# -*- coding: utf-8 -*-
import operator
import sys
import warnings
from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
import matplotlib.pyplot as plt
import matplotlib.transforms as transforms
import numpy as np
import pandas as pd
import scipy.cluster.hierarchy as sch
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.category import UnitData
from matplotlib.colors import Normalize
from matplotlib.figure import Figure
from matplotlib.lines import Line2D
from matplotlib.ticker import MaxNLocator
from gseapy.scipalette import SciPalette
[docs]
class MidpointNormalize(Normalize):
def __init__(self, vmin=None, vmax=None, vcenter=None, clip=False):
self.vcenter = vcenter
Normalize.__init__(self, vmin, vmax, clip)
def __call__(self, value, clip=None):
# I'm ignoring masked values and all kinds of edge cases to make a
# simple example...
# Note also that we must extrapolate beyond vmin/vmax
x, y = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y, left=-np.inf, right=np.inf))
def inverse(self, value):
y, x = [self.vmin, self.vcenter, self.vmax], [0, 0.5, 1]
return np.interp(value, x, y, left=-np.inf, right=np.inf)
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def zscore(data2d: pd.DataFrame, axis: Optional[int] = 0):
"""Standardize the mean and variance of the data axis Parameters.
:param data2d: DataFrame to normalize.
:param axis: int, Which axis to normalize across. If 0, normalize across rows,
if 1, normalize across columns. If None, don't change data
:Returns: Normalized DataFrame. Normalized data with a mean of 0 and variance of 1
across the specified axis.
"""
if axis is None:
# normalized to mean and std using entire matrix
# z_scored = (data2d - data2d.values.mean()) / data2d.values.std(ddof=1)
return data2d
assert axis in [0, 1]
z_scored = data2d.apply(
lambda x: (x - x.mean()) / x.std(ddof=1), axis=operator.xor(1, axis)
)
return z_scored
class Heatmap(object):
def __init__(
self,
df: pd.DataFrame,
z_score: Optional[int] = None,
title: Optional[str] = None,
figsize: Tuple[float, float] = (5, 5),
cmap: Optional[str] = None,
xticklabels: bool = True,
yticklabels: bool = True,
ofname: Optional[str] = None,
**kwargs,
):
self.title = "" if title is None else title
self.figsize = figsize
self.xticklabels = xticklabels
self.yticklabels = yticklabels
self.ofname = ofname
# scale dataframe
df = df.astype(float)
df = zscore(df, axis=z_score)
df = df.iloc[::-1]
self.data = df
self.cbar_title = "Norm.Exp" if z_score is None else "Z-Score"
self.cmap = cmap
if cmap is None:
self.cmap = SciPalette.create_colormap() # navyblue2darkred
self._zscore = z_score
def _skip_ticks(self, labels, tickevery):
"""Return ticks and labels at evenly spaced intervals."""
n = len(labels)
if tickevery == 0:
ticks, labels = [], []
elif tickevery == 1:
ticks, labels = np.arange(n) + 0.5, labels
else:
start, end, step = 0, n, tickevery
ticks = np.arange(start, end, step) + 0.5
labels = labels[start:end:step]
return ticks, labels
def _auto_ticks(self, ax, labels, axis):
transform = ax.figure.dpi_scale_trans.inverted()
bbox = ax.get_window_extent().transformed(transform)
size = [bbox.width, bbox.height][axis]
axis = [ax.xaxis, ax.yaxis][axis]
(tick,) = ax.xaxis.set_ticks([0])
fontsize = tick.label1.get_size()
max_ticks = int(size // (fontsize / 72))
if max_ticks < 1:
tickevery = 1
else:
tickevery = len(labels) // max_ticks + 1
return tickevery
def get_ax(self):
if hasattr(sys, "ps1") and (self.ofname is None):
fig = plt.figure(figsize=self.figsize)
else:
fig = Figure(figsize=self.figsize)
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
self.fig = fig
return ax
def draw(self):
df = self.data
ax = self.get_ax()
vmin = np.percentile(df, 2)
vmax = np.percentile(df, 98)
if self._zscore is None:
norm = Normalize(vmin=vmin, vmax=vmax)
cbar_locator = MaxNLocator(nbins=5, integer=True)
else:
norm = MidpointNormalize(vmin=vmin, vmax=vmax, vcenter=0)
cbar_locator = MaxNLocator(nbins=3, symmetric=True) # symmetric=True
matrix = ax.pcolormesh(
df.values,
cmap=self.cmap,
norm=norm,
rasterized=True,
)
xstep = self._auto_ticks(ax, df.columns.values, 0)
ystep = self._auto_ticks(ax, df.index.values, 1)
xticks, xlabels = self._skip_ticks(df.columns.values, tickevery=xstep)
yticks, ylabels = self._skip_ticks(df.index.values, tickevery=ystep)
ax.set_ylim([0, len(df)])
ax.set(xticks=xticks, yticks=yticks)
ax.set_xticklabels(
xlabels if self.xticklabels else "", fontsize=14, rotation=90
)
ax.set_yticklabels(ylabels if self.yticklabels else "", fontsize=14)
ax.set_title(self.title, fontsize=20, fontweight="bold")
ax.tick_params(
axis="both", which="both", bottom=False, top=False, right=False, left=False
)
# cax=fig.add_axes([0.93,0.25,0.05,0.20])
cbar = self.fig.colorbar(matrix, shrink=0.3, aspect=10) # ticks=[-1, 0, 1]
cbar.ax.yaxis.set_tick_params(
color="white", direction="in", left=True, right=True
)
# Add colorbar, make sure to specify tick locations to match desired ticklabels
cbar.locator = cbar_locator # LinearLocator(3)
cbar.update_ticks()
cbar.ax.set_title(self.cbar_title, loc="left", fontweight="bold")
for key, spine in cbar.ax.spines.items():
spine.set_visible(False)
# cbar = colorbar(matrix)
for side in ["top", "right", "left", "bottom"]:
ax.spines[side].set_visible(False)
# cbar.ax.spines[side].set_visible(False)
return ax
[docs]
def heatmap(
df: pd.DataFrame,
z_score: Optional[int] = None,
title: str = "",
figsize: Tuple[float, float] = (5, 5),
cmap: Optional[str] = None,
xticklabels: bool = True,
yticklabels: bool = True,
ofname: Optional[str] = None,
**kwargs,
):
"""Visualize the dataframe.
