dohlee.plot module

dohlee.plot.save(file, dpi=300, tight_layout=True)

Save plot to a file.

Parameters:

• file (str) – Path to the resulting image file.
• dpi (int) – (default=300) Resolution.
• tight_layout (bool) – (default=True) Whether to run plt.tight_layout() before saving the plot.

dohlee.plot.clear()

Clear the plot.

dohlee.plot.set_suptitle(title)

Set suptitle for the plot.

dohlee.plot.set_style(style='white', palette='deep', context='talk', font='Helvetica Neue', scale=1.0, font_scale=1.0)

Set plot preference in a way that looks good to me.

dohlee.plot.set_paper()

dohlee.plot.set_talk()

dohlee.plot.set_presentation()

dohlee.plot.set_poster()

dohlee.plot.get_axis(figsize=None, dpi=300)

Get plot axis with predefined/user-defined width and height.

>>> ax = get_axis()
>>> ax = get_axis(figsize=(7.2, 4.45))

Parameters:

• scale (float) – Figure size scale. Width and height will be scale with this value.
• figsize (tuple) – Use user-defined width and height. If this is given, scale parameter will be ignored.

dohlee.plot.get_grid(shape=(1, 1), ax=None, hspace=None, wspace=None)

TODO

dohlee.plot.get_axis_from_grid(grid)

TODO

dohlee.plot.frequency(data, order=None, sort_by_values=False, dy=0.01, ax=None, **kwargs)

Plot frequency bar chart.

>>> frequency([1, 2, 2, 3, 3, 3], order=[3, 1, 2], sort_by_values=True)

Parameters:

• data (list) – A list of elements.
• order (list) – A list of elements which represents the order of the elements to be plotted.
• sort_by_values (bool) – If True, the plot will be sorted in decreasing order of frequency values.
• dy (float) – Gap between a bar and its count label.
• ax (pyplot-axis) – Axis to draw the plot.

dohlee.plot.histogram(data, ax=None, **kwargs)

Draw a histogram.

>>> histogram(data=data, ax=ax, lw=1.55)

Parameters:

• data (list) – A list containing values. Density of the values will be drawn as a histogram.
• ax (axis) – Matplotlib axis to draw the plot on.

dohlee.plot.boxplot(data, x, y, hue=None, ax=None, strip=False, box_kwargs={}, strip_kwargs={})

Draw a boxplot.

>>> boxplot(data, x='species', y='sepal_length', strip=True)

Parameters:

• data (dataframe) – Dataframe for boxplot.
• x (str) – Column name representing x variable of the plot.
• y (str) – Column name representing y variable of the plot.
• ax (axis) – (Optional) Matplotlib axis to draw the plot on.
• strip (bool) – (default=False) Draw overlapped stripplot.

dohlee.plot.volcano(data, x, y, padj, label, cutoff=0.05, sample1=None, sample2=None, ax=None)

Draw a volcano plot.

>>> volcano(data=data,
            x='log2FoldChange',
            y='pvalue',
            label='Gene_Symbol',
            cutoff=0.05,
            padj='padj',
            figsize=(10.8, 8.4))

Parameters:

• data (dataframe) – A dataframe resulting from DEG-discovery tool.
• x (str) – Column name denoting log2 fold change.
• y (str) – Column name denoting p-value. (Note that p-values will be log10-transformed, so they should not be transformed beforehand.)
• padj (str) – Column name denoting adjusted p-value.
• label (str) – Column name denoting gene identifier.
• cutoff (float) – (Optional) Adjusted p-value cutoff value to report significant DEGs.
• sample1 (str) – (Optional) First sample name.
• sample2 (str) – (Optional) Second sample name.
• ax (axis) – (Optional) Matplotlib axis to draw the plot on.

dohlee.plot.pca(data, labels=None, ax=None, **kwargs)

Draw a simple principle component analysis plot of the data.

Parameters:

• data (matrix) – Input data. Numpy array recommended.
• labels (list) – (Optional) Corresponding labels to each datum. If specified, data points in the plot will be colored according to the label.
• ax (axis) – (Optional) Matplotlib axis to draw the plot on.
• kwargs – Any other keyword arguments will be passed onto matplotlib.pyplot.scatter.

dohlee.plot.tsne(data, labels=None, ax=None, **kwargs)

Draw a T-SNE analysis plot of the data.

Parameters:

• data (matrix) – Input data. Numpy array recommended.
• labels (list) – (Optional) Corresponding labels to each datum. If specified, data points in the plot will be colored according to the label.
• ax (axis) – (Optional) Matplotlib axis to draw the plot on.
• kwargs – Any other keyword arguments will be passed onto matplotlib.pyplot.scatter.

dohlee.plot.coverages(path, chrom, start, end, strict=False, tick_every=1000, ax=None, **kwargs)

dohlee.plot.has_no_overlap(r1, r2)

Returns True if the two reads overlap.

dohlee.plot.plot_bisulfite_read(read, depth_reads_dict, chrom, start, end, strict, ax)

dohlee.plot.bisulfite(path, chrom, start, end, ax=None, tick_every=1000, strict=False, **kwargs)

dohlee.plot.mutation_signature(data, ax=None, **kwargs)

dohlee.plot.linear_regression(x, y, regression=True, ax=None, color='k')

TODO

dohlee.plot.m_bias(mbias_data, ax=None)

TODO

dohlee.plot.stacked_bar_chart(data, x, y, ax=None, sort=False, reverse=True, sort_by=None, group=None, group_order=None, group_label=True)

TODO

dohlee.plot.umap(data, labels=None, ax=None, **kwargs)

Draw a UMAP embedding plot of the data.

Parameters:

• data (matrix) – Input data. Numpy array recommended.
• labels (list) – (Optional) Corresponding labels to each datum. If specified, data points in the plot will be colored according to the label.
• ax (axis) – (Optional) Matplotlib axis to draw the plot on.
• kwargs – Any other keyword arguments will be passed onto matplotlib.pyplot.scatter.

dohlee.plot.dimensionality_reduction(data, labels, ax=None, scatter_kwargs={})

dohlee.plot.line(y, data=None, x=None, hue=None, ax=None, agg='mean', **kwargs)