sc_toolbox.plot.average_expression#

sc_toolbox.plot.average_expression(gene_expression, genes, order, id_label='identifier', xlabel='days', cluster='all', hue=None, palette='tab:blue', figsize=(15, 6), smooth=None, rotation=None, order_smooth=None, conf_int=None, scatter=None, save=None)[source]#

Draw a line plot showing the gene expression over time. Expression values are averaged by individual sample.

Parameters:

  • gene_expression – Data frame containing gene expression values

  • genes – List of genes for which individual line plots will be generated

  • order (List[str]) – Order of x-axis labels from left to right

  • id_label (str) – Adata column in which sample id information is stored

  • xlabel (str) – x-axis label

  • cluster (str) – Which clusters to plot. Select ‘all” if all clusters should be drawn.

  • hue – Which value to color by

  • figsize (Tuple[int, int]) – Size of the figure as specified in matplotlib

  • smooth – Set to true for smoothened line plot using polynomial regression

  • rotation (Optional[int]) – set to True to rotate x-axis labels 90 degrees

  • order_smooth – If greater than 1, use numpy.polyfit to estimate a polynomial regression

  • conf_int – Size of the confidence interval for the regression estimate

  • scatter – Set to True to add average expression values per sample ID as dots

  • save (Optional[str]) – Path to save the plot to

Example smooth:
../../_images/average_expression_smooth.png
Example raw:
../../_images/average_expression_raw.png