cytonormpy.pl.scatter

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cytonormpy.pl.scatter#

cytonormpy.pl.scatter(cnp, file_name, x_channel, y_channel, x_scale='linear', y_scale='linear', xlim=None, ylim=None, legend_labels=None, subsample=None, linthresh=500, display_reference=True, vary_textures=False, figsize=(2, 2), ax=None, return_fig=False, show=True, save=None, **kwargs)#

Scatterplot visualization.

Parameters:

  • file_name (str) – The file name of the file that is supposed to be plotted.

  • x_channel (str) – The channel plotted on the x-axis.

  • y_channel (str) – The channel plotted on the y-axis.

  • x_scale (Literal['biex', 'log', 'linear']) – The scale type of the x-axis. Can be one of biex, linear or log. Defaults to biex.

  • y_scale (Literal['biex', 'log', 'linear']) – The scale type of the y-axis. Can be one of biex, linear or log. Defaults to biex.

  • xlim (Optional[tuple[float, float]]) – Sets the x-axis limits.

  • ylim (Optional[tuple[float, float]]) – Sets the y-axis limits.

  • legend_labels (Optional[list[str]]) – The labels displayed in the legend.

  • subsample (Optional[int]) – A number of events to subsample to. Can prevent overcrowding of the plot.

  • linthresh (float) – The value to switch from a linear to a log axis. Ignored if neither x- nor y-scale are biex.

  • display_reference (bool) – Whether to display the reference data from that batch as well. Defaults to True.

  • vary_textures (bool) – If True, use different marker shapes for each ‘origin’ category by passing style=”origin” and a markers mapping to seaborn.

  • ax (Optional[Axes]) – A Matplotlib Axes to plot into.

  • return_fig (bool) – Returns the figure. Defaults to False.

  • show (bool) – Whether to show the figure.

  • save (Optional[str]) – A string specifying a file path. Defaults to None, where no image is saved.

  • kwargs – keyword arguments ultimately passed to sns.scatterplot.

Return type:

If show==False, a Axes.

Examples

import cytonormpy as cnp

cn = cnp.example_cytonorm()
cnp.pl.scatter(cn,
               cn._datahandler.metadata.validation_file_names[0],
               x_channel = "Ho165Di",
               y_channel = "Yb172Di",
               x_scale = "linear",
               y_scale = "linear",
               figsize = (4,4),
               s = 10,
               linewidth = 0.4,
               edgecolor = "black")
../_images/cytonormpy-pl-scatter-1.png

Note

If you want additional separation of the individual point classes, you can pass ‘vary_textures=True’.

import cytonormpy as cnp

cn = cnp.example_cytonorm()
cnp.pl.scatter(cn,
               cn._datahandler.metadata.validation_file_names[0],
               x_channel = "Ho165Di",
               y_channel = "Yb172Di",
               x_scale = "linear",
               y_scale = "linear",
               vary_textures = True,
               figsize = (4,4),
               s = 10,
               linewidth = 0.4,
               edgecolor = "black")
../_images/cytonormpy-pl-scatter-2.png
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