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plotProfile.py
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plotProfile.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import argparse
import numpy as np
import matplotlib
matplotlib.use('Agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['svg.fonttype'] = 'none'
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from matplotlib import colors as pltcolors
import matplotlib.gridspec as gridspec
import plotly.offline as py
import plotly.graph_objs as go
# own modules
from deeptools import parserCommon
from deeptools import heatmapper
from deeptools.heatmapper_utilities import plot_single, plotly_single, getProfileTicks
from deeptools.computeMatrixOperations import filterHeatmapValues
debug = 0
old_settings = np.seterr(all='ignore')
plt.ioff()
def parse_arguments(args=None):
parser = argparse.ArgumentParser(
parents=[parserCommon.heatmapperMatrixArgs(),
parserCommon.heatmapperOutputArgs(mode='profile'),
parserCommon.heatmapperOptionalArgs(mode='profile')],
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description='This tool creates a profile plot for '
'scores over sets of genomic regions. '
'Typically, these regions are genes, but '
'any other regions defined in BED '
' will work. A matrix generated '
'by computeMatrix is required.',
epilog='An example usage is: plotProfile -m <matrix file>',
add_help=False)
return parser
def process_args(args=None):
args = parse_arguments().parse_args(args)
# Ensure that yMin/yMax are there and a list
try:
assert(args.yMin is not None)
except:
args.yMin = [None]
try:
assert(args.yMax is not None)
except:
args.yMax = [None]
# Sometimes Galaxy sends --yMax '' and --yMin ''
if args.yMin == ['']:
args.yMin = [None]
if args.yMax == ['']:
args.yMax = [None]
# Convert to floats
if args.yMin != [None]:
foo = [float(x) for x in args.yMin]
args.yMin = foo
if args.yMax != [None]:
foo = [float(x) for x in args.yMax]
args.yMax = foo
if args.plotHeight < 0.5:
args.plotHeight = 0.5
elif args.plotHeight > 100:
args.plotHeight = 100
return args
class Profile(object):
def __init__(self, hm, out_file_name,
plot_title='', y_axis_label='',
y_min=None, y_max=None,
averagetype='median',
reference_point_label=None,
start_label='TSS', end_label='TES',
plot_height=7,
plot_width=11,
per_group=False,
plot_type='simple',
image_format=None,
color_list=None,
legend_location='auto',
plots_per_row=8,
label_rotation=0,
dpi=200):
"""
Using the hm matrix, makes a line plot
either per group or per sample
using the specified parameters.
Args:
hm: heatmapper object
out_file_name: string
plot_title: string
y_axis_label: list
y_min: list
y_max: list
averagetype: mean, sum, median
reference_point_label: string
start_label: string
end_label: string
plot_height: in cm
plot_width: in cm
per_group: bool
plot_type: string
image_format: string
color_list: list
legend_location:
plots_per_row: int
label_rotation: float
Returns:
"""
self.hm = hm
self.out_file_name = out_file_name
self.plot_title = plot_title
self.y_axis_label = y_axis_label
self.y_min = y_min
self.y_max = y_max
self.averagetype = averagetype
self.reference_point_label = reference_point_label
self.start_label = start_label
self.end_label = end_label
self.plot_height = plot_height
self.plot_width = plot_width
self.per_group = per_group
self.plot_type = plot_type
self.image_format = image_format
self.color_list = color_list
self.legend_location = legend_location
self.plots_per_row = plots_per_row
self.label_rotation = label_rotation
self.dpi = dpi
# Honor reference point labels from computeMatrix
if reference_point_label is None:
self.reference_point_label = hm.parameters['ref point']
# decide how many plots are needed
if self.per_group:
self.numplots = self.hm.matrix.get_num_groups()
self.numlines = self.hm.matrix.get_num_samples()
else:
self.numplots = self.hm.matrix.get_num_samples()
self.numlines = self.hm.matrix.get_num_groups()
if self.numplots > self.plots_per_row:
rows = np.ceil(self.numplots / float(self.plots_per_row)).astype(int)
cols = self.plots_per_row
else:
rows = 1
cols = self.numplots
self.grids = gridspec.GridSpec(rows, cols)
plt.rcParams['font.size'] = 10.0
self.font_p = FontProperties()
self.font_p.set_size('small')
# convert cm values to inches
plot_height_inches = rows * self.cm2inch(self.plot_height)[0]
self.fig = plt.figure(figsize=self.cm2inch(cols * self.plot_width, rows * self.plot_height))
self.fig.suptitle(self.plot_title, y=(1 - (0.06 / plot_height_inches)))
# Ensure that the labels are vectors
nSamples = len(self.hm.matrix.sample_labels)
if not isinstance(self.reference_point_label, list):
self.reference_point_label = [self.reference_point_label] * nSamples
if not isinstance(self.start_label, list):
self.start_label = [self.start_label] * nSamples
if not isinstance(self.end_label, list):
self.end_label = [self.end_label] * nSamples
def getTicks(self, idx):
"""
This is essentially a wrapper around getProfileTicks to accomdate the fact that each column has its own ticks.