:param df: DataFrame from expression table.
:param z_score: 0, 1, or None. z_score axis{0, 1}. If None, not scale.
:param title: figure title.
:param figsize: heatmap figsize.
:param cmap: matplotlib colormap. e.g. "RdBu_r".
:param xticklabels: bool, whether to show xticklabels.
:param xticklabels: bool, whether to show xticklabels.
:param ofname: output file name. If None, don't save figure
"""
ht = Heatmap(df, z_score, title, figsize, cmap, xticklabels, yticklabels, ofname)
ax = ht.draw()
if ofname is None:
return ax
# canvas.print_figure(ofname, bbox_inches='tight', dpi=300)
ht.fig.savefig(ofname, bbox_inches="tight", dpi=300)
class GSEAPlot(object):
def __init__(
self,
term: str,
tag: Sequence[int],
runes: Sequence[float],
nes: float,
pval: float,
fdr: float,
rank_metric: Optional[Sequence[float]] = None,
pheno_pos: str = "",
pheno_neg: str = "",
color: Optional[str] = None,
figsize: Tuple[float, float] = (6, 5.5),
cmap: str = "seismic",
ofname: Optional[str] = None,
ax: Optional[plt.Axes] = None,
**kwargs,
):
"""
:param term: gene_set name
:param tag: hit indices of rank_metric.index presented in gene set S.
:param runes: running enrichment scores.
:param nes: Normalized enrichment scores.
:param pval: nominal p-value.
:param fdr: false discovery rate.
:param rank_metric: pd.Series for rankings, rank_metric.values.
:param pheno_pos: phenotype label, positive correlated.
:param pheno_neg: phenotype label, negative correlated.
:param figsize: matplotlib figsize.
:param ofname: output file name. If None, don't save figure
"""
# dataFrame of ranked matrix scores
self.color = "#88C544" if color is None else color
self._x = np.arange(len(runes))
self.rankings = None
self._zero_score_ind = None
self._z_score_label = None
if rank_metric is not None:
self.rankings = np.asarray(rank_metric)
self._zero_score_ind = np.abs(self.rankings).argmin()
self._z_score_label = "Zero score at " + str(self._zero_score_ind)
self.RES = np.asarray(runes)
self.figsize = figsize
self.term = term
self.cmap = cmap
self.ofname = ofname
self._pos_label = pheno_pos
self._neg_label = pheno_neg
self._hit_indices = tag
self.module = "tmp" if ofname is None else ofname.split(".")[-2]
if self.module == "ssgsea":
self._nes_label = "ES: " + "{:.3f}".format(float(nes))
self._pval_label = "Pval: invliad for ssgsea"
self._fdr_label = "FDR: invalid for ssgsea"
else:
self._nes_label = "NES: " + "{:.3f}".format(float(nes))
self._pval_label = "Pval: " + "{:.3e}".format(float(pval))
self._fdr_label = "FDR: " + "{:.3e}".format(float(fdr))
# output truetype
plt.rcParams.update({"pdf.fonttype": 42, "ps.fonttype": 42})
# in most case, we will have many plots, so do not display plots
# It's also usefull to run this script on command line.
# GSEA Plots
if ax is None:
if hasattr(sys, "ps1") and (self.ofname is None):
# working inside python console, show figure
self.fig = plt.figure(figsize=self.figsize, facecolor="white")
else:
# If working on command line, don't show figure
self.fig = Figure(figsize=self.figsize, facecolor="white")
self._canvas = FigureCanvas(self.fig)
else:
self.fig = ax.figure
self.fig.suptitle(self.term, fontsize=16, wrap=True, fontweight="bold")
def axes_rank(self, rect):
"""
rect : sequence of float
The dimensions [left, bottom, width, height] of the new axes. All
quantities are in fractions of figure width and height.
"""
# Ranked Metric Scores Plot
ax1 = self.fig.add_axes(rect)
if self.module == "ssgsea":
ax1.fill_between(self._x, y1=np.log(self.rankings), y2=0, color="#C9D3DB")
ax1.set_ylabel("log ranked metric", fontsize=16, fontweight="bold")
else:
ax1.fill_between(self._x, y1=self.rankings, y2=0, color="#C9D3DB")
ax1.set_ylabel("Ranked metric", fontsize=16, fontweight="bold")
ax1.text(
0.05,
0.9,
self._pos_label,
color="red",
horizontalalignment="left",
verticalalignment="top",
transform=ax1.transAxes,
)
ax1.text(
0.95,
0.05,
self._neg_label,
color="Blue",
horizontalalignment="right",
verticalalignment="bottom",
transform=ax1.transAxes,
)
# the x coords of this transformation are data, and the y coord are axes
trans1 = transforms.blended_transform_factory(ax1.transData, ax1.transAxes)
ax1.vlines(
self._zero_score_ind,
0,
1,
linewidth=0.5,
transform=trans1,
linestyles="--",
color="grey",
)
hap = self._zero_score_ind / max(self._x)
if hap < 0.25:
ha = "left"
elif hap > 0.75:
ha = "right"
else:
ha = "center"
ax1.text(
hap,
0.5,
self._z_score_label,
horizontalalignment=ha,
verticalalignment="center",
transform=ax1.transAxes,
fontsize=14,
)
ax1.set_xlabel("Gene Rank", fontsize=16, fontweight="bold")
ax1.spines["top"].set_visible(False)
ax1.tick_params(
axis="both", which="both", top=False, right=False, left=False, labelsize=14
)
ax1.locator_params(axis="y", nbins=5)
ax1.yaxis.set_major_formatter(
plt.FuncFormatter(lambda tick_loc, tick_num: "{:.1f}".format(tick_loc))
)
def axes_hits(self, rect, bottom: bool = False):
"""
rect : sequence of float
The dimensions [left, bottom, width, height] of the new axes. All
quantities are in fractions of figure width and height.
