sc_toolbox.plot.average_expression_per_cell

sc_toolbox.plot.average_expression_per_cell(gene_expression, genes, order, 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, scatter=None, cols=None, save=None)

Plots the average gene expression as a line plot per cell. Ideally used when the scatter point should not be sample wise, but cell wise. :type gene_expression: :param gene_expression: Data frame containing gene expression values :type genes: :param genes: List of genes for which individual line plots will be generated :type order: :param order: Order of x-axis labels from left to right :type xlabel: str :param xlabel: x-axis label :type cluster: str :param cluster: Which clusters to plot. Select ‘all” if all clusters should be drawn. :type hue: :param hue: Split expression values by this grouping, one line per category, will be drawn :type figsize: Tuple[int, int] :param figsize: Size of the figure as specified in matplotlib :type smooth: :param smooth: Set to true for smoothened line plot using polynomial regression :type rotation: :param rotation: Set to True to rotate x-axis labels 90 degrees :type tick_size: :param tick_size: Size of the ticks as specified in matplotlib :type label_size: :param label_size: Size of the labels as specified in matplotlib :type order_smooth: :param order_smooth: If greater than 1, use numpy.polyfit to estimate a polynomial regression :type conf_int: :param conf_int: Size of the confidence interval for the regression estimate :type scatter: :param scatter: Set to True to add average expression values per sample ID as dots :type cols: :param cols: List of colors to use for line plot :type save: Optional[str] :param save: Path to save the plot to