Plotting API

The Plotting API provides built-in plotting functions. For basic usage, you can generate plots dierctly via Nanocompore's command line interface (Plotting guide). However, if you want to use the plotting functions programatically or want to customize them beyond the basic parameters provided by the command line interface, you can use the API. Nanocompore uses Seaborn, so all functions produce Matplotlib figures that you can modify.

For example:

>>> from nanocompore.api import load_config
>>> from nanocompore.plotting import plot_coverage

# Load the YAML configuration file to a Config object.
>>> config = load_config('analysis.yaml')

# Get a coverage figure for a given transcript.
>>> ref_id = 'ENST00000464651.1|ENSG00000166136.16|OTTHUMG00000019346.4|OTTHUMT00000051221.1|NDUFB8-204|NDUFB8|390|retained_intron|'
>>> fig = plot_coverage(config, ref_id)

# We can now manipulate the figure to customize it
# beyond the parametrization of provided by the API.
# E.g. we can add a title:
>>> fig.axes[0].set_title('Coverage of NDUF8B-204')

# Then we update the layout and save the figure:
>>> fig.tight_layout()
>>> fig.savefig('NDUF8B_coverage.png')

Reference

plot_coverage(config, reference, start=None, end=None, figsize=(30, 10), split_samples=False, palette='Dark2')

Plot the read coverage over a reference for all samples analysed. Note that this would plot the input coverage before applying any filtering that Nanocompore does before the comparison.

Parameters:

Name	Type	Description	Default
config	Config	The configuration object for the run.	required
reference	str	Transcript reference.	required
start	Union[int, None]	Start of the region that will be plotted.	None
end	Union[int, None]	End of the region that will be plotted.	None
figsize	tuple[int, int]	Size of the figure.	(30, 10)
split_samples	bool	If True, all samples would be plotted separately. By default it's False and the samples are grouped by condition.	False
palette	str	Color palette to use.	'Dark2'

Returns:

Type	Description
Union[Figure, SubFigure, None]	Figure with the coverage plot.

Source code in nanocompore/plotting.py

def plot_coverage(config: Config,
                  reference: str,
                  start: Union[int, None]=None,
                  end: Union[int, None]=None,
                  figsize: tuple[int, int]=(30, 10),
                  split_samples: bool=False,
                  palette: str='Dark2') -> Union[Figure, SubFigure, None]:
    """
    Plot the read coverage over a reference for all samples analysed.
    Note that this would plot the input coverage before applying
    any filtering that Nanocompore does before the comparison.

    Parameters
    ----------
    config : Config
        The configuration object for the run.
    reference : str
        Transcript reference.
    start : Union[int, None]
        Start of the region that will be plotted.
    end : Union[int, None]
        End of the region that will be plotted.
    figsize : tuple[int, int]
        Size of the figure.
    split_samples : bool
        If True, all samples would be plotted separately.
        By default it's False and the samples are grouped
        by condition.
    palette : str
        Color palette to use.

    Returns
    -------
    Union[Figure, SubFigure, None]
        Figure with the coverage plot.

    """
    data, reads, samples, conditions = get_reads(config, reference)
    positions = np.arange(data.shape[1])[start:end]
    data = data[:, start:end, 0]

    if split_samples:
        group_var = 'sample'
        groups = np.array(samples)
    else:
        group_var = 'condition'
        groups = np.array(conditions)

    covs = {}
    all_group_labels = np.unique(groups)
    for group in all_group_labels:
        covs[group] = np.sum(~np.isnan(data[groups == group, :]), axis=0)
    covs['pos'] = positions
    covs = pd.DataFrame(covs)
    covs = pd.melt(covs,
                   id_vars=['pos'],
                   value_vars=all_group_labels,
                   var_name=group_var,
                   value_name='cov')

    fig, ax = plt.subplots(figsize=figsize)

    sns.lineplot(covs, x='pos', y='cov', hue=group_var, palette=palette, ax=ax)
    ax.axhline(y=config.get_min_coverage(), linestyle='--', color='grey', label='Minimum coverage')

    plt.legend()
    sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))
    plt.tight_layout()

    ax.set_xlabel('Reference position')
    ax.set_ylabel('Coverage')

    return fig

plot_gmm(config, reference, position, figsize=(10, 10), point_size=20, xlim=(None, None), ylim=(None, None), gmm_levels=4, palette='Dark2', point_palette=None, gmm_palette=None)

Plot the GMM fitted by Nanocompore for a given position.