"""
xticks, xtickslabel = getProfileTicks(self.hm, self.reference_point_label[idx], self.start_label[idx], self.end_label[idx], idx)
return xticks, xtickslabel
@staticmethod
def cm2inch(*tupl):
inch = 2.54
if isinstance(tupl[0], tuple):
return tuple(i / inch for i in tupl[0])
else:
return tuple(i / inch for i in tupl)
def plot_hexbin(self):
from matplotlib import cm
cmap = cm.coolwarm
cmap.set_bad('black')
if self.image_format == "plotly":
return self.plotly_hexbin()
for plot in range(self.numplots):
col = plot % self.plots_per_row
row = int(plot / float(self.plots_per_row))
localYMin = None
localYMax = None
# split the ax to make room for the colorbar and for each of the
# groups
sub_grid = gridspec.GridSpecFromSubplotSpec(self.numlines, 2, subplot_spec=self.grids[row, col],
width_ratios=[0.92, 0.08], wspace=0.05, hspace=0.1)
ax = self.fig.add_subplot(sub_grid[0, 0])
ax.tick_params(
axis='y',
which='both',
left='off',
right='off',
labelleft='on')
if self.per_group:
title = self.hm.matrix.group_labels[plot]
else:
title = self.hm.matrix.sample_labels[plot]
vmin = np.inf
vmax = -np.inf
for data_idx in range(self.numlines):
# get the max and min
if self.per_group:
_row, _col = plot, data_idx
else:
_row, _col = data_idx, plot
sub_matrix = self.hm.matrix.get_matrix(_row, _col)
ma = sub_matrix['matrix']
x_values = np.tile(np.arange(ma.shape[1]), (ma.shape[0], 1))
img = ax.hexbin(x_values.flatten(), ma.flatten(), cmap=cmap, mincnt=1)
_vmin, _vmax = img.get_clim()
if _vmin < vmin:
vmin = _vmin
if _vmax > vmax:
vmax = _vmax
if localYMin is None or self.y_min[col % len(self.y_min)] < localYMin:
localYMin = self.y_min[col % len(self.y_min)]
if localYMax is None or self.y_max[col % len(self.y_max)] > localYMax:
localYMax = self.y_max[col % len(self.y_max)]
self.fig.delaxes(ax)
# iterate again after having computed the vmin and vmax
ax_list = []
for data_idx in range(self.numlines)[::-1]:
ax = self.fig.add_subplot(sub_grid[data_idx, 0])
if data_idx == 0:
ax.set_title(title)
if data_idx != self.numlines - 1:
plt.setp(ax.get_xticklabels(), visible=False)
if self.per_group:
_row, _col = plot, data_idx
else:
_row, _col = data_idx, plot
sub_matrix = self.hm.matrix.get_matrix(_row, _col)
if self.per_group:
label = sub_matrix['sample']
else:
label = sub_matrix['group']
ma = sub_matrix['matrix']
try:
# matplotlib 2.0
ax.set_facecolor('black')
except:
# matplotlib <2.0
ax.set_axis_bgcolor('black')
x_values = np.tile(np.arange(ma.shape[1]), (ma.shape[0], 1))
img = ax.hexbin(x_values.flatten(), ma.flatten(), cmap=cmap, mincnt=1, vmin=vmin, vmax=vmax)
if plot == 0:
ax.axes.set_ylabel(label)
ax_list.append(ax)
lims = ax.get_ylim()
if localYMin is not None:
lims = (localYMin, lims[1])
if localYMax is not None:
lims = (lims[0], localYMax)
if lims[0] >= lims[1]:
lims = (lims[0], lims[0] + 1)
ax.set_ylim(lims)
xticks, xtickslabel = self.getTicks(plot)
if np.ceil(max(xticks)) != float(ma.shape[1]):
tickscale = float(sub_matrix['matrix'].shape[1]) / max(self.xticks)
