flotilla.visualize.generic module¶

flotilla.visualize.generic.nmf_space_transitions(nmf_space_positions, feature_id, phenotype_to_color, phenotype_to_marker, order, ax=None, xlabel=None, ylabel=None)[source]¶

flotilla.visualize.generic.plot_pooled_dot(ax, pooled, x_offset=0, label=False)[source]¶

Parameters:	ax : matplotlib.axes.Axes Axes object to plot on pooled : pandas.Series Pooled data of this gene

flotilla.visualize.generic.simple_twoway_scatter(sample1, sample2, **kwargs)[source]¶

Plot a two-dimensional scatterplot between two samples

Parameters:	sample1 : pandas.Series Data to plot on the x-axis sample2 : pandas.Series Data to plot on the y-axis Any other keyword arguments valid for seaborn.jointplot
Returns:	jointgrid : seaborn.axisgrid.JointGrid Returns a JointGrid instance See Also seaborn.jointplot

flotilla.visualize.generic.violinplot(data, groupby=None, color_ordered=None, ax=None, pooled_data=None, order=None, violinplot_kws=None, title=None, label_pooled=False, outliers=None, data_type=None)[source]¶

Parameters:

data : pandas.Series The main data to plot as violins groupby : dict-like, optional How to group the samples (e.g. by phenotype) color_ordered : list, optional List of colors, in the order you want to plot ax : matplotlib.Axes, optional Where to plot the violins. If None, get the current axes pooled_data : pandas.Series, optional Pooled samples. Will be plotted as black dots order : list, optional The order in which to plot the phenotypes, e.g. if the data is form a differentiation time course violinplot_kws : dict, optional Other keywords to pass to seaborn.violinplot title : str, optional Title of the plot label_pooled : bool, optional If True, label the sample id of the pooled samples outliers : pandas.Series Outlier samples. Will be plotted in their phenotype category, as a grey shadow data_type : ‘expression’ | ‘splicing’ | None If ‘splicing’, then force the y-axis to be from 0 to 1. If ‘expression’ or None, don’t mess with the y-axis