Parameters:

Name	Type	Description	Default
config	Config	The configuration object for the run.	required
reference	str	Transcript reference.	required
position	int	0-based index on the reference indicating the position.	required
figsize	tuple[int, int]	Size of the figure.	(10, 10)
point_size	int	Size of the points.	20
xlim	Union[tuple[Union[int, None], Union[int, None]], None]	Limits of the x-axis.	(None, None)
ylim	Union[tuple[Union[int, None], Union[int, None]], None]	Limits of the y-axis.	(None, None)
gmm_levels	int	How many levels of the GMM to show.	4
palette	Union[str, None]	Palette that will be used to determine the colors of both points and the GMMs. If set, it will override point_palette and gmm_palette.	'Dark2'
point_palette	Union[str, None]	Palette to use for the points.	None
gmm_palette	Union[str, None]	Palette to use for the GMMs.	None

Returns:

Type	Description
Union[Figure, SubFigure, None]	The resulting figure that will contain a 2D plot with the observations as points and the gaussians obtained from the GMM.

Source code in nanocompore/plotting.py

def plot_gmm(config: Config,
             reference: str,
             position: int,
             figsize: tuple[int, int]=(10, 10),
             point_size: int=20,
             xlim: Union[tuple[Union[int, None], Union[int, None]], None]=(None, None),
             ylim: Union[tuple[Union[int, None], Union[int, None]], None]=(None, None),
             gmm_levels: int=4,
             palette: Union[str, None]='Dark2',
             point_palette: Union[str, None]=None,
             gmm_palette: Union[str, None]=None) -> Union[Figure, SubFigure, None]:
    """
    Plot the GMM fitted by Nanocompore for a given position.

    Parameters
    ----------
    config : Config
        The configuration object for the run.
    reference : str
        Transcript reference.
    position : int
        0-based index on the reference indicating the position.
    figsize : tuple[int, int]
        Size of the figure.
    point_size : int
        Size of the points.
    xlim : Union[tuple[Union[int, None], Union[int, None]], None]
        Limits of the x-axis.
    ylim : Union[tuple[Union[int, None], Union[int, None]], None]
        Limits of the y-axis.
    gmm_levels : int
        How many levels of the GMM to show.
    palette : Union[str, None]
        Palette that will be used to determine the
        colors of both points and the GMMs. If set,
        it will override point_palette and gmm_palette.
    point_palette : Union[str, None]
        Palette to use for the points.
    gmm_palette : Union[str, None]
        Palette to use for the GMMs.

    Returns
    -------
    Union[Figure, SubFigure, None]
       The resulting figure that will contain a
       2D plot with the observations as points
       and the gaussians obtained from the GMM.
    """
    fasta_fh = Fasta(config.get_fasta_ref())
    ref_seq = str(fasta_fh[reference])

    transcript = Transcript(1, reference, ref_seq)

    if config.get_resquiggler() == UNCALLED4:
        worker_class = Uncalled4Worker
    else:
        worker_class = GenericWorker
    worker = worker_class(1, None, None, None, None, 'cpu', config)
    data, samples, conditions = worker._read_data(transcript)
    prepared_data = worker._prepare_data(data, samples, conditions)
    data, samples, conditions, _ = prepared_data
    data = data[[position], :, :]
    max_reads = worker._conf.get_downsample_high_coverage()
    data, samples, conditions = worker._downsample(
            data, samples, conditions, max_reads)

    std = nanstd(data, 1).unsqueeze(1)
    outliers = (((data - data.nanmean(1, keepdim=True)) / std).abs() > 3).any(2)
    data[outliers] = np.nan

