Module kitchen.plotting

Custom plotting functions for data in pd.DataFrame, anndata.AnnData, and decoupler formats

Functions

def boxplots_group(a, features, groupby, groupby_order=None, groupby_colordict=None, points_colorby=None, points_colordict=None, pairby=None, layer=None, log_scale=None, log_scale_method='functionlog', pseudocount=1.0, sig=True, test_method='t-test', test_transform=None, test_pairs=None, bonferroni=False, sample_n=False, ylabel=None, xlab_rot=None, titles=None, legend=True, size=3, hlines=None, panelsize=(3, 3), ncols=6, outdir='./', save_prefix='', dpi=300)

Plot trends from a.obs metadata. Save all plots in grid of single .png file.

Parameters

a : Union[anndata.Anndata, pd.DataFrame]

The annotated data matrix of shape n_obs by n_vars. Rows correspond to samples and columns to genes. Can also be pd.DataFrame.

features : list of str

List of genes, .obs columns, or DataFrame columns to plot (if a is pd.DataFrame) (y variable)

groupby : list of str

Columns from a or a.obs to group by (x variable)

groupby_order : list of str, optional (default=None)

List of values in a[groupby] or a.obs[groupby] specifying the order of groups on x-axis. If groupby is a list, groupby_order should also be a list with corresponding orders in each element.

groupby_colordict : dict, optional (default=None)

Dictionary of group, color pairs from groupby to color boxes and points by

points_colorby : str, optional (default=None)

Categorical .obs column to color stripplot points by.

points_colordict : dict, optional (default=None)

Dictionary of group, color pairs from points_colorby to color points by

pairby : str, optional (default=None)

Categorical .obs column identifying point pairings to draw lines between across groupby categories.

layer : str, optional (default=None)

Key from layers attribute of adata if present

log_scale : int, optional (default=None)

Set axis scale(s) to log. Numeric values are interpreted as the desired base (e.g. 10). When None, plot defers to the existing Axes scale.

log_scale_method : literal ['functionlog','symlog'], optional (default='functionlog')

Method for adjusting y-axis scale if log_scale is not None

pseudocount : float, optional (default=1.0)

Pseudocount to add to values before log-transforming with base=log_scale

sig : bool, optional (default=False)

Perform significance testing (2-way t-test) between all groups and add significance bars to plot(s)

test_method : literal ["t-test","wilcoxon"], optional (default="t-test")

Method for performing significance testing across groups

test_transform : literal ["log1p","log10p","log2p","arcsinh"], optional (default=None)

Transformation to apply to values before testing. "log1p"=np.log(val+1), "log10p"=np.log10(val+1), "log2p"=np.log2(val+1), "arcsinh"=np.arcsinh(val).

test_pairs : list of tuple, optional (default=None)

List of pairs of groupby values to include in testing. If None, test all levels in groupby pairwise.

bonferroni : bool, optional (default=False)

Adjust significance p-values with simple Bonferroni correction

sample_n : bool, optional (default=False)

Add sample number to x-axis tick labels for each group

ylabel : str, optional (default=None)

Label for y axes. If None use colors.

xlab_rot : int, optional (default=None)

Rotation (in degrees) to force on all x ticklabels. If None, auto-rotate x ticklabels 90 degrees if they're longer than 10 characters.

titles : list of str, optional (default=None)

Titles for each set of axes. If None use features.

legend : bool, optional (default=True)

Add legend to plot

size : int, optional (default=3)

Size of the jitter points

hlines : dict, optional (default=None)

Dictionary containing values to draw horizontal lines at for each feature. e.g. {"feature1":10,"feature2":12,"feature3":8} where "feature1", etc. are found in features.

panelsize : tuple of float, optional (default=(3, 3))

Size of each panel in output figure in inches

ncols : int, optional (default=5)

Number of columns in gridspec

outdir : str, optional (default="./")

Path to output directory for saving plots

save_prefix : str, optional (default="")

Prefix to add to filenames for saving

dpi : float, optional (default=300)

Resolution in dots per inch for saving figure. Ignored if save_prefix is None.

Returns

gs : gridspec.GridSpec

Return gridspec object if save==None. Otherwise, write to .png in outdir/.

sig_out : dict

Dictionary of t-test statistics if sig==True. Otherwise, write to .csv in outdir/.

def build_gridspec(panels, ncols, panelsize=(3, 3))

Create gridspec.GridSpec object from a list of panels

Parameters

panels : list or str

List of panels in plot grid. If string, only one panel is made.

ncols : int

Number of columns in grid. Number of rows is calculated from len(panels) and ncols.

panelsize : tuple of float, optional (default=(3,3))

Size in inches of each panel within the plot grid.