xticks_use = [x * tickscale for x in xticks]
ax_list[0].axes.set_xticks(xticks_use)
else:
ax_list[0].axes.set_xticks(xticks)
ax_list[0].axes.set_xticklabels(xtickslabel, rotation=self.label_rotation)
# align the first and last label
# such that they don't fall off
# the heatmap sides
ticks = ax_list[-1].xaxis.get_major_ticks()
ticks[0].label1.set_horizontalalignment('left')
ticks[-1].label1.set_horizontalalignment('right')
cax = self.fig.add_subplot(sub_grid[:, 1])
self.fig.colorbar(img, cax=cax)
plt.subplots_adjust(wspace=0.05, hspace=0.3)
plt.tight_layout()
plt.savefig(self.out_file_name, dpi=self.dpi, format=self.image_format)
plt.close()
def plotly_hexbin(self):
"""plot_hexbin, but for plotly. it's annoying that we have to have sub-subplots"""
fig = go.Figure()
cols = self.plots_per_row if self.numplots > self.plots_per_row else self.numplots
rows = np.ceil(self.numplots / float(cols)).astype(int)
fig['layout'].update(title=self.plot_title)
domainWidth = .9 / cols
domainHeight = .9 / rows
bufferHeight = 0.0
if rows > 1:
bufferHeight = 0.1 / (rows - 1)
else:
domainHeight = 1.0
bufferWidth = 0.0
if cols > 1:
bufferWidth = 0.1 / (cols - 1)
else:
domainWidth = 1.0
subHeight = domainHeight / float(self.numlines)
if self.per_group:
sideLabels = self.hm.matrix.sample_labels
else:
sideLabels = self.hm.matrix.group_labels
data = []
annos = []
vmin = np.inf
vmax = -np.inf
for i in range(self.numplots):
row = rows - i / self.plots_per_row - 1
col = i % self.plots_per_row
if self.per_group:
title = self.hm.matrix.group_labels[i]
else:
title = self.hm.matrix.sample_labels[i]
base = row * (domainHeight + bufferHeight)
domain = [base, base + domainHeight]
titleY = base + domainHeight
base = col * (domainWidth + bufferWidth)
domain = [base, base + domainWidth]
titleX = base + 0.5 * domainWidth
xanchor = 'x{}'.format(i + 1)
fig['layout']['xaxis{}'.format(i + 1)] = dict(domain=domain)
annos.append({'yanchor': 'bottom', 'xref': 'paper', 'xanchor': 'center', 'yref': 'paper', 'text': title, 'y': titleY, 'x': titleX, 'font': {'size': 16}, 'showarrow': False})
# set yMin/yMax
yMin = np.inf
yMax = -np.inf
for j in range(self.numlines):
# get the max and min
if self.per_group:
_row, _col = i, j
else:
_row, _col = j, i
ma = self.hm.matrix.get_matrix(_row, _col)['matrix']
if np.min(ma) < yMin:
yMin = np.min(ma)
if np.max(ma) > yMax:
yMax = np.max(ma)
if self.y_min[i % len(self.y_min)] is not None:
yMin = self.y_min[i % len(self.y_min)]
if self.y_max[i % len(self.y_max)] is not None:
yMax = self.y_max[i % len(self.y_max)]
for j in range(self.numlines):
if self.per_group:
_row, _col = i, j
else:
_row, _col = j, i
foo = i * self.numlines + j + 1
yanchor = 'y{}'.format(foo)
base = row * (domainHeight + bufferHeight) + j * subHeight
domain = [base, base + subHeight]
fig['layout']['yaxis{}'.format(foo)] = {'domain': domain, 'title': self.y_axis_label, 'anchor': xanchor, 'range': [yMin, yMax]}
if j == 0:
_ = "xaxis{}".format(xanchor[1:])
fig['layout'][_].update(anchor='y{}'.format(foo))
if col == 0:
titleY = base + 0.5 * subHeight
annos.append({'yanchor': 'middle', 'xref': 'paper', 'xanchor': 'left', 'yref': 'paper', 'text': sideLabels[j], 'y': titleY, 'x': -0.03, 'font': {'size': 16}, 'showarrow': False, 'textangle': -90})
sub_matrix = self.hm.matrix.get_matrix(_row, _col)
ma = self.hm.matrix.get_matrix(_row, _col)['matrix']
fig['layout']['xaxis{}'.format(i + 1)].update(range=[0, ma.shape[1]])
if self.per_group:
label = sub_matrix['sample']
else:
label = sub_matrix['group']
# Manually compute the 2D histogram with 100x100 bins
x_values = np.tile(np.arange(ma.shape[1]), (ma.shape[0], 1))
z, xe, ye = np.histogram2d(x_values.flatten(), ma.flatten(), bins=100, range=[[0, ma.shape[1]], [yMin, yMax]])
_vmin = np.min(z)
_vmax = np.max(z)
if _vmin < vmin:
vmin = _vmin
if _vmax > vmax:
vmax = _vmax
trace = go.Contour(z=z.T, x=xe, y=ye, xaxis=xanchor, yaxis=yanchor, name=label, connectgaps=False)
data.append(trace)
# Assume the bounds for the last graph are correct
totalWidth = ma.shape[1]
xticks, xtickslabel = self.getTicks(i)
if np.ceil(max(xticks)) != float(totalWidth):
tickscale = float(totalWidth) / max(xticks)
xticks_use = [x * tickscale for x in xticks]
else:
xticks_use = xticks
xticks_use = [np.ceil(x) for x in xticks_use]
fig['layout']['xaxis{}'.format(i + 1)].update(tickmode='array', tickvals=xticks_use, ticktext=xtickslabel, tickangle=self.label_rotation)
for trace in data:
trace.update(zmin=vmin, zmax=vmax)
fig['data'] = data
fig['layout']['annotations'] = annos
py.plot(fig, filename=self.out_file_name, auto_open=False)
def plot_heatmap(self):
matrix_flatten = None
if self.y_min == [None]:
matrix_flatten = self.hm.matrix.flatten()
# try to avoid outliers by using np.percentile
self.y_min = [np.percentile(matrix_flatten, 1.0)]
if np.isnan(self.y_min[0]):
self.y_min = [None]
if self.y_max == [None]:
if matrix_flatten is None:
matrix_flatten = self.hm.matrix.flatten()
# try to avoid outliers by using np.percentile
self.y_max = [np.percentile(matrix_flatten, 98.0)]
if np.isnan(self.y_max[0]):
self.y_max = [None]
if self.image_format == "plotly":
return self.plotly_heatmap()
ax_list = []
# turn off y ticks
for plot in range(self.numplots):
labels = []
col = plot % self.plots_per_row
row = int(plot / float(self.plots_per_row))
localYMin = None
localYMax = None
# split the ax to make room for the colorbar
sub_grid = gridspec.GridSpecFromSubplotSpec(1, 2, subplot_spec=self.grids[row, col],
width_ratios=[0.92, 0.08], wspace=0.05)
ax = self.fig.add_subplot(sub_grid[0])
cax = self.fig.add_subplot(sub_grid[1])
ax.tick_params(
axis='y',
which='both',
left='off',
right='off',
labelleft='on')
if self.per_group:
title = self.hm.matrix.group_labels[plot]
else:
title = self.hm.matrix.sample_labels[plot]
ax.set_title(title)
mat = [] # when drawing a heatmap (in contrast to drawing lines)
for data_idx in range(self.numlines):
