sc_toolbox.plot.average_expression_per_cluster#

sc_toolbox.plot.average_expression_per_cluster(gene_expression, genes, order, obs=None, id_label='identifier', xlabel='days', cluster='all', hue=None, figsize=(15, 6), smooth=None, rotation=None, tick_size=12, label_size=15, order_smooth=None, conf_int=None, palette=None, scatter=None, save=None)[source]#

Plots gene expression over time split by cluster identity.

One line per cluster.

Parameters:

  • gene_expression – Data frame containing gene expression values

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

  • order – Order of x-axis labels from left to right

  • obs – Data frame containing meta data information

  • xlabel (str) – x-axis label

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

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

  • hue – Split expression values by this grouping, one line per category will be drawn

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

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

  • rotation – Set to True to rotate x-axis labels 90 degrees

  • tick_size (int) – Size of the ticks as specified in matplotlib

  • label_size (int) – Size of the labels as specified in matplotlib

  • 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

  • palette – Color palette that gets passed to Seaborn’s lineplot. For example a list of colors.

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

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