Returns

gs : gridspec.GridSpec

GridSpec object

fig : matplotlib.Figure

Figure object

def calc_significance(df, ax, feature, groupby, groupby_order=None, test_pairs=None, method='t-test', transform=None, bonferroni=False, log_scale=None, **kwargs)

Calculate significance with t-test between two groups

Parameters

df : pd.DataFrame

DataFrame containing data to plot

ax : matplotlib.axes

Axes object to plot significance bars on

feature : str

DataFrame column to plot (y variable)

groupby : list of str

Columns from a or a.obs to group by (x variable)

groupby_order : list of str, optional (default=None)

List of values in df[groupby] specifying the order of groups on x-axis.

test_pairs : list of tuple, optional (default=None)

List of pairs of df[groupby] values to include in testing. If None, test all levels in df[groupby] pairwise.

method : literal ["t-test","wilcoxon"], optional (default="t-test")

Method for performing significance testing across groups

transform : literal ["log1p","log10p","log2p","arcsinh",None], optional (default=None)

Transformation to apply to values before testing. "log1p"=np.log(val+1), "log10p"=np.log10(val+1), "log2p"=np.log2(val+1), "arcsinh"=np.arcsinh(val).

bonferroni : bool, optional (default=False)

Adjust significance p-values with simple Bonferroni correction

log_scale : int, optional (default=None)

Set axis scale(s) to log. Numeric values are interpreted as the desired base (e.g. 10). When None, plot defers to the existing Axes scale.

**kwargs : optional

Keyword args to pass to significance_bar()

Returns

sig_out : dict

Dictionary of t-test statistics if sig==True. Otherwise, write to .csv in outdir/.

def calc_significance_split(df, ax, feature, groupby, splitby, method='t-test', transform=None, groupby_order=None, splitby_order=None, log_scale=None, **kwargs)

Calculate significance with t-test between two groups

Parameters

df : pd.DataFrame

DataFrame containing data to plot

ax : matplotlib.axes

Axes object to plot significance bars on

feature : str

DataFrame column to plot (y variable)

groupby : str

Column from dfa to group by (x variable)

splitby : str

Categorical column from df to split boxes/violins by

method : literal ["t-test","wilcoxon"], optional (default="t-test")

Method for performing significance testing across groups

transform : literal ["log1p","log10p","log2p","arcsinh",None], optional (default=None)

Transformation to apply to values before testing. "log1p"=np.log(val+1), "log10p"=np.log10(val+1), "log2p"=np.log2(val+1), "arcsinh"=np.arcsinh(val).

groupby_order : list of str, optional (default=None)

List of values in df[groupby] specifying the order of groups on x-axis.

splitby_order : list of str, optional (default=None)

Order of categories in df[splitby]

log_scale : int, optional (default=None)

Set axis scale(s) to log. Numeric values are interpreted as the desired base (e.g. 10). When None, plot defers to the existing Axes scale.

**kwargs : optional

Keyword args to pass to significance_bar()

Returns

sig_out : dict

Dictionary of t-test statistics if sig==True. Otherwise, write to .csv in outdir/.

def cluster_pie(adata, pie_by='batch', groupby='leiden', ncols=5, show=None, figsize=(5, 5))

Plots pie graphs showing makeup of cluster groups

Parameters

adata : anndata.AnnData

the data

pie_by : str, optional (default="batch")

adata.obs column to split pie charts by

groupby : str, optional (default="leiden")

adata.obs column to create pie charts for

ncols : int, optional (default=5)

number of columns in gridspec

show : bool, optional (default=None)

show figure or just return axes

figsize : tuple of float, optional (default=(5,5))

size of matplotlib figure

Returns

matplotlib gridspec with access to the axes

 

def custom_heatmap(adata, groupby, features=None, vars_dict=None, cluster_obs=False, cluster_vars=False, groupby_order=None, groupby_colordict='native', layer=None, plot_type='dotplot', cmap='Greys', log_scale=None, linthresh=1.0, italicize_vars=True, colorbar_title='Mean expression\nin group', vars_axis='y', figsize=(5, 5), save=None, dpi=300, **kwargs)

