bioalpha.singlecell.plotting.highest_expr_genes

bioalpha.singlecell.plotting.highest_expr_genes(adata: AnnData, n_top: int = 30, show: bool | None = None, save: str | bool | None = None, ax: Axes | None = None, gene_symbols: str | None = None, log: bool = False, **kwds)

Fraction of counts assigned to each gene over all cells.

Computes, for each gene, the fraction of counts assigned to that gene within a cell. The n_top genes with the highest mean fraction over all cells are plotted as boxplots.

This plot is similar to the scater package function plotHighestExprs(type = “highest-expression”), see here. Quoting from there:

We expect to see the “usual suspects”, i.e., mitochondrial genes, actin, ribosomal protein, MALAT1. A few spike-in transcripts may also be present here, though if all of the spike-ins are in the top 50, it suggests that too much spike-in RNA was added. A large number of pseudo-genes or predicted genes may indicate problems with alignment. – Davis McCarthy and Aaron Lun

Parameters:

  • adata – Annotated data matrix.

  • n_top – Number of top

  • show – Show the plot, do not return axis.

  • save – If True or a str, save the figure. A string is appended to the default filename. Infer the filetype if ending on {‘.pdf’, ‘.png’, ‘.svg’}.

  • ax – A matplotlib axes object. Only works if plotting a single component.

  • gene_symbols – Key for field in .var that stores gene symbols if you do not want to use .var_names.

  • log – Plot x-axis in log scale

  • **kwds – Are passed to boxplot().

Return type:

If show==False a Axes.