"""
# gene hits
ax2 = self.fig.add_axes(rect)
# the x coords of this transformation are data, and the y coord are axes
trans2 = transforms.blended_transform_factory(ax2.transData, ax2.transAxes)
# to make axes shared with same x cooridincates, make the vlines same ranges to x
ax2.vlines(
[self._x[0], self._x[-1]],
0,
1,
linewidth=0.5,
transform=trans2,
color="white",
alpha=0,
) # alpha 0 to transparency
# add hits line
ax2.vlines(
self._hit_indices, 0, 1, linewidth=0.5, transform=trans2, color="black"
)
ax2.tick_params(
axis="both",
which="both",
bottom=bottom,
top=False,
right=False,
left=False,
labelbottom=bottom,
labelleft=False,
)
if bottom:
ax2.set_xlabel("Gene Rank", fontsize=16, fontweight="bold")
ax2.spines["bottom"].set_visible(True)
def axes_cmap(self, rect):
"""
rect : sequence of float
The dimensions [left, bottom, width, height] of the new axes. All
quantities are in fractions of figure width and height.
"""
# center color map at midpoint = 0
mat = self.rankings
if self.rankings is None:
mat = self.RES
vmin = np.percentile(mat.min(), 2)
vmax = np.percentile(mat.max(), 98)
midnorm = MidpointNormalize(vmin=vmin, vcenter=0, vmax=vmax)
# colormap
ax3 = self.fig.add_axes(rect)
ax3.pcolormesh(
mat[np.newaxis, :],
rasterized=True,
norm=midnorm,
cmap=self.cmap,
) # cm.coolwarm
ax3.spines["bottom"].set_visible(False)
ax3.tick_params(
axis="both",
which="both",
bottom=False,
top=False,
right=False,
left=False,
labelbottom=False,
labelleft=False,
)
def axes_stat(self, rect):
"""
rect : sequence of float
The dimensions [left, bottom, width, height] of the new axes. All
quantities are in fractions of figure width and height.
"""
# Enrichment score plot
ax4 = self.fig.add_axes(rect)
ax4.plot(self._x, self.RES, linewidth=4, color=self.color)
ax4.text(0.1, 0.1, self._fdr_label, transform=ax4.transAxes, fontsize=14)
ax4.text(0.1, 0.2, self._pval_label, transform=ax4.transAxes, fontsize=14)
ax4.text(0.1, 0.3, self._nes_label, transform=ax4.transAxes, fontsize=14)
# the y coords of this transformation are data, and the x coord are axes
trans4 = transforms.blended_transform_factory(ax4.transAxes, ax4.transData)
ax4.hlines(0, 0, 1, linewidth=1, transform=trans4, color="grey")
ax4.set_ylabel("Enrichment Score", fontsize=16, fontweight="bold")
# ax4.set_xlim(min(self._x), max(self._x))
ax4.tick_params(
axis="both",
which="both",
bottom=False,
top=False,
right=False,
labelbottom=False,
labelsize=14,
)
ax4.locator_params(axis="y", nbins=5)
# FuncFormatter need two argument, I don't know why. this lambda function used to format yaxis tick labels.
ax4.yaxis.set_major_formatter(
plt.FuncFormatter(lambda tick_loc, tick_num: "{:.1f}".format(tick_loc))
)
self.ax = ax4
def add_axes(self):
"""
Please check matplotlib docs about how to `add_axes` to figure.
Here is a more flexible way to create a new gseaplot.
For example, don't show ranking and merge hits and colormap together
just used:
self.axes_stat([0.1,0.2,0.8,0.8]) # axes_stat should be called first
self.axes_cmap([0.1,0.1,0.8,0.1])
self.axes_hits([0.1,0.1,0.8,0.1])
"""
left = 0.1
width = 0.8
bottom = 0.1
height = 0
stat_height_ratio = 0.4
hits_height_ratio = 0.05
cmap_height_ratio = 0.05
rank_height_ratio = 0.3
## make stat /hits height ratio const
if self.rankings is None:
rank_height_ratio = 0
cmap_height_ratio = 0
base = 0.8 / (
stat_height_ratio
+ hits_height_ratio
+ cmap_height_ratio
+ rank_height_ratio
)
# for i, hit in enumerate(self.hits):
# height = hits_height_ratio * base
if self.rankings is not None:
height = rank_height_ratio * base
self.axes_rank([left, bottom, width, height])
bottom += height
height = cmap_height_ratio * base
self.axes_cmap([left, bottom, width, height])
bottom += height
height = hits_height_ratio * base
self.axes_hits(
[left, bottom, width, height], bottom=False if bottom > 0.1 else True
)
bottom += height
height = stat_height_ratio * base
self.axes_stat([left, bottom, width, height])
# self.fig.subplots_adjust(hspace=0)
# self.fig.tight_layout()
def savefig(self, bbox_inches="tight", dpi=300):
# if self.ofname is not None:
if hasattr(sys, "ps1") and (self.ofname is not None):
self.fig.savefig(self.ofname, bbox_inches=bbox_inches, dpi=dpi)
elif self.ofname is None:
return
else:
self._canvas.print_figure(self.ofname, bbox_inches=bbox_inches, dpi=300)
return
[docs]
def gseaplot(
term: str,
hits: Sequence[int],
nes: float,
pval: float,
fdr: float,
RES: Sequence[float],
rank_metric: Optional[Sequence[float]] = None,
pheno_pos: str = "",
pheno_neg: str = "",
color: str = "#88C544",
figsize: Tuple[float, float] = (6, 5.5),
cmap: str = "seismic",
ofname: Optional[str] = None,
**kwargs,
) -> Optional[List[plt.Axes]]:
"""This is the main function for generating the gsea plot.
:param term: gene_set name
:param hits: hits indices of rank_metric.index presented in gene set S.
:param nes: Normalized enrichment scores.
:param pval: nominal p-value.
:param fdr: false discovery rate.
:param RES: running enrichment scores.
:param rank_metric: pd.Series for rankings, rank_metric.values.
:param pheno_pos: phenotype label, positive correlated.
:param pheno_neg: phenotype label, negative correlated.
:param color: color for RES and hits.
:param figsize: matplotlib figsize.
:param ofname: output file name. If None, don't save figure
return matplotlib.Figure.
"""
g = GSEAPlot(
term,
hits,
RES,
nes,
pval,
fdr,
rank_metric,
pheno_pos,
pheno_neg,
color,
figsize,
cmap,
ofname,
)
g.add_axes()
if ofname is None:
return g.fig.axes
g.savefig()
class DotPlot(object):
def __init__(
self,
df: pd.DataFrame,
x: Optional[str] = None,
y: str = "Term",
hue: str = "Adjusted P-value",
dot_scale: float = 5.0,
x_order: Optional[List[str]] = None,
y_order: Optional[List[str]] = None,
thresh: float = 0.05,
n_terms: int = 10,
title: str = "",
figsize: Tuple[float, float] = (6, 5.5),
cmap: str = "viridis_r",
ofname: Optional[str] = None,
**kwargs,
):
"""Visualize GSEApy Results with categorical scatterplot
When multiple datasets exist in the input dataframe, the `x` argument is your friend.