Custom wrapper around sc.pl.dotplot, sc.pl.matrixplot, and sc.pl.stacked_violin

Parameters

adata : sc.AnnData

AnnData object to plot from

groupby : str

Categorical column of adata.obs to group dotplot by

features : list of str (default=None)

List of features from adata.obs.columns or adata.var_names to plot. If None, then vars_dict must provided.

vars_dict : dict, optional (default=None)

Dictionary of groups of vars to highlight with brackets on dotplot. Keys are variable group names and values are list of variables found in features. If provided, features is ignored.

cluster_obs : bool, optional (default=False)

Hierarchically cluster groupby observations and show dendrogram

cluster_vars : bool, optional (default=False)

Hierarchically cluster features for a prettier dotplot. If True, return features in their new order (first return variable).

groupby_order : list, optional (default=None)

Explicit order for groups of observations from adata.obs[groupby]

groupby_colordict : dict, str, or None, optional (default='native')

Dictionary mapping groupby categories (keys) to colors (values) for the group axis text. If groupby_colordict == 'native', use the colors available in adata.uns[f'{groupby}_colors']. If None, don't color group text on plot.

layer : str

Key from adata.layers to use for plotting gene values

plot_type : str, optional (default="dotplot")

One of "dotplot", "matrixplot", "dotmatrix", "stacked_violin", or "heatmap"

cmap : str, optional (default="Greys")

matplotlib colormap for dots

log_scale : int, optional (default=None)

Set axis scale(s) to symlog. Numeric values are interpreted as the desired base (e.g. 10). When None, plot defers to the existing Axes scale.

linthresh : float, optional (default=1.0)

The range within which the color scale is linear (to avoid having the plot go to infinity around zero).

italicize_vars : bool, optional (default=True)

Whether or not to italicize variable names on plot

colorbar_title : str, optional (default="Mean expression\nin group")

Title for colorbar key

vars_axis : str, optional (default="y")

If "y", vars are shown on y-axis. If "x", vars are shown on x-axis.

figsize : tuple of float, optional (default=(5,5))

Size of the figure in inches

save : str or None, optional (default=None)

Path to file to save image to. If None, return figure object (second return variable if cluster_vars == True).

dpi : float, optional (default=300)

Resolution in dots per inch for saving figure. Ignored if save is None.

**kwargs

Keyword arguments to pass to sc.pl.dotplot, sc.pl.matrixplot, sc.pl.stacked_violin, or sc.pl.heatmap

Returns

features_ordered : list of str

If cluster_vars == True, reordered features based on hierarchical clustering

figure : sc._plotting object (DotPlot, matrixplot, stacked_violin)

If save == None, scanpy plotting object is returned

def decoupler_dotplot(df, x, y, c, s, largest_dot=50, cmap='Reds', title=None, figsize=(3, 5), ax=None, return_fig=False, save=None, dpi=200)

Plot results of decoupler enrichment analysis as dots.

Parameters

df : DataFrame

Results of enrichment analysis.

x : str

Column name of df to use as continous value.

y : str

Column name of df to use as labels.

c : str

Column name of df to use for coloring.

s : str

Column name of df to use for dot size.

largest_dot : int

Parameter to control the size of the dots in points.

cmap : str

Colormap to use.

title : str, None

Text to write as title of the plot.

figsize : tuple

Figure size.

ax : Axes, None

A matplotlib axes object. If None returns new figure.

return_fig : bool

Whether to return a Figure object or not.

save : str, None

Path to where to save the plot. Infer the filetype if ending in {.pdf, .png, .svg}.

dpi : float, optional (default=200)

Resolution in dots per inch for saving figure. Ignored if save is None.

Returns

fig : matplotlib.Figure, None

If return_fig==True, returns figure object.

def decoupler_dotplot_facet(df, group_col='group', x='Combined score', y='Term', c='FDR p-value', s='Overlap ratio', top_n=None, ncols=4, figsize_scale=1.5, save=None, dpi=200, **kwargs)

Plot results of decoupler enrichment analysis as dots, faceted by group

Parameters

df : DataFrame

results of enrichment analysis.

group_col : str

column from df to facet by

x : str

column name of df to use as continous value.

y : str

column name of df to use as labels.

c : str

column name of df to use for coloring.

s : str

column name of df to use for dot size.

top_n : int

number of top terms to plot per group. If None show all terms.

ncols : int

number of columns for faceting. If None use len(df[group_col].unique())

figsize_scale : float

scale size of matplotlib figure

save : str

path to file to save image to. If None, return axes objects

dpi : float, optional (default=200)

Resolution in dots per inch for saving figure. Ignored if save is None.

