sc_toolbox.plot.standard_lineplot#

sc_toolbox.plot.standard_lineplot(data, order, xlabel, ylabel, hue=None, gene=None, smooth=None, palette=None, title=None, rotation=None, figsize=(15, 5), tick_size=None, label_size=None, order_smooth=3, confidence_interval=None, scatter=None, save=None)[source]#

Draws a standard line plot based on Seaborn’s lmplot.

Parameters:

  • data – Data frame containing averaged expression values

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

  • xlabel (str) – x-axis label

  • ylabel (str) – y-axis label

  • hue – Subsets of the data which will be drawn on separate facets in the grid. Example: “condition”

  • gene – Gene of interest

  • smooth (Optional[bool]) – Whether to smoothen (interpolate) the curve

  • palette – Color palette. For example a list of colors.

  • title – Title of the plot

  • rotation (Optional[int]) – Rotation of the x-axis labels

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

  • tick_size – Size of the ticks as specified in matplotlib

  • label_size – Size of the labels as specified in matplotlib

  • order_smooth (int) – If greater than 1, numpy.polyfit is used to estimate a polynomial regression

  • confidence_interval – Confidence interval

  • scatter – Set to true in order to add mean expression per sample in form of scatter point

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