:param df: GSEApy DataFrame results.
:param x: Categorical variable in `df` that map the x-axis data. Default: None.
:param y: Categorical variable in `df` that map the y-axis data. Default: Term.
:param hue: Grouping variable that will produce points with different colors.
Can be either categorical or numeric
:param x_order: bool, array-like list. Default: False.
If True, peformed hierarchical_clustering on X-axis.
or input a array-like list of `x` categorical levels.
:param x_order: bool, array-like list. Default: False.
If True, peformed hierarchical_clustering on Y-axis.
or input a array-like list of `y` categorical levels.
:param title: Figure title.
:param thresh: Terms with `column` value < cut-off are shown. Work only for
("Adjusted P-value", "P-value", "NOM p-val", "FDR q-val")
:param n_terms: Number of enriched terms to show.
:param dot_scale: float, scale the dot size to get proper visualization.
:param figsize: tuple, matplotlib figure size.
:param cmap: Matplotlib colormap for mapping the `column` semantic.
:param ofname: Output file name. If None, don't save figure
:param marker: The matplotlib.markers. See https://matplotlib.org/stable/api/markers_api.html
"""
self.marker = "o"
if "marker" in kwargs:
self.marker = kwargs["marker"]
self.y = y
self.x = x
self.x_order = x_order
self.y_order = y_order
self.hue = str(hue)
self.colname = str(hue)
self.figsize = figsize
self.cmap = cmap
self.ofname = ofname
self.scale = dot_scale
self.title = title
self.n_terms = n_terms
self.thresh = thresh
self.data = self.process(df)
plt.rcParams.update({"pdf.fonttype": 42, "ps.fonttype": 42})
def isfloat(self, x):
try:
float(x)
except:
return False
else:
return True
def process(self, df: pd.DataFrame):
# check if any values in `df[colname]` can't be coerced to floats
can_be_coerced = df[self.colname].map(self.isfloat).sum()
if can_be_coerced < len(df):
msg = "some value in %s could not be typecast to `float`" % self.colname
raise ValueError(msg)
# subset
mask = df[self.colname] <= self.thresh
if self.colname in ["Combined Score", "NES", "ES", "Odds Ratio"]:
mask.loc[:] = True
df = df.loc[mask]
if len(df) < 1:
msg = "Warning: No enrich terms when cutoff = %s" % self.thresh
raise ValueError(msg)
self.cbar_title = self.colname
# clip GSEA lower bounds
# if self.colname in ["NOM p-val", "FDR q-val"]:
# df[self.colname].clip(1e-5, 1.0, inplace=True)
# sorting the dataframe for better visualization
colnd = {
"Adjusted P-value": "FDR",
"P-value": "Pval",
"NOM p-val": "Pval",
"FDR q-val": "FDR",
}
if self.colname in ["Adjusted P-value", "P-value", "NOM p-val", "FDR q-val"]:
# get top_terms
df = df.sort_values(by=self.colname)
df[self.colname].replace(
0, method="bfill", inplace=True
) ## asending order, use bfill
df = df.assign(p_inv=np.log10(1 / df[self.colname].astype(float)))
_t = colnd[self.colname]
self.colname = "p_inv"
self.cbar_title = r"$\log_{10} \frac{1}{ " + _t + " }$"
# get top terms; sort ascending
if (self.x is not None) and (self.x in df.columns):
# get top term of each group
df = (
df.groupby(self.x)
.apply(lambda _x: _x.sort_values(by=self.colname).tail(self.n_terms))
.reset_index(drop=True)
)
else:
df = df.sort_values(by=self.colname).tail(self.n_terms) # acending
# get scatter area
if df.columns.isin(["Overlap", "Tag %"]).any():
ol = df.columns[df.columns.isin(["Overlap", "Tag %"])]
temp = (
df[ol].squeeze(axis=1).str.split("/", expand=True).astype(int)
) # axis=1, in case you have only 1 row
df = df.assign(Hits_ratio=temp.iloc[:, 0] / temp.iloc[:, 1])
else:
df = df.assign(Hits_ratio=1.0) # if Overlap column missing
return df
def _hierarchical_clustering(self, mat, method, metric) -> List[int]:
# mat.shape -> [n_sample, m_features]
Y0 = sch.linkage(mat, method=method, metric=metric)
Z0 = sch.dendrogram(
Y0,
orientation="left",
# labels=mat.index,
no_plot=True,
distance_sort="descending",
)
idx = Z0["leaves"][::-1] # reverse the order to make the view better
return idx
def get_x_order(
self, method: str = "single", metric: str = "euclidean"
) -> List[str]:
"""See scipy.cluster.hierarchy.linkage()
Perform hierarchical/agglomerative clustering.
Return categorical order.
"""
if hasattr(self.x_order, "__len__"):
return self.x_order
mat = self.data.pivot(
index=self.y,
columns=self.x,
values=self.colname, # [self.colname, "Hits_ratio"],
).fillna(0)
idx = self._hierarchical_clustering(mat.T, method, metric)
return list(mat.columns[idx])
def get_y_order(
self, method: str = "single", metric: str = "euclidean"
) -> List[str]:
"""See scipy.cluster.hierarchy.linkage()
Perform hierarchical/agglomerative clustering.
Return categorical order.
