Web Reference: The procedure in scanpy models the mean-variance relationship inherent in single-cell data, and is implemented in the sc.pp.highly_variable_genes function. By default, 2,000 genes (features) per dataset are returned and these will be used in downstream analysis, like PCA. Other useful option is to normalize the gene expression using sc.pp.scale. Here we store this information under the scale layer. Afterwards we adjust the plot min and max and use a diverging color map (in this case RdBu_r where _r means reversed). The shifted logarithm can be conveniently called with scanpy by running pp.normalize_total with target_sum=None. We are setting the inplace parameter to False as we want to explore three different normalization techniques in this tutorial.
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