    # Standardize the data
    std = nanstd(data, 1)
    data = (data - data.nanmean(1).unsqueeze(1)) / std.unsqueeze(1)

    gmm = GMM(n_components=2,
              device='cpu',
              random_seed=42,
              dtype=torch.float32)
    gmm.fit(data)

    # Means is a list with the means for each component.
    # The shape of each is (Points, Dims). We have a single point.
    c1_mean = gmm.means[0][0]
    c2_mean = gmm.means[1][0]

    # Covs is a list with the cov matrices for the components.
    # The shape of each is (Points, Dims, Dims).
    c1_cov = gmm.covs[0][0]
    c2_cov = gmm.covs[1][0]

    x1, y1 = np.random.multivariate_normal(c1_mean, c1_cov, 1000).T
    x2, y2 = np.random.multivariate_normal(c2_mean, c2_cov, 1000).T
    sampled_gaussians = pd.DataFrame(
        {'x': np.concatenate([x1, x2]),
         'y': np.concatenate([y1, y2]),
         'cluster': np.concatenate([np.full((1000,), 'Cluster 1'),
                                    np.full((1000,), 'Cluster 2')])})

    df = pd.DataFrame(data[0], columns=['intensity', 'dwell'])
    cond_labels = config.get_condition_labels()
    df['condition'] = [cond_labels[c] for c in conditions]

    if palette is not None:
        palette = {cond_labels[0]: plt.get_cmap(palette).colors[0],
                   cond_labels[1]: plt.get_cmap(palette).colors[1],
                   'Cluster 1': plt.get_cmap(palette).colors[2],
                   'Cluster 2': plt.get_cmap(palette).colors[3]}
    else:
        palette = {cond_labels[0]: plt.get_cmap(point_palette).colors[0],
                   cond_labels[1]: plt.get_cmap(point_palette).colors[1],
                   'Cluster 1': plt.get_cmap(gmm_palette).colors[0],
                   'Cluster 2': plt.get_cmap(gmm_palette).colors[1]}

    fig, ax = plt.subplots(figsize=figsize)
    if xlim is not None:
        ax.set_xlim(*xlim)
    if ylim is not None:
        ax.set_ylim(*ylim)
    if xlim is None and ylim is None:
        lims = (min(df.dwell.min(),
                    df.intensity.min(),
                    sampled_gaussians.x.min(),
                    sampled_gaussians.y.min()),
                max(df.dwell.max(),
                    df.intensity.max(),
                    sampled_gaussians.x.max(),
                    sampled_gaussians.y.max()))
        ax.set_xlim(*lims)
        ax.set_ylim(*lims)

    sns.kdeplot(sampled_gaussians,
                x='x',
                y='y',
                levels=gmm_levels,
                hue="cluster",
                palette=palette,
                ax=ax)
    sns.scatterplot(df,
                    x='dwell',
                    y='intensity',
                    hue='condition',
                    s=point_size,
                    palette=palette,
                    legend=False,
                    ax=ax)

    handles = [mpatches.Patch(facecolor=col, label=label)
               for label, col in palette.items()]
    plt.legend(handles=handles)

    ax.set_xlabel('Standardized log10(dwell)')
    ax.set_ylabel('Standardized intensity')
    return fig

plot_position(config, reference, position, figsize=(10, 10), point_size=20, xlim=(None, None), ylim=(None, None), show_kde=True, kde_levels=10, palette='Dark2')

Plot the signal data for a given position.

Parameters:

Name	Type	Description	Default
config	Config	The configuration object for the run.	required
reference	str	Transcript reference.	required
position	int	0-based index on the reference indicating the position.	required
figsize	tuple[int, int]	Size of the figure.	(10, 10)
point_size	int	Size of the points.	20
xlim	Union[tuple[Union[int, None], Union[int, None]], None]	Limits of the x-axis.	(None, None)
ylim	Union[tuple[Union[int, None], Union[int, None]], None]	Limits of the y-axis.	(None, None)
kde	bool	Whether to show the KDEs.	required
kde_levels	int	How many levels of the KDEs to show.	10
palette	Union[str, None]	Palette that will be used to determine the colors of both points and the GMMs. If set, it will override point_palette and gmm_palette.	'Dark2'

Returns:

Type	Description
Union[Figure, SubFigure, None]	The resulting figure that will contain a 2D plot with the observations as points and the gaussians obtained from the GMM.

