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If you don’t proceed below with correcting the data with sc.pp.regress_out and scaling it via sc.pp.scale, you can also get away without using .raw at all.
The result of the previous highly-variable-genes detection is stored as an annotation in .var.highly_variable and auto-detected by PCA and hence, sc.pp.neighbors and subsequent manifold/graph tools. In that case, the step actually do the filtering below is unnecessary, too.
Since data sizes these days are big enough that a sparse .X is all but necessary (pp.scale densifies data), and methods exist that work with unscaled expression values and therefore don‘t need scaling, people tend to not do it these days.
but we still need to deal with many old singcell data, which may bot contain that many cells or not tat big enough, so can I scale all the time, no matter it is big or small
Please make sure these conditions are met
What happened?
Thanks a lot
when I read docs in
https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering-2017.html (legacy, used pp.scale)
https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering.html#nearest-neighbor-graph-constuction-and-visualization ( not used pp.scale)
so does it mean in scanpy, not matther single-cell rna-seq or spatial data, both not need the pp.scale
Minimal code sample
Error output
No response
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