itpseq.DataSet.itp_len_plot#

DataSet.itp_len_plot(ax=None, col=None, row=None, min_codon=0, max_codon=10, limit=100, norm=True, hue='auto', plt_kwargs={'aspect': 3, 'height': 2, 'kind': 'line'})[source]#

Generates a line plot of inverse-toeprint (ITP) counts per length.

This method uses the output of itp_len to create a line plot showing the counts of inverse-toeprints across lengths for each replicate. Optionally, counts can be normalized (per million reads), and the plotted lengths can be limited.

Parameters:

  • ax (matplotlib.axes.Axes, optional) – Pre-existing axes to draw the plot on. A new figure and axes are created if not provided.

  • col (string, optional) – attribute to use as columns in the FacetGrid

  • row (string, optional) – attribute to use as rows in the FacetGrid

  • min_codon (int, optional) – The minimum codon position to annotate on the plot. Defaults to 0.

  • max_codon (int, optional) – The maximum codon position to annotate on the plot. Defaults to 10.

  • limit (int, optional) – The maximum length to include in the plot. Defaults to 100.

  • norm (bool, optional) – Whether to normalize counts to reads per million. Defaults to False.

  • hue (str, optional) – Parameter to use a hue in the FacetGrid (by default ‘replicate’).

  • plt_kwargs (dict, optional) – parameters used in the FacetGrid if col/row is used.

Return type:

matplotlib.axes.Axes or seaborn.axisgrid.FacetGrid

Notes

  • The x-axis represents the distance from the 3’ end of the inverse-toeprint in nucleotides.

  • The y-axis shows the counts of inverse-toeprints, either absolute or normalized per million reads.

  • Each replicate is plotted independently and distinguished by the hue attribute in the plot.

Examples

Plot a line with error band for each sample:

>>> dataset.itp_len_plot()
../../_images/dataset_itp_len_plot.png

Create a figure with a subplot per sample and a line per replicate:

>>> dataset.itp_len_plot(row='sample')
../../_images/dataset_itp_len_plot_row.png
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