**kwargs

keyword arguments to pass to decoupler_dotplot() function

Returns

fig : matplotlib.Figure

Return figure object if save==None. Otherwise, write to save.

def jointgrid_boxplots_category(a, x, y, color, figheight=5, sig=True, bonferroni=False, cmap_dict=None, stripplot=True, outdir='./', save_prefix=None, dpi=300)

Jointplot with scatter between two variables and marginal boxplots showing distributions and stats across a third variable (color)

Parameters

a : Union[anndata.Anndata, pd.DataFrame]

The annotated data matrix of shape n_obs by n_vars. Rows correspond to samples and columns to genes. Can also be pd.DataFrame.

x : str

Column from a or a.obs to plot on x axis of jointgrid

y : str

Column from a or a.obs to plot on y axis of jointgrid

color : str

Column from a or a.obs containing categories for marginal boxplots and statistics in x and y.

figheight : float, optional (default=5)

Size of output figure in inches (it will be square)

sig : bool, optional (default=True)

Perform significance testing (2-way t-test) between all groups and add significance bars to marginal boxplots

bonferroni : bool, optional (default=False)

Adjust significance p-values with simple Bonferroni correction

cmap_dict : dictionary, optional (default=None)

Dictionary of group, color pairs from color to color boxes and points by

stripplot : bool, optional (default=True)

Plot stripplot with jittered points over marginal boxplots

outdir : str

Path to output directory for saving plots

save_prefix : str, optional (default=None)

Prefix to add to filenames for saving. If None, don't save anything.

dpi : float, optional (default=300)

Resolution in dots per inch for saving figure. Ignored if save_prefix is None.

Returns

If save_prefix!=None, saves plot as .png file to outdir and stats (if

sig==True) to .csv file.

g : sns.JointGrid

Plot object

sig_out : dict

Dictionary containing statistics (if sig==True)

def jointgrid_boxplots_threshold(a, x, y, color, x_thresh, figheight=5, sig=True, cmap_dict=None, stripplot=True, dodge_by_color=False, outdir='./', save_prefix=None, dpi=300)

Jointplot with scatter between two variables and marginal boxplots showing distributions and stats across a third variable (color, x values in y margin) and a threshold of x (x_thresh, y values in x margin)

Parameters

a : Union[anndata.Anndata, pd.DataFrame]

The annotated data matrix of shape n_obs by n_vars. Rows correspond to samples and columns to genes. Can also be pd.DataFrame.

x : str

Column from a or a.obs to plot on x axis of jointgrid

y : str

Column from a or a.obs to plot on y axis of jointgrid

color : str

Column from a or a.obs containing categories for marginal boxplots and statistics in x and y.

x_thresh : float

Threshold along x for which to split points by and plot marginal boxplots for y

figheight : float, optional (default=5)

Size of output figure in inches (it will be square)

sig : bool, optional (default=True)

Perform significance testing (2-way t-test) between all groups and add significance bars to marginal boxplots

cmap_dict : dictionary, optional (default=None)

Dictionary of group, color pairs from color to color boxes and points by

stripplot : bool, optional (default=True)

Plot stripplot with jittered points over marginal boxplots

dodge_by_color : bool, optional (default=False)

Dodge boxplots and stripplots in y-marginal axes by color. If False, only one boxplot per category ("-lo" and "-hi" based on x_thresh), with jitterplot points colored by color if stripplot==True.

outdir : str

Path to output directory for saving plots

save_prefix : str, optional (default=None)

Prefix to add to filenames for saving. If None, don't save anything.

dpi : float, optional (default=300)

Resolution in dots per inch for saving figure. Ignored if save_prefix is None.

Returns

If save_prefix!=None, saves plot as .png file to outdir and stats (if

sig==True) to .csv file.

g : sns.JointGrid

Plot object

sig_out : dict

Dictionary containing statistics (if sig==True)

def list_union(lst1, lst2)

Combines two lists by the union of their values

Parameters

lst1, lst2 : list

lists to combine

Returns

final_list : list

union of values in lst1 and lst2

def myexpm1(x, base=2, pseudocount=0.1, myround=False)

Custom expm1 function

def mylog1p(x, base=2, pseudocount=0.1)

Custom log1p function

def myround(x, n_precision=3)

Custom rounding function

def pie_from_col(df, col, title=None, figsize=(8, 8), save_to=None)