"""
if hasattr(self.y_order, "__len__"):
return self.y_order
mat = self.data.pivot(
index=self.y,
columns=self.x,
values=self.colname, # [self.colname, "Hits_ratio"],
).fillna(0)
idx = self._hierarchical_clustering(mat, method, metric)
return list(mat.index[idx])
def get_ax(self):
"""
setup figure axes
"""
# create fig
if hasattr(sys, "ps1") and (self.ofname is None):
# working inside python console, show figure
fig = plt.figure(figsize=self.figsize)
else:
# If working on commandline, don't show figure
fig = Figure(figsize=self.figsize)
_canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
self.fig = fig
return ax
def set_x(self):
"""
set x-axis's value
"""
x = self.x
xlabel = ""
# set xaxis values, so you could get dotplot
if (x is not None) and (x in self.data.columns):
xlabel = x
elif "Combined Score" in self.data.columns:
xlabel = "Combined Score"
x = xlabel
elif "Odds Ratio" in self.data.columns:
xlabel = "Odds Ratio"
x = xlabel
elif "NES" in self.data.columns:
xlabel = "NES"
x = xlabel
else:
# revert back to p_inv
x = self.colname
xlabel = self.cbar_title
return x, xlabel
def scatter(
self,
outer_ring: bool = False,
):
"""
build scatter
"""
# scatter colormap range
# df = df.assign(colmap=self.data[self.colname].round().astype("int"))
# make area bigger to better visualization
# area = df["Hits_ratio"] * plt.rcParams["lines.linewidth"] * 100
df = self.data.assign(
area=(
self.data["Hits_ratio"] * self.scale * plt.rcParams["lines.markersize"]
).pow(2)
)
colmap = df[self.colname].astype(int)
vmin = np.percentile(colmap.min(), 2)
vmax = np.percentile(colmap.max(), 98)
# vmin = np.percentile(df.colmap.min(), 2)
# vmax = np.percentile(df.colmap.max(), 98)
ax = self.get_ax()
# if self.x is None:
x, xlabel = self.set_x()
y = self.y
# set x, y order
xunits = UnitData(self.get_x_order()) if self.x_order else None
yunits = UnitData(self.get_y_order()) if self.y_order else None
# outer ring
if outer_ring:
smax = df["area"].max()
# TODO:
# Matplotlib BUG: when setting edge colors,
# there's the center of scatter could not aligned.
# Must set backend to TKcario... to fix it
# Instead, I just add more dots in the plot to get the ring
blk_sc = ax.scatter(
x=x,
y=y,
s=smax * 1.6,
edgecolors="none",
c="black",
data=df,
marker=self.marker,
xunits=xunits, # set x categorical order
yunits=yunits, # set y categorical order
zorder=0,
)
wht_sc = ax.scatter(
x=x,
y=y,
s=smax * 1.3,
edgecolors="none",
c="white",
data=df,
marker=self.marker,
xunits=xunits, # set x categorical order
yunits=yunits, # set y categorical order
zorder=1,
)
# data = np.array(rg.get_offsets()) # get data coordinates
# inner circle
sc = ax.scatter(
x=x,
y=y,
data=df,
s="area",
edgecolors="none",
c=self.colname,
cmap=self.cmap,
vmin=vmin,
vmax=vmax,
marker=self.marker,
xunits=xunits, # set x categorical order
yunits=yunits, # set y categorical order
zorder=2,
)
ax.set_xlabel(xlabel, fontsize=14, fontweight="bold")
ax.xaxis.set_tick_params(labelsize=14)
ax.yaxis.set_tick_params(labelsize=16)
ax.set_axisbelow(True) # set grid blew other element
ax.grid(axis="y", zorder=-1) # zorder=-1.0
ax.margins(x=0.25)
# We change the fontsize of minor ticks label
# ax.tick_params(axis='y', which='major', labelsize=16)
# ax.tick_params(axis='both', which='minor', labelsize=14)
# scatter size legend
# we use the *func* argument to supply the inverse of the function
# used to calculate the sizes from above. The *fmt* ensures to string you want
handles, labels = sc.legend_elements(
prop="sizes",
num=3, #
fmt="{x:.2f}",
color="gray",
func=lambda s: np.sqrt(s) / plt.rcParams["lines.markersize"] / self.scale,
)
ax.legend(
handles,
labels,
title="% Genes\nin set",
bbox_to_anchor=(1.02, 0.9),
loc="upper left",
frameon=False,
labelspacing=2,
)
ax.set_title(self.title, fontsize=20, fontweight="bold")
self.add_colorbar(sc)
return ax
def add_colorbar(self, sc):
"""
:param sc: matplotlib.Scatter
"""
# colorbar
# cax = fig.add_axes([1.0, 0.20, 0.03, 0.22])
cbar = self.fig.colorbar(
sc,
shrink=0.25,
aspect=10,
anchor=(0.0, 0.2), # (0.0, 0.2),
location="right"
# cax=cax,
)
# cbar.ax.tick_params(direction='in')
cbar.ax.yaxis.set_tick_params(
color="white", direction="in", left=True, right=True
)
cbar.ax.set_title(self.cbar_title, loc="left", fontweight="bold")
for key, spine in cbar.ax.spines.items():
spine.set_visible(False)
def _parse_colors(self, color=None):
"""
parse colors for groups
"""
# map color to group
if isinstance(color, dict):
return list(color.values())
# get default color cycle
if (not isinstance(color, str)) and hasattr(color, "__len__"):
_colors = list(color)
else:
# get current matplotlib color cycle
prop_cycle = plt.rcParams["axes.prop_cycle"]
_colors = prop_cycle.by_key()["color"]
return _colors
def barh(self, color=None, group=None, ax=None):
"""
Barplot
"""
if ax is None:
ax = self.get_ax()
x, xlabel = self.set_x()
bar = self.data.plot.barh(
x=self.y, y=self.colname, alpha=0.75, fontsize=16, ax=ax
)
if self.hue in ["Adjusted P-value", "P-value", "FDR q-val", "NOM p-val"]:
xlabel = r"$- \log_{10}$ (%s)" % self.hue
else:
xlabel = self.hue
bar.set_xlabel(xlabel, fontsize=16, fontweight="bold")
bar.set_ylabel("")
bar.set_title(self.title, fontsize=24, fontweight="bold")
bar.xaxis.set_major_locator(MaxNLocator(nbins=5, integer=True))
#
_colors = self._parse_colors(color=color)
colors = _colors
# remove old legend first
bar.legend_.remove()
if (group is not None) and (group in self.data.columns):
num_grp = self.data[group].value_counts(sort=False)
# set colors for each bar (groupby hue) using full length
colors = []
legend_elements = []
for i, n in enumerate(num_grp):
# cycle _colors if num_grp > len(_colors)
c = _colors[i % len(_colors)]
# group_label
label = num_grp.index[i]
# if input color is a dict with keys in group
if isinstance(color, dict) and label in color:
c = color[label]
