Plotting tools

Generate Heatmaps

magine.plotting.heatmaps.cluster_distance_mat(dist_mat, names, figsize=(8, 8))

Creates heatmap from distance matrix.

Parameters

dist_matnp.array

Distance matrix array.

nameslist_like

Names of ticks for distance matrix

figsizetuple

Size of figure, passed to matplotlib

magine.plotting.heatmaps.heatmap_by_terms(data, term_labels, term_sets, colors=None, min_sig=None, convert_to_log=False, y_tick_labels='auto', columns='sample_id', index='identifier', values='fold_change', linewidths=0, cluster_row=False, cluster_col=False, div_colors=False, num_colors=21, figsize=None, annotate_sig=False, **kwargs)

Parameters

datapd.DataFrame

term_labelslist_like

List of labels for grouping

term_setslist_like

List of list like that create the terms

colorslist_like

Colors for plotting, if not provided it will be created

min_sigint

Number of sign

convert_to_logbool

y_tick_labelslist_like

columnsstr

Name of columns of df for pivotn

indexstr

Name of index of df for pivot

valuesstr

Name of values of df for pivot

cluster_colbool

Cluster the data using searborn.clustermap

cluster_rowbool

Cluster rows

div_colorsbool

Use divergent colors for plotting

figsizetuple

Size of figure, passed to matplotlib/seaborn

num_colorsint

Number of colors for color bar

annotate_sigbool

Add ‘*’ annotation to plot for significant changed terms

linewidthsfloat or None

Add white line between plots

min_sigint

Minimum number of significant ‘index’ across samples. Can be used to remove rows that are not significant across any sample.

Returns

plt.Figure

magine.plotting.heatmaps.heatmap_from_array(data, convert_to_log=False, y_tick_labels='auto', cluster_row=False, cluster_col=False, columns='sample_id', index='term_name', values='combined_score', div_colors=False, num_colors=7, figsize=(6, 4), sort_row=None, annotate_sig=False, rank_index=None, linewidths=0.0, cluster_by_set=False, min_sig=0)

Parameters

datamagine.data.base.BaseData

convert_to_logbool

Convert fold_change column to log2 scale

y_tick_labelslist_like

columnsstr

Name of columns of df for pivot

indexstr

Name of index of df for pivot

valuesstr

Name of values of df for pivot

cluster_colbool

Cluster the data using searborn.clustermap

cluster_rowbool

Cluster the data using searborn.clustermap

div_colorsbool

Use divergent colors for plotting

figsizetuple

Size of figure, passed to matplotlib/seaborn

sort_rowstr

Sort rows by (‘index’, ‘mean’, max’)

num_colorsint

Number of colors for color bar

annotate_sigbool

Add ‘*’ annotation to plot for significant changed terms

linewidthsfloat or None

Add white line between plots

cluster_by_set: bool

Cluster by gene set column. Only works for enrichment_array

min_sigint

Minimum number of significant ‘index’ across samples. Can be used to remove rows that are not significant across any sample.

rank_index: bool

Rank rows by index. Deprecated , plus use sort_row arg instead.

Returns

——-

plt.Figure

magine.plotting.species_plotting module

magine.plotting.species_plotting.plot_dataframe(exp_data, html_filename, out_dir='proteins', plot_type='plotly', run_parallel=False)

Creates

Parameters

exp_datamagine.BaseData.

html_filenamestr

out_dir: str, path

Directory that will contain all proteins

plot_typestr

plotly or matplotlib output

run_parallelbool

create plots in parallel

Returns

——-

magine.plotting.species_plotting.plot_genes_by_ont(data, list_of_terms, save_name, out_dir=None, exp_data=None, run_parallel=False, plot_type='plotly')

Creates a figure for each GO term in data

BaseData should be a result of running calculate_enrichment. This function creates a plot of all proteins per term if a term is significant and the number of the reference set is larger than 5 and the total number of species measured is less than 100.