Create a pie chart from the values in a pd.DataFrame column

Parameters

df : pd.DataFrame

Dataframe from which to plot

col : str

Column in df to create pie chart for

title : str

Title of plot

figsize : tuple of float, optional (default=(8,8))

Size of figure

save_to : str, optional (default=None)

Path to image file to save plot to

def plot_embedding(adata, basis='X_umap', colors=None, show_clustering=True, ncols=5, n_cnmf_markers=7, figsize_scale=1.0, cmap='viridis', seed=18, save_to=None, verbose=True, **kwargs)

Plots reduced-dimension embeddings of single-cell dataset

Parameters

adata : anndata.AnnData

object containing preprocessed and dimension-reduced counts matrix

basis : str, optional (default="X_umap")

key from adata.obsm containing embedding coordinates

colors : list of str, optional (default=None)

colors to plot; can be genes or .obs columns

show_clustering : bool, optional (default=True)

plot PAGA graph and leiden clusters on first two axes

basis : str, optional (default="X_umap")

embedding to plot - key from adata.obsm

ncols : int, optional (default=5)

number of columns in gridspec

n_cnmf_markers : int, optional (default=7)

number of top genes to print on cNMF plots

figsize_scale : float, optional (default=1.0)

scaler for figure size. calculated using ncols to keep each panel square. values < 1.0 will compress figure, > 1.0 will expand.

cmap : str, optional (default="viridis")

valid color map for the plot

seed : int, optional (default=18)

random state for plotting PAGA

save_to : str, optional (default=None)

path to .png file for saving figure; default is plt.show()

verbose : bool, optional (default=True)

print updates to console

**kwargs : optional

args to pass to sc.pl.embedding (e.g. "size", "add_outline", etc.)

Returns

plot of embedding with overlays from "colors" as matplotlib gridspec object,

 

unless save_to is not None.

def rank_genes_cnmf(adata, attr='varm', keys='cnmf_spectra', indices=None, labels=None, titles=None, color='black', n_points=20, ncols=5, log=False, show=None, figsize=(5, 5))

Plots rankings. [Adapted from scanpy.plotting._anndata.ranking]

See, for example, how this is used in pl.pca_ranking.

Parameters

adata : anndata.AnnData

the data

attr : str {'var', 'obs', 'uns', 'varm', 'obsm'}

the attribute of adata that contains the score

keys : str or list of str, optional (default="cnmf_spectra")

scores to look up an array from the attribute of adata

indices : list of int, optional (default=None)

the column indices of keys for which to plot (e.g. [0,1,2] for first three keys)

labels : list of str, optional (default=None)

Labels to use for features displayed as plt.txt objects on the axes

titles : list of str, optional (default=None)

Labels for titles of each plot panel, in order

ncols : int, optional (default=5)

number of columns in gridspec

show : bool, optional (default=None)

show figure or just return axes

figsize : tuple of float, optional (default=(5,5))

size of matplotlib figure

Returns

matplotlib gridspec with access to the axes

 

def rank_genes_groups(adata, groups=None, n_genes=20, gene_symbols=None, key='rank_genes_groups', fontsize=8, ncols=4, sharey=True, save_to=None, ax=None, include_lowest=False, panelsize=(4, 4), **kwds)

Plot ranking of genes with optional inclusion of lowest ranking genes. Adapted from sc.pl.rank_genes_groups

Parameters

adata : AnnData

Annotated data matrix.

groups : str | Sequence[str] | None, optional

The groups for which to show the gene ranking.

n_genes : int, optional

Number of genes to show. Default is 20.

gene_symbols : str | None, optional

Key for field in .var that stores gene symbols if you do not want to use .var_names.

key : str | None, optional

Key for accessing the rank genes groups in adata.uns. Default is "rank_genes_groups".

fontsize : int, optional

Fontsize for gene names. Default is 8.

ncols : int, optional

Number of panels shown per row. Default is 4.

sharey : bool, optional

Controls if the y-axis of each panel should be shared. By passing sharey=False, each panel has its own y-axis range. Default is True.

save_to : bool | None, optional

Path to save the figure to. If None, the figure is not saved.

ax : Axes | None, optional

Matplotlib Axes object to draw the plots on. If None, a new figure is created.

include_lowest : bool, optional

Whether to include the lowest ranking genes. Default is False.

panelsize : tuple, optional

Tuple specifying the (width, height) of each panel. Default is (4, 4).

**kwds

Additional keyword arguments.

