about quantileNorm, but just confused when the first glance on documentation. Thanks again.
Yu
from liger.
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