Source code in nanocompore/plotting.py

def plot_position(config: Config,
                  reference: str,
                  position: int,
                  figsize: tuple[int, int]=(10, 10),
                  point_size: int=20,
                  xlim: Union[tuple[Union[int, None], Union[int, None]], None]=(None, None),
                  ylim: Union[tuple[Union[int, None], Union[int, None]], None]=(None, None),
                  show_kde: bool=True,
                  kde_levels: int=10,
                  palette: Union[str, None]='Dark2') -> Union[Figure, SubFigure, None]:
    """
    Plot the signal data for a given position.

    Parameters
    ----------
    config : Config
        The configuration object for the run.
    reference : str
        Transcript reference.
    position : int
        0-based index on the reference indicating the position.
    figsize : tuple[int, int]
        Size of the figure.
    point_size : int
        Size of the points.
    xlim : Union[tuple[Union[int, None], Union[int, None]], None]
        Limits of the x-axis.
    ylim : Union[tuple[Union[int, None], Union[int, None]], None]
        Limits of the y-axis.
    kde : bool
        Whether to show the KDEs.
    kde_levels : int
        How many levels of the KDEs to show.
    palette : Union[str, None]
        Palette that will be used to determine the
        colors of both points and the GMMs. If set,
        it will override point_palette and gmm_palette.

    Returns
    -------
    Union[Figure, SubFigure, None]
       The resulting figure that will contain a
       2D plot with the observations as points
       and the gaussians obtained from the GMM.
    """
    fig, ax = plt.subplots(figsize=figsize)
    if xlim is not None:
        ax.set_xlim(*xlim)
    if ylim is not None:
        ax.set_ylim(*ylim)
    df = get_pos(config, reference, position)
    sns.scatterplot(df,
                    x='dwell',
                    y='intensity',
                    hue='condition',
                    s=point_size,
                    palette=palette,
                    ax=ax)
    if show_kde:
        sns.kdeplot(df,
                    x='dwell',
                    y='intensity',
                    levels=kde_levels,
                    hue="condition",
                    palette=palette,
                    ax=ax)

    ax.set_xlabel('Dwell time')
    ax.set_ylabel('Intensity')

    return fig

plot_pvalues(config, reference, start=None, end=None, kind='lineplot', threshold=0.01, figsize=(30, 10), tests=None, palette='Dark2')

Plot the p-values from the statistical tests performed in a Nanocompore run.

Parameters:

Name	Type	Description	Default
config	Config	The configuration object for the run.	required
reference	str	Transcript reference.	required
start	Union[int, None]	Start of the region that will be plotted.	None
end	Union[int, None]	End of the region that will be plotted.	None
kind	str	Kind of plot to make. The available options are: lineplot, barplot	'lineplot'
threshold	Union[float, None]	If set, it will indicate the p-value threshold as a dashed horizontal line.	0.01
figsize	tuple[int, int]	Size of the figure.	(30, 10)
tests	Union[list[str], None]	List of tests to plot. The available options are: GMM, KS, TT, MW. If set to None (default) it would plot all tests that were listed in the configuration.	None
palette	str	Color palette to use.	'Dark2'

Returns:

Type	Description
Union[Figure, SubFigure, None]	The resulting figure that will contain the p-values for the specified tests in the provided region.

Source code in nanocompore/plotting.py

def plot_pvalues(config: Config,
                 reference: str,
                 start: Union[int, None]=None,
                 end: Union[int, None]=None,
                 kind: str='lineplot',
                 threshold: Union[float, None]=0.01,
                 figsize: tuple[int, int]=(30, 10),
                 tests: Union[list[str], None]=None,
                 palette: str='Dark2') -> Union[Figure, SubFigure, None]:
    """
    Plot the p-values from the statistical tests
    performed in a Nanocompore run.

