dohlee.plot module

dohlee.plot.save(file, dpi=120, tight_layout=True)[source]

Save plot to a file.

Attributes:
file (str): Path to the resulting image file. dpi (int, default=120): Resolution. tight_layout (bool, default=True): Whether to run plt.tight_layout() before saving the plot.
dohlee.plot.set_style(style='white', palette='deep', context='talk', font='Helvetica Neue', font_scale=1.25, rcparams={'figure.figsize': (11.7, 8.27)})[source]
dohlee.plot.frequency(data, order=None, sort_by_values=False, ax=None, **kwargs)[source]

Plot frequency bar chart.

Examples:
frequency([1, 2, 2, 3, 3, 3], order=[3, 1, 2], sort_by_values=True)
Attributes:
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. ax (pyplot axis): Axis to draw the plot.
dohlee.plot.histogram(data, ax=None, **kwargs)[source]
dohlee.plot.volcano(data, x, y, padj, label, cutoff=0.05, sample1=None, sample2=None, ax=None)[source]

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)[source]

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.