sc_toolbox.plot.average_expression_split_cluster#

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

Plot average gene expression as line plots for multiple clusters at once.

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

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

  • xlabel – x-axis label

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

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

  • figsize – Size of the figure as specified in matplotlib

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

  • rotation – x-axis label rotation

  • cols – List of colors to use for line plot

  • tick_size – Size of the ticks as specified in matplotlib

  • label_size – Size of the labels as specified in matplotlib

  • order_smooth – If greater than 1, numpy.polyfit is used 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 – Path to save the plot to

Example smooth:
../../_images/average_expression_per_cluster_smooth.png
Example raw:
../../_images/average_expression_per_cluster_raw.png