# expand the length to match bars
colors += [c] * n
ele = Line2D(
xdata=[0],
ydata=[0],
marker="o",
color="w",
label=label,
markerfacecolor=c,
markersize=8,
)
legend_elements.append(ele)
# add custom legend
ax.legend(
handles=legend_elements,
loc="upper left",
title=group,
bbox_to_anchor=(1.02, 0.5),
frameon=False,
)
# update color of bars
for j, b in enumerate(ax.patches):
c = colors[j % len(colors)]
b.set_facecolor(c)
# self.adjust_spines(ax, spines=["left", "bottom"])
for side in ["right", "top"]:
ax.spines[side].set_visible(False)
# set ticks
ax.tick_params(axis="both", which="both", top=False, right=False)
return ax
def to_edgelist(self) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""
return two dataframe of nodes, and edges
"""
num_nodes = len(self.data)
# build graph
# G = nx.Graph()
group_loc = None
if self.x is not None:
group_loc = self.data.columns.get_loc(self.x)
term_loc = self.data.columns.get_loc(self.y) # "Terms"
if "Genes" in self.data.columns:
gene_loc = self.data.columns.get_loc("Genes")
elif "Lead_genes":
gene_loc = self.data.columns.get_loc("Lead_genes")
else:
raise KeyError("Sorry, could not locate enriched gene list")
# build graph
# nodes = []
nodes = self.data.reset_index(drop=True)
nodes.index.name = "node_idx"
genes = self.data.iloc[:, gene_loc].str.split(";")
# ns_loc = self.data.columns.get_loc("Hits_ratio")
edge_list = []
for i in range(num_nodes):
# nodes.append([i, self.data.iloc[i, term_loc], self.data.iloc[i, ns_loc]])
# if group_loc is not None:
# nodes[-1].append(self.data.iloc[i, group_loc])
for j in range(i + 1, num_nodes):
set_i = set(genes.iloc[i])
set_j = set(genes.iloc[j])
ov = set_i.intersection(set_j)
if len(ov) < 1:
continue
jaccard_coefficient = len(ov) / len(set_i.union(set_j))
overlap_coefficient = len(ov) / min(len(set_i), len(set_j))
edge = [
i,
j,
self.data.iloc[i, term_loc],
self.data.iloc[j, term_loc],
jaccard_coefficient,
overlap_coefficient,
",".join(ov),
]
edge_list.append(edge)
# G.add_edge(src,
# targ,
# jaccard= jaccard_coefficient,
# overlap = overlap_coefficient,
# genes = list(ov))
edges = pd.DataFrame(
edge_list,
columns=[
"src_idx",
"targ_idx",
"src_name",
"targ_name",
"jaccard_coef",
"overlap_coef",
"overlap_genes",
],
)
# node_c = ["node_idx", "node_name", "node_size"]
# if group_loc is not None:
# node_c += ["node_group"]
# nodes = pd.DataFrame(nodes, columns=node_c)
return nodes, edges
[docs]
def dotplot(
df: pd.DataFrame,
column: str = "Adjusted P-value",
x: Optional[str] = None,
y: str = "Term",
x_order: Union[List[str], bool] = False,
y_order: Union[List[str], bool] = False,
title: str = "",
cutoff: float = 0.05,
top_term: int = 10,
size: float = 5,
figsize: Tuple[float, float] = (4, 6),
cmap: str = "viridis_r",
ofname: Optional[str] = None,
xticklabels_rot: Optional[float] = None,
yticklabels_rot: Optional[float] = None,
marker: str = "o",
show_ring: bool = False,
**kwargs,
):
"""Visualize GSEApy Results with categorical scatterplot
When multiple datasets exist in the input dataframe, the `x` argument is your friend.
:param df: GSEApy DataFrame results.
:param column: column name in `df` that map the dot colors. Default: Adjusted P-value.
:param x: Categorical variable in `df` that map the x-axis data. Default: None.
:param y: Categorical variable in `df` that map the y-axis data. Default: Term.
:param x_order: bool, array-like list. Default: False.
If True, peformed hierarchical_clustering on X-axis.
or input a array-like list of `x` categorical levels.
:param x_order: bool, array-like list. Default: False.
If True, peformed hierarchical_clustering on Y-axis.
or input a array-like list of `y` categorical levels.
:param title: Figure title.
:param cutoff: Terms with `column` value < cut-off are shown. Work only for
("Adjusted P-value", "P-value", "NOM p-val", "FDR q-val")
:param top_term: Number of enriched terms to show.
:param size: float, scale the dot size to get proper visualization.
:param figsize: tuple, matplotlib figure size.
:param cmap: Matplotlib colormap for mapping the `column` semantic.
:param ofname: Output file name. If None, don't save figure
:param marker: The matplotlib.markers. See https://matplotlib.org/stable/api/markers_api.html
:param show_ring bool: Whether to draw outer ring.
:return: matplotlib.Axes. return None if given ofname.
Only terms with `column` <= `cut-off` are plotted.
"""
if "group" in kwargs:
warnings.warn("group is deprecated; use x instead", DeprecationWarning, 2)
return
dot = DotPlot(
df=df,
x=x,
y=y,
x_order=x_order,
y_order=y_order,
hue=column,
title=title,
thresh=cutoff,
n_terms=int(top_term),
dot_scale=size,
figsize=figsize,
cmap=cmap,
ofname=ofname,
marker=marker,
)
ax = dot.scatter(outer_ring=show_ring)
if xticklabels_rot:
for label in ax.get_xticklabels():
label.set_ha("right")
label.set_rotation(xticklabels_rot)
if yticklabels_rot:
for label in ax.get_yticklabels():
label.set_ha("right")
label.set_rotation(yticklabels_rot)
if ofname is None:
return ax
dot.fig.savefig(ofname, bbox_inches="tight", dpi=300)
[docs]
def ringplot(
df: pd.DataFrame,
column: str = "Adjusted P-value",
x: Optional[str] = None,
title: str = "",
cutoff: float = 0.05,
top_term: int = 10,
size: float = 5,
figsize: Tuple[float, float] = (4, 6),
cmap: str = "viridis_r",
ofname: Optional[str] = None,
xticklabels_rot: Optional[float] = None,
yticklabels_rot: Optional[float] = None,
marker="o",
show_ring: bool = True,
**kwargs,
):
"""ringplot is deprecated, use dotplot instead
:param df: GSEApy DataFrame results.
:param x: Group by the variable in `df` that will produce categorical scatterplot.
:param column: column name in `df` to map the dot colors. Default: Adjusted P-value
:param title: figure title
:param cutoff: terms with `column` value < cut-off are shown. Work only for
("Adjusted P-value", "P-value", "NOM p-val", "FDR q-val")
:param top_term: number of enriched terms to show.