Parameters

datapandas.DataFrame

previously ran enrichment analysis

list_of_termslist_list

save_namestr

name to save file

out_dirstr

output path for file

exp_datamagine.ExperimentalData

data to plot

run_parallelbool

To run in parallel using pathos.multiprocessing

plot_typestr

plotly or matplotlib

Returns

out_arraydict

dict where keys are pointers to figure locations

magine.plotting.species_plotting.plot_species(df, species_list=None, save_name='test', out_dir=None, title=None, plot_type='plotly', image_format='pdf', close_plots=False)

Parameters

df: pandas.DataFrame

magine formatted dataframe

species_list: list

List of genes to be plotter

save_name: str

Filename to be saved as

out_dir: str

Path for output to be saved

title: str

Title of plot, useful when list of genes corresponds to a GO term

plot_typestr

Use plotly to generate html output or matplotlib to generate pdf

image_formatstr

pdf or png, only used if plot_type=”matplotlib”

close_plotsbool

Close plot after making, use when creating lots of plots in parallel.

magine.plotting.species_plotting.write_table_to_html(data, save_name='index', out_dir=None, run_parallel=False, exp_data=None, plot_type='matplotlib')

Creates a html table of plots of genes for each ontology term.

Parameters

datamagine.enrichment.enrichment_result.EnrichmentResult

save_namestr

name of html output file

out_dirstr, optional

output path for all plots

run_parallelbool

Create plots in parallel

exp_datamagine.data.ExperimentalData

plot_typestr {‘matplotlib’, ‘plotly’}

magine.plotting.venn_diagram_maker module

magine.plotting.venn_diagram_maker.create_venn2(list1, list2, label1, label2, save_name=None, title=None, image_format='png', ax=None)

Creates a venn digram containing for 2 lists

Parameters

list1list_like

list2list_like

label1str

label2str

save_namestr

titlestr

image_formatstr, optional

default png

axmatplotlib.axes

magine.plotting.venn_diagram_maker.create_venn3(list1, list2, list3, label1, label2, label3, save_name=None, image_format='png', title=None, ax=None, colors=('g', 'r', 'b'))

Creates a venn digram containing for 3 lists

Parameters

list1list_like

list2list_like

list3list_like

label1str

label2str

label3str

save_namestr

image_formatstr

default png

title: str

axmatplotlib.axes

magine.plotting.volcano_plots module

magine.plotting.volcano_plots.add_volcano_plot(fig_axis, section_0, section_1, section_2)

Adds a volcano plot to a fig axis

Parameters

fig_axisplt.Figure.axes

section_0pd.DataFrame

section_1pd.DataFrame

section_2pd.DataFrame

magine.plotting.volcano_plots.create_mask(data, use_sig=True, p_value=0.1, fold_change_cutoff=1.5)

Creates a mask for volcano plots.

# Visual example of volcano plot # section 0 are significant criteria

# 0 # 1 # 0 # # # # # ################################# # # # # # 2 # 2 # 2 # # # # # #################################

Parameters

datapd.DataFrame

use_sigbool

p_valuefloat

p_value threshold

fold_change_cutofffloat

fold change threshold

magine.plotting.volcano_plots.save_plot(fig, save_name, out_dir=None, image_type='png')

Saves fig

Parameters

figplt.Figure

Figure to be saved

save_namestr

output file name

out_dirstr, optional

output path

image_typestr, optional

output type of file, {“png”, “pdf”, etc..}

magine.plotting.volcano_plots.volcano_plot(data, save_name=None, out_dir=None, sig_column=False, p_value=0.1, fold_change_cutoff=1.5, x_range=None, y_range=None)

Create a volcano plot of data

Creates a volcano plot of data type provided

Parameters

datapandas.DataFrame

data to create volcano plots from

save_name: str

name to save figure

out_dir: str, directory

Location to save figure

sig_column: bool, optional

If to use significant flags of data

p_value: float, optional

Criteria for significant

fold_change_cutoff: float, optional

Criteria for significant

y_range: array_like

upper and lower bounds of plot in y direction

x_range: array_like

upper and lower bounds of plot in x direction