damuta.plotting module

plot_cosmic_signatures(sigs, pal=None, aspect=5)

Plot COSMIC-style mutational signatures with 96 trinucleotide contexts.

Parameters:

  • sigs (pandas.DataFrame) – DataFrame containing COSMIC signature data with signatures as rows and 96 mutation types as columns.

  • pal (list or seaborn color palette, optional) – Color palette for the bar plots. If None, uses cosmic_palette.

  • aspect (float, default=5) – Aspect ratio for each subplot in the FacetGrid.

Returns:

FacetGrid object containing the COSMIC signature plots.

Return type:

seaborn.FacetGrid

plot_damage_signatures(sigs, pal=None, aspect=3)

Plot damage signatures with 32 trinucleotide contexts.

Parameters:

  • sigs (pandas.DataFrame) – DataFrame containing damage signature data with signatures as rows and 32 mutation types as columns.

  • pal (list or seaborn color palette, optional) – Color palette for the bar plots. If None, uses damage_palette.

  • aspect (float, default=3) – Aspect ratio for each subplot in the FacetGrid.

Returns:

FacetGrid object containing the damage signature plots.

Return type:

seaborn.FacetGrid

plot_eta_posterior(eta_approx, cols=None)

Plot posterior distributions of misrepair signature parameters (eta).

Parameters:

  • eta_approx (numpy.ndarray) – 4D array of posterior samples with shape (n_samples, n_misrepair_sigs, 2, 3). Contains posterior samples of misrepair signature parameters for C and T contexts.

  • cols (list, optional) – List of colors for each mutation type. If None, uses eta_col.

Returns:

Figure object containing the posterior distribution plots.

Return type:

matplotlib.figure.Figure

plot_misrepair_signatures(sigs, pal=None, aspect=1)

Plot misrepair signatures with 6 substitution types.

Parameters:

  • sigs (pandas.DataFrame) – DataFrame containing misrepair signature data with signatures as rows and 6 mutation types as columns.

  • pal (list or seaborn color palette, optional) – Color palette for the bar plots. If None, uses misrepair_palette.

  • aspect (float, default=1) – Aspect ratio for each subplot in the FacetGrid.

Returns:

FacetGrid object containing the misrepair signature plots.

Return type:

seaborn.FacetGrid

plot_phi_posterior(phi_approx, cols=None)

Plot posterior distributions of damage signature parameters (phi).

Parameters:

  • phi_approx (numpy.ndarray) – 3D array of posterior samples with shape (n_samples, n_damage_sigs, n_contexts). Contains posterior samples of damage signature parameters.

  • cols (list, optional) – List of colors for each mutation context. If None, uses phi_col.

Returns:

Figure object containing the posterior distribution plots.

Return type:

matplotlib.figure.Figure

plot_signatures(sigs, pal=None, aspect=5)

Plot mutational signatures as bar plots using seaborn FacetGrid.

Parameters:

  • sigs (pandas.DataFrame) – DataFrame containing signature data with signatures as rows and mutation types as columns.

  • pal (list or seaborn color palette, optional) – Color palette for the bar plots. If None, uses cosmic_palette.

  • aspect (float, default=5) – Aspect ratio for each subplot in the FacetGrid.

Returns:

FacetGrid object containing the signature plots.

Return type:

seaborn.FacetGrid