    Parameters
    ----------
    config : Config
        The configuration object for the run.
    reference : str
        Transcript reference.
    start : Union[int, None]
        Start of the region that will be plotted.
    end : Union[int, None]
        End of the region that will be plotted.
    kind : str
        Kind of plot to make. The available options
        are: lineplot, barplot
    threshold : Union[float, None]
        If set, it will indicate the p-value threshold
        as a dashed horizontal line.
    figsize : tuple[int, int]
        Size of the figure.
    tests : Union[list[str], None]
        List of tests to plot. The available options
        are: GMM, KS, TT, MW.
        If set to None (default) it would plot all
        tests that were listed in the configuration.
    palette : str
        Color palette to use.

    Returns
    -------
    Union[Figure, SubFigure, None]
       The resulting figure that will contain the
       p-values for the specified tests in the
       provided region.
    """
    db = ResultsDB(config)
    cols = []
    if tests is None:
        tests = config.get_comparison_methods()
    for test in tests:
        if test not in TEST_PVALUE_COLUMNS:
            raise KeyError(f"Test {test} not supported.")
        for col in TEST_PVALUE_COLUMNS[test]:
            cols.append(col)
    pvals = db.get_columns_for_ref(cols + ['kmer'], reference)
    pvals = pd.melt(pvals,
                    id_vars=['pos', 'kmer'],
                    value_vars=[c for c in pvals.columns if c != 'pos'],
                    var_name='test',
                    value_name='pval')
    pvals['pval'] = -np.log10(pvals.pval)
    pvals['kmer'] = pvals.kmer.apply(lambda k: decode_kmer(k, config.get_kit().len))
    if start is not None and end is not None:
        pvals = pvals[pvals.pos.between(start, end)]
    elif start is not None:
        pvals = pvals[pvals.pos >= start]
    elif end is not None:
        pvals = pvals[pvals.pos <= end]

    max_pos = pvals.pos.max()
    min_pos = pvals.pos.min()
    is_detailed_plot = max_pos - min_pos <= 50

    fig, ax = plt.subplots(figsize=figsize)

    if threshold is not None:
        ax.axhline(y=-np.log10(threshold), color='grey', linestyle='--', label=f'p-value = {threshold}')

    if kind == 'lineplot' and is_detailed_plot:
        sns.pointplot(pvals, x='pos', y='pval', hue='test', ax=ax)
    elif kind == 'lineplot':
        sns.lineplot(pvals, x='pos', y='pval', hue='test', ax=ax)
    elif kind == 'barplot':
        sns.barplot(pvals, x='pos', y='pval', hue='test', ax=ax)
    else:
        raise ValueError(f'Plot kind {kind} not supported.')

    ax.set_xlabel("Reference position")
    ax.set_ylabel("-log10(pvalue)")

    if is_detailed_plot:
        ax2 = ax.twiny()
        ax2.spines['bottom'].set_position(('axes', -0.18))
        ax2.spines['bottom'].set_visible(False)
        kmer_df = pvals.loc[:, ['pos', 'kmer']].drop_duplicates()
        ax2.set_xlim(min_pos - 0.5, max_pos + 0.5)
        ax2.set_xticks(kmer_df.pos)
        ax2.set_xticklabels(kmer_df.kmer, rotation=60)


    # plt.legend()
    # print(ax2.get_legend_handles_labels())
    # plt.legend(loc="upper left", bbox_to_anchor=(1, 1))
    sns.move_legend(ax, "upper left", bbox_to_anchor=(1, 1))
    plt.tight_layout()

    return fig

plot_signal(config, reference, start=None, end=None, kind='violinplot', figsize=(30, 10), split_samples=False, markersize=2, palette='Dark2')

Plot the raw signal values (intensity and dwell time) for the given reference and region. Note that this will plot all reads from the input files without applying all the filtering and downsampling that Nanocompore does.