:param size: float, scale the dot size to get proper visualization.
:param figsize: tuple, matplotlib figure size.
:param cmap: matplotlib colormap for mapping the `column` semantic.
:param ofname: output file name. If None, don't save figure
:param marker: the matplotlib.markers. See https://matplotlib.org/stable/api/markers_api.html
:param show_ring bool: whether to show outer ring.
:return: matplotlib.Axes. return None if given ofname.
Only terms with `column` <= `cut-off` are plotted.
"""
warnings.warn("ringplot is deprecated; use dotplot instead", DeprecationWarning, 2)
return
[docs]
def barplot(
df: pd.DataFrame,
column: str = "Adjusted P-value",
group: Optional[str] = None,
title: str = "",
cutoff: float = 0.05,
top_term: int = 10,
figsize: Tuple[float, float] = (4, 6),
color: Union[str, List[str], Dict[str, str]] = "salmon",
ofname: Optional[str] = None,
**kwargs,
):
"""Visualize GSEApy Results.
When multiple datasets exist in the input dataframe, the `group` argument is your friend.
:param df: GSEApy DataFrame results.
:param column: column name in `df` to map the x-axis data. Default: Adjusted P-value
:param group: group by the variable in `df` that will produce bars with different colors.
:param title: figure title.
:param cutoff: terms with `column` value < cut-off are shown. Work only for
("Adjusted P-value", "P-value", "NOM p-val", "FDR q-val")
:param top_term: number of top enriched terms grouped by `hue` are shown.
:param figsize: tuple, matplotlib figsize.
:param color: color or list or dict of matplotlib.colors. Must be reconigzed by matplotlib.
if dict input, dict keys must be found in the `group`
:param ofname: output file name. If None, don't save figure
:return: matplotlib.Axes. return None if given ofname.
Only terms with `column` <= `cut-off` are plotted.
"""
dot = DotPlot(
df=df,
x=group if group else None, # x turns into hue in bar
y="Term",
hue=column, # hue turns into x in bar
title=title,
thresh=cutoff,
n_terms=int(top_term),
figsize=figsize,
cmap="viridis", # placeholder only
ofname=ofname,
)
if isinstance(color, str):
color = [color]
ax = dot.barh(color=color, group=group)
if ofname is None:
return ax
dot.fig.savefig(ofname, bbox_inches="tight", dpi=300)
class TracePlot(object):
def __init__(
self,
terms: List[str],
tags: List[Sequence[int]],
runes: List[Sequence[float]],
rank_metric: Optional[Sequence[float]] = None,
figsize: Tuple[float, float] = (6, 5.5),
colors: Union[str, List[str], None] = None,
legend_kws: Optional[Dict[str, Any]] = None,
ofname: Optional[str] = None,
ax: Optional[plt.Axes] = None,
**kwargs,
):
"""
terms: list of terms/pathways to show
tags: list of hit indices for each term
runes: list runing enrichment score for each term
hits: list of ranks of the overlap genes in pathway/term.
rank_metric: ranking metric in descending order.
colors: list of colors for each term/pathway
legend_kws: kwargs to pass to ax.legend. e.g. `loc`, `bbox_to_achor`.
ofname: savefig
ax: matplotlib's Axes.
"""
# dataFrame of ranked matrix scores
self.rankings = rank_metric
self.terms = terms
self.runes = runes
self.hits = tags
self.figsize = figsize
if isinstance(colors, str):
colors = [colors]
self.colors = colors
self.ofname = ofname
self.legend_kws = legend_kws
# self._pos_label = pheno_pos
# self._neg_label = pheno_neg
# output truetype
plt.rcParams.update({"pdf.fonttype": 42, "ps.fonttype": 42})
# in most case, we will have many plots, so do not display plots
# It's also usefull to run this script on command line.
if ax is None:
# GSEA Plots
if hasattr(sys, "ps1") and (self.ofname is None):
# working inside python console, show figure
self.fig = plt.figure(figsize=self.figsize, facecolor="white")
else:
# If working on command line, don't show figure
self.fig = Figure(figsize=self.figsize, facecolor="white")
self._canvas = FigureCanvas(self.fig)
else:
self.fig = ax.figure
# self.fig.suptitle(self.term, fontsize=16, wrap=True, fontweight="bold")
def axes_hits(
self, tags: Sequence[int], rect: List[float], color="#0033FF", bottom=False
):
"""
hits: 1d array of this
rect : sequence of float
The dimensions [left, bottom, width, height] of the new axes. All
quantities are in fractions of figure width and height.
"""
# gene hits
ax2 = self.fig.add_axes(rect)
# the x coords of this transformation are data, and the y coord are axes
trans2 = transforms.blended_transform_factory(ax2.transData, ax2.transAxes)
# align hits to runes
ax2.vlines(
[0, len(self.runes) - 1],
0,
1,
linewidth=0.5,
transform=trans2,
color="white",
alpha=0,
) # alpha 0 to transparency
ax2.vlines(tags, 0, 1, linewidth=0.5, transform=trans2, color=color)
ax2.spines["bottom"].set_visible(True)
ax2.tick_params(
axis="both",
which="both",
bottom=bottom,
top=False,
right=False,
left=False,
labelbottom=bottom,
labelleft=False,
labelsize=14,
)
if bottom:
ax2.set_xlabel("Gene Rank", fontsize=16, fontweight="bold")
def axes_stat(self, rect: List[float]):
"""
rect : sequence of float
The dimensions [left, bottom, width, height] of the new axes. All
quantities are in fractions of figure width and height.