Returns

List of each group’s matplotlib axis or None if show=True.

def save_plot(fig, ax, save)

def significance_bar(start, end, height, displaystring, linewidth=1.2, markersize=8, boxpad=0.3, fontsize=15, color='k', ax=None, horizontal=False)

Draw significance bracket on matplotlib figure

def split_violin(a, features, groupby=None, groupby_order=None, splitby=None, splitby_order=None, points_colorby=None, points_colordict=None, layer=None, log_scale=None, log_scale_method='functionlog', pseudocount=1.0, plot_type='violin', scale='width', sig=False, test_method='t-test', test_transform=None, split=True, strip=True, jitter=True, size=3, hlines=None, panelsize=(3, 3), ncols=1, sample_n=False, ylabel=None, xlab_rot=None, titles=None, legend=True, save=None, dpi=300)

Plot genes grouped by one variable and split by another

Parameters

a : Union[anndata.Anndata, pd.DataFrame]

The annotated data matrix of shape n_obs by n_vars. Rows correspond to samples and columns to genes. Can also be pd.DataFrame.

features : list of str

List of genes, .obs columns, or DataFrame columns to plot (if a is pd.DataFrame).

groupby : str, optional (default=None)

Column from a or a.obs to group by (x variable)

groupby_order : list of str, optional (default=None)

List of values in a[groupby] or a.obs[groupby] specifying the order of groups on x-axis. If groupby is a list, groupby_order should also be a list with corresponding orders in each element.

splitby : str, optional (default=None)

Categorical .obs column to split violins by.

splitby_order : list of str, optional (default=None)

Order of categories in adata.obs[splitby].

points_colorby : str, optional (default=None)

Categorical .obs column to color stripplot points by.

points_colordict : dict, optional (default=None)

Dictionary of group, color pairs from points_colorby to color points by

layer : str, optional (default=None)

Key from layers attribute of adata if present

log_scale : int, optional (default=None)

Set axis scale(s) to log. Numeric values are interpreted as the desired base (e.g. 10). When None, plot defers to the existing Axes scale.

log_scale_method : literal ['functionlog','symlog'], optional (default='functionlog')

Method for adjusting y-axis scale if log_scale is not None

pseudocount : float, optional (default=1.0)

Pseudocount to add to values before log-transforming with base=log_scale

plot_type : str, optional (default="violin")

"violin" for violinplot, "box" for boxplot

scale : str, optional (default="width")

See :func:~seaborn.violinplot.

sig : bool, optional (default=False)

Perform significance testing (2-way t-test) between all groups across split_by and add significance bars to plot(s)

test_method : literal ["t-test","wilcoxon"], optional (default="t-test")

Method for performing significance testing across groups

test_transform : literal ["log1p","log10p","log2p","arcsinh"], optional (default=None)

Transformation to apply to values before testing. "log1p"=np.log(val+1), "log10p"=np.log10(val+1), "log2p"=np.log2(val+1), "arcsinh"=np.arcsinh(val).

split : bool, optional (default=True)

Split box/violinplots and dodge stripplots if splitby is not None

strip : bool, optional (default=True)

Show a strip plot on top of the violin plot.

jitter : Union[int, float, bool], optional (default=True)

If set to 0, no points are drawn. See :func:~seaborn.stripplot.

size : int, optional (default=3)

Size of the jitter points

hlines : dict, optional (default=None)

Dictionary containing values to draw horizontal lines at for each feature. e.g. {"feature1":10,"feature2":12,"feature3":8} where "feature1", etc. are found in features.

panelsize : tuple of int, optional (default=(3, 3))

Size of each panel in output figure in inches

ncols : int, optional (default=1)

Number of columns in gridspec. If None use len(features).

sample_n : bool, optional (default=False)

Add sample number to x-axis tick labels for each group

ylabel : str, optional (default=None)

Label for y axes. If None use colors.

xlab_rot : int, optional (default=None)

Rotation (in degrees) to force on all x ticklabels. If None, auto-rotate x ticklabels 90 degrees if they're longer than 10 characters.

titles : list of str, optional (default=None)

Titles for each set of axes. If None use features.

legend : bool, optional (default=True)

Add legend to plot

save : str, optional (default=None)

Path to file to save image to. If None, return axes objects

dpi : float, optional (default=300)

Resolution in dots per inch for saving figure. Ignored if save is None.

Returns

fig : matplotlib.Figure

Return figure object if save==None. Otherwise, write to save.

sig_out : dict

Dictionary of t-test statistics if sig==True. Otherwise, write to .csv in outdir/.