if self.per_group:
row, col = plot, data_idx
else:
row, col = data_idx, plot
if localYMin is None or self.y_min[col % len(self.y_min)] < localYMin:
localYMin = self.y_min[col % len(self.y_min)]
if localYMax is None or self.y_max[col % len(self.y_max)] > localYMax:
localYMax = self.y_max[col % len(self.y_max)]
sub_matrix = self.hm.matrix.get_matrix(row, col)
if self.per_group:
label = sub_matrix['sample']
else:
label = sub_matrix['group']
labels.append(label)
mat.append(np.ma.__getattribute__(self.averagetype)(sub_matrix['matrix'], axis=0))
img = ax.imshow(np.vstack(mat), interpolation='nearest',
cmap='RdYlBu_r', aspect='auto', vmin=localYMin, vmax=localYMax)
self.fig.colorbar(img, cax=cax)
totalWidth = np.vstack(mat).shape[1]
xticks, xtickslabel = self.getTicks(plot)
if np.ceil(max(xticks)) != float(totalWidth):
tickscale = float(totalWidth) / max(xticks)
xticks_use = [x * tickscale for x in xticks]
ax.axes.set_xticks(xticks_use)
else:
ax.axes.set_xticks(xticks)
ax.axes.set_xticklabels(xtickslabel, rotation=self.label_rotation)
# align the first and last label
# such that they don't fall off
# the heatmap sides
ticks = ax.xaxis.get_major_ticks()
ticks[0].label1.set_horizontalalignment('left')
ticks[-1].label1.set_horizontalalignment('right')
# add labels as y ticks labels
ymin, ymax = ax.axes.get_ylim()
pos, distance = np.linspace(ymin, ymax, len(labels), retstep=True, endpoint=False)
d_half = float(distance) / 2
yticks = [x + d_half for x in pos]
# TODO: make rotation a parameter
# ax.axes.set_yticklabels(labels[::-1], rotation='vertical')
if plot == 0:
ax.axes.set_yticks(yticks)
ax.axes.set_yticklabels(labels[::-1])
else:
ax.axes.set_yticklabels([])
ax_list.append(ax)
plt.subplots_adjust(wspace=0.05, hspace=0.3)
plt.tight_layout()
plt.savefig(self.out_file_name, dpi=self.dpi, format=self.image_format)
plt.close()
def plotly_heatmap(self):
"""plot_heatmap, but with plotly output"""
fig = go.Figure()
cols = self.plots_per_row if self.numplots > self.plots_per_row else self.numplots
rows = np.ceil(self.numplots / float(cols)).astype(int)
fig['layout'].update(title=self.plot_title)
domainWidth = .9 / cols
domainHeight = .9 / rows
bufferHeight = 0.0
if rows > 1:
bufferHeight = 0.1 / (rows - 1)
else:
domainHeight = 1.0
bufferWidth = 0.0
if cols > 1:
bufferWidth = 0.1 / (cols - 1)
else:
domainWidth = 1.0
data = []
annos = []
zmin = np.inf
zmax = -np.inf
for i in range(self.numplots):
row = rows - i / self.plots_per_row - 1
col = i % self.plots_per_row
if self.per_group:
title = self.hm.matrix.group_labels[i]
else:
title = self.hm.matrix.sample_labels[i]
base = row * (domainHeight + bufferHeight)
domain = [base, base + domainHeight]
titleY = base + domainHeight
xanchor = 'x{}'.format(i + 1)
yanchor = 'y{}'.format(i + 1)
visible = False
if col == 0:
visible = True
fig['layout']['yaxis{}'.format(i + 1)] = {'domain': domain, 'anchor': xanchor, 'visible': visible}
base = col * (domainWidth + bufferWidth)
domain = [base, base + domainWidth]