Parameters:

Name	Type	Description	Default
config	Config	The configuration object for the run.	required
reference	str	Transcript reference.	required
start	Union[int, None]	Start of the region that will be plotted.	None
end	Union[int, None]	End of the region that will be plotted.	None
kind	str	Kind of plot to make. The available options are: lineplot, barplot	'violinplot'
figsize	tuple[int, int]	Size of the figure.	(30, 10)
split_samples	bool	If True, all samples would be plotted separately. By default it's False and the samples are grouped by condition.	False
markersize	int	Size of the points (only used for the swarmplot).	2
palette	str	Color palette to use.	'Dark2'

Returns:

Type	Description
Union[Figure, SubFigure, None]	The resulting figure that will contain the intensity and log-dwell-time plots for the specified reference and region.

Source code in nanocompore/plotting.py

def plot_signal(config: Config,
                reference: str,
                start: Union[int, None]=None,
                end: Union[int, None]=None,
                kind: str='violinplot',
                figsize: tuple[int, int]=(30, 10),
                split_samples: bool=False,
                markersize: int=2,
                palette: str='Dark2') -> Union[Figure, SubFigure, None]:
    """
    Plot the raw signal values (intensity and dwell time)
    for the given reference and region. Note that this
    will plot all reads from the input files without
    applying all the filtering and downsampling that
    Nanocompore does.

    Parameters
    ----------
    config : Config
        The configuration object for the run.
    reference : str
        Transcript reference.
    start : Union[int, None]
        Start of the region that will be plotted.
    end : Union[int, None]
        End of the region that will be plotted.
    kind : str
        Kind of plot to make. The available options
        are: lineplot, barplot
    figsize : tuple[int, int]
        Size of the figure.
    split_samples : bool
        If True, all samples would be plotted separately.
        By default it's False and the samples are grouped
        by condition.
    markersize : int
        Size of the points (only used for the swarmplot).
    palette : str
        Color palette to use.

    Returns
    -------
    Union[Figure, SubFigure, None]
       The resulting figure that will contain the
       intensity and log-dwell-time plots for the
       specified reference and region.
    """
    data, _, samples, conditions = get_reads(config, reference)
    positions = np.arange(data.shape[1])

    data = data[:, start:end, :]
    positions = positions[start:end]

    nreads = data.shape[0]
    group_var = samples if split_samples else conditions
    group_var_name = 'Sample' if split_samples else 'Condition'
    long_data = []
    for r in range(nreads):
        for p, pos in enumerate(positions):
            long_data.append((group_var[r],
                              pos,
                              data[r, p, INTENSITY_POS],
                              np.log10(data[r, p, DWELL_POS])))
    df = pd.DataFrame(long_data, columns=[group_var_name, 'pos', 'intensity', 'dwell'])

    fig, ax = plt.subplots(2, 1, figsize=figsize)

    if kind == 'violinplot':
        sns.violinplot(df, x='pos', y='intensity', hue=group_var_name, ax=ax[0], inner='quart', split=not split_samples)
        sns.violinplot(df, x='pos', y='dwell', hue=group_var_name, ax=ax[1], inner='quart', split=not split_samples, legend=False)
    elif kind == 'swarmplot':
        sns.swarmplot(df, x='pos', y='intensity', hue=group_var_name, ax=ax[0], size=markersize, dodge=True)
        sns.swarmplot(df, x='pos', y='dwell', hue=group_var_name, ax=ax[1], size=markersize, dodge=True, legend=False)
    elif kind == 'boxenplot':
        sns.boxenplot(df, x='pos', y='intensity', hue=group_var_name, ax=ax[0])
        sns.boxenplot(df, x='pos', y='dwell', hue=group_var_name, ax=ax[1], legend=False)
    else:
        raise ValueError(f'Plot kind {kind} not supported.')

    ax[0].set_xlabel(None)
    ax[0].set_ylabel("intensity")
    ax[1].set_xlabel("Reference position")
    ax[1].set_ylabel("log10(dwell time)")

    sns.move_legend(ax[0], "upper left", bbox_to_anchor=(1, 1), markerscale=math.ceil(10/markersize))

    plt.tight_layout()

    return fig