"""
# Enrichment score plot
ax4 = self.fig.add_axes(rect)
if self.rankings is not None:
ax44 = ax4.twinx() # instantiate a second axes that shares the same x-axis
ax44.fill_between(
range(len(self.rankings)),
y1=self.rankings,
y2=0,
color="#C9D3DB",
zorder=1,
alpha=0.5,
)
ax44.tick_params(axis="y", labelcolor="#808080")
ax44.set_ylabel(
"Ranked metric", fontsize=16, fontweight="bold", color="#808080"
)
if self.colors is None:
cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
else:
cycle = self.colors
for i, r, term in zip(range(len(self.terms)), self.runes, self.terms):
ax4.plot(
range(len(r)),
r,
linewidth=2,
label=term,
color=cycle[i % len(cycle)],
zorder=2 + i,
) # color=color)
# the y coords of this transformation are data, and the x coord are axes
trans4 = transforms.blended_transform_factory(ax4.transAxes, ax4.transData)
ax4.hlines(0, 0, 1, linewidth=1, transform=trans4, color="grey")
ax4.set_ylabel("Enrichment Score", fontsize=16, fontweight="bold")
# ax4.set_xlim(min(self._x), max(self._x))
ax4.tick_params(
axis="both",
which="both",
bottom=False,
top=False,
right=False,
labelbottom=False,
labelsize=14,
)
ax4.locator_params(axis="y", nbins=5)
# FuncFormatter need two argument, I don't know why. this lambda function used to format yaxis tick labels.
ax4.yaxis.set_major_formatter(
plt.FuncFormatter(lambda tick_loc, tick_num: "{:.1f}".format(tick_loc))
)
if isinstance(self.legend_kws, dict):
ax4.legend(**self.legend_kws)
else:
ax4.legend(loc=(0, 1.02))
if self.rankings is not None:
self.align_yaxis(ax4, ax44)
def add_axes(self):
"""
Please check matplotlib docs about how to `add_axes` to figure.
"""
left = 0.1
width = 0.8
bottom = 0.1
# height = 0
stat_height_ratio = 4
hits_height_ratio = 0.5
## make stat /hits height ratio const
# 0.8 = base*(4 + len(terms)*0.05)
base = 0.8 / (stat_height_ratio + len(self.terms) * hits_height_ratio)
# add each hit track
if self.colors is None:
cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
else:
cycle = self.colors
for i, hit in enumerate(self.hits):
height = hits_height_ratio * base
self.axes_hits(
tags=hit,
rect=[left, bottom, width, height],
color=cycle[i % len(cycle)],
bottom=False if bottom > 0.1 else True,
)
bottom += height
height = stat_height_ratio * base
# add mountain curve
self.axes_stat([left, bottom, width, height])
# self.fig.subplots_adjust(hspace=0)
# self.fig.tight_layout()
def savefig(self, ofname: str, bbox_inches: str = "tight", dpi: float = 300):
# if self.ofname is not None:
if hasattr(sys, "ps1") and (ofname is not None):
self.fig.savefig(self.ofname, bbox_inches=bbox_inches, dpi=dpi)
elif self.ofname is None:
return
else:
self._canvas.print_figure(ofname, bbox_inches=bbox_inches, dpi=300)
return
def align_yaxis(self, ax1, ax2):
"""Align zeros of the two axes, zooming them out by same ratio"""
axes = np.array([ax1, ax2])
extrema = np.array([ax.get_ylim() for ax in axes])
tops = extrema[:, 1] / (extrema[:, 1] - extrema[:, 0])
# Ensure that plots (intervals) are ordered bottom to top:
if tops[0] > tops[1]:
axes, extrema, tops = [a[::-1] for a in (axes, extrema, tops)]
# How much would the plot overflow if we kept current zoom levels?
tot_span = tops[1] + 1 - tops[0]
extrema[0, 1] = extrema[0, 0] + tot_span * (extrema[0, 1] - extrema[0, 0])
extrema[1, 0] = extrema[1, 1] + tot_span * (extrema[1, 0] - extrema[1, 1])
[axes[i].set_ylim(*extrema[i]) for i in range(2)]
[docs]
def gseaplot2(
terms: List[str],
hits: List[Sequence[int]],
RESs: List[Sequence[float]],
rank_metric: Optional[Sequence[float]] = None,
colors: Optional[Union[str, List[str]]] = None,
figsize: Tuple[float, float] = (6, 4),
legend_kws: Optional[Dict[str, Any]] = None,
ofname: Optional[str] = None,
**kwargs,
) -> Optional[List[plt.Axes]]:
"""Trace plot for combining multiple terms/pathways into one plot
:param terms: list of terms to show in trace plot
:param hits: list of hits indices correspond to each term.
:param RESs: list of running enrichment scores correspond to each term.
:param rank_metric: Optional, rankings.
:param figsize: matplotlib figsize.
:legend_kws: Optional, contol the location of lengends
:param ofname: output file name. If None, don't save figure
return matplotlib.Figure.
"""
# in case you just input one pathway
if isinstance(terms, str):
terms = [terms]
# make the inputs are legal
assert (
hasattr(terms, "__len__")
and hasattr(hits, "__len__")
and hasattr(RESs, "__len__")
)
assert len(terms) == len(hits) == len(RESs)
trace = TracePlot(
terms=list(terms),
runes=list(RESs),
tags=list(hits),
rank_metric=rank_metric,
colors=colors,
figsize=figsize,
ofname=ofname,
legend_kws=legend_kws,
**kwargs,
)
trace.add_axes()
if ofname is None:
return trace.fig.axes
trace.savefig(ofname)
[docs]
def enrichment_map(
df: pd.DataFrame,
column: str = "Adjusted P-value",
cutoff: float = 0.05,
top_term: int = 10,
**kwargs,
) -> Tuple[pd.DataFrame, pd.DataFrame]:
"""Visualize GSEApy Results.
Node size corresponds to the percentage of gene overlap in a certain term of interest.
Colour of the node corresponds to the significance of the enriched terms.
Edge size corresponds to the number of genes that overlap between the two connected nodes.
Gray edges correspond to both nodes when it is the only colour edge.
When there are two different edge colours, red corresponds to positve nodes and blue corresponds to negative nodes.
:param df: GSEApy DataFrame results.
:param column: column name in `df` to map the node colors. Default: Adjusted P-value or FDR q-val.
choose from ("Adjusted P-value", "P-value", "FDR q-val", "NOM p-val").
:param group: group by the variable in `df` that will produce bars with different colors.
:param title: figure title.
:param cutoff: nodes with `column` value < cut-off are shown. Work only for
("Adjusted P-value", "P-value", "NOM p-val", "FDR q-val")
:param top_term: number of top enriched terms are selected as nodes.
:return: tuple of dataframe (nodes, edges)
"""
# c = column
if column not in df.columns:
for c in ["Adjusted P-value", "P-value", "FDR q-val", "NOM p-val"]:
if c in df:
column = c
break
dot = DotPlot(
df=df,
x=None, # x turns into hue in colors of nodes
y="Term", # node
hue=column, # node size
thresh=cutoff,
n_terms=int(top_term),
)
return dot.to_edgelist()