titleX = base + 0.5 * domainWidth
fig['layout']['xaxis{}'.format(i + 1)] = {'domain': domain, 'anchor': yanchor}
annos.append({'yanchor': 'bottom', 'xref': 'paper', 'xanchor': 'center', 'yref': 'paper', 'text': title, 'y': titleY, 'x': titleX, 'font': {'size': 16}, 'showarrow': False})
mat = []
labels = []
for j in range(self.numlines):
if self.per_group:
row, col = i, j
else:
row, col = j, i
sub_matrix = self.hm.matrix.get_matrix(row, col)
if self.per_group:
label = sub_matrix['sample']
else:
label = sub_matrix['group']
labels.append(label)
mat.append(np.ma.__getattribute__(self.averagetype)(sub_matrix['matrix'], axis=0))
if np.min(mat[-1]) < zmin:
zmin = np.min(mat[-1])
if np.max(mat[-1]) > zmax:
zmax = np.max(mat[-1])
totalWidth = len(mat[-1])
trace = go.Heatmap(name=title, z=mat, x=range(totalWidth + 1), y=labels, xaxis=xanchor, yaxis=yanchor)
data.append(trace)
# Add ticks
xticks, xtickslabel = self.getTicks(i)
if np.ceil(max(xticks)) != float(totalWidth):
tickscale = float(totalWidth) / max(xticks)
xticks_use = [x * tickscale for x in xticks]
else:
xticks_use = xticks
xticks_use = [np.ceil(x) for x in xticks_use]
fig['layout']['xaxis{}'.format(i + 1)].update(tickmode='array', tickvals=xticks_use, ticktext=xtickslabel, tickangle=self.label_rotation)
# Adjust color scale limits
for i, trace in enumerate(data):
zminUse = zmin
zmaxUse = zmax
if self.y_min[i % len(self.y_min)] is not None:
zminUse = self.y_min[i % len(self.y_min)]
if self.y_max[i % len(self.y_max)] is not None:
zmaxUse = self.y_max[i % len(self.y_max)]
trace.update(zmin=zminUse, zmax=zmaxUse)
fig['data'] = data
fig['layout']['annotations'] = annos
py.plot(fig, filename=self.out_file_name, auto_open=False)
def plot_profile(self):
if self.y_min is None:
self.y_min = [None]
if self.y_max is None:
self.y_max = [None]
if not self.color_list:
cmap_plot = plt.get_cmap('jet')
if self.numlines > 1:
# kmeans, so we need to color by cluster
self.color_list = cmap_plot(np.arange(self.numlines, dtype=float) / float(self.numlines))
else:
self.color_list = cmap_plot(np.arange(self.numplots, dtype=float) / float(self.numplots))
if (self.numlines > 1 and len(self.color_list) < self.numlines) or\
(self.numlines == 1 and len(self.color_list) < self.numplots):
sys.exit("\nThe given list of colors is too small, "
"at least {} colors are needed\n".format(self.numlines))
for color in self.color_list:
if not pltcolors.is_color_like(color):
sys.exit("\nThe color name {} is not valid. Check "
"the name or try with a html hex string "
"for example #eeff22".format(color))
if self.image_format == "plotly":
return self.plotly_profile()
first = True
ax_list = []
for plot in range(self.numplots):
localYMin = None
localYMax = None
col = plot % self.plots_per_row
row = int(plot / float(self.plots_per_row))
if (row == 0 and col == 0) or len(self.y_min) > 1 or len(self.y_max) > 1:
ax = self.fig.add_subplot(self.grids[row, col])
else:
ax = self.fig.add_subplot(self.grids[row, col], sharey=ax_list[0])
if self.per_group:
title = self.hm.matrix.group_labels[plot]
if row != 0 and len(self.y_min) == 1 and len(self.y_max) == 1:
plt.setp(ax.get_yticklabels(), visible=False)
tickIdx = plot % self.hm.matrix.get_num_samples()
else:
title = self.hm.matrix.sample_labels[plot]
if col != 0 and len(self.y_min) == 1 and len(self.y_max) == 1:
plt.setp(ax.get_yticklabels(), visible=False)
tickIdx = plot
ax.set_title(title)
for data_idx in range(self.numlines):
if self.per_group:
_row, _col = plot, data_idx
else:
_row, _col = data_idx, plot
if localYMin is None or self.y_min[_col % len(self.y_min)] < localYMin:
localYMin = self.y_min[_col % len(self.y_min)]
if localYMax is None or self.y_max[_col % len(self.y_max)] > localYMax:
localYMax = self.y_max[_col % len(self.y_max)]
sub_matrix = self.hm.matrix.get_matrix(_row, _col)
if self.per_group:
label = sub_matrix['sample']
else:
label = sub_matrix['group']
if self.numlines > 1:
coloridx = data_idx
else:
coloridx = plot
plot_single(ax, sub_matrix['matrix'],
self.averagetype,
self.color_list[coloridx],
label,
plot_type=self.plot_type)
# remove the numbers of the y axis for all plots
plt.setp(ax.get_yticklabels(), visible=False)
if col == 0 or len(self.y_min) > 1 or len(self.y_max) > 1:
# add the y axis label for the first plot
# on each row and make the numbers and ticks visible
plt.setp(ax.get_yticklabels(), visible=True)
ax.axes.set_ylabel(self.y_axis_label)
"""
# reduce the number of yticks by half
num_ticks = len(ax.get_yticks())
yticks = [ax.get_yticks()[i] for i in range(1, num_ticks, 2)]
ax.set_yticks(yticks)
"""
totalWidth = sub_matrix['matrix'].shape[1]
xticks, xtickslabel = self.getTicks(tickIdx)
if np.ceil(max(xticks)) != float(totalWidth):
tickscale = float(totalWidth) / max(xticks)
xticks_use = [x * tickscale for x in xticks]
ax.axes.set_xticks(xticks_use)
else:
ax.axes.set_xticks(xticks)
ax.axes.set_xticklabels(xtickslabel, rotation=self.label_rotation)
# align the first and last label
# such that they don't fall off
# the heatmap sides
ticks = ax.xaxis.get_major_ticks()
ticks[0].label1.set_horizontalalignment('left')
ticks[-1].label1.set_horizontalalignment('right')
if first and self.plot_type not in ['heatmap', 'overlapped_lines']:
ax.legend(loc=self.legend_location.replace('-', ' '),
ncol=1, prop=self.font_p,
frameon=False, markerscale=0.5)
if len(self.y_min) == 1 and len(self.y_max) == 1:
first = False
"""
ax.legend(bbox_to_anchor=(-0.05, -1.13, 1., 1),
loc='upper center',
ncol=1, mode="expand", prop=font_p,
frameon=False, markerscale=0.5)
"""
lims = ax.get_ylim()
if localYMin is not None:
lims = (localYMin, lims[1])
if localYMax is not None:
lims = (lims[0], localYMax)
if lims[0] >= lims[1]:
lims = (lims[0], lims[0] + 1)
ax.set_ylim(lims)
ax_list.append(ax)
plt.subplots_adjust(wspace=0.05, hspace=0.3)
plt.tight_layout()
plt.savefig(self.out_file_name, dpi=self.dpi, format=self.image_format)
plt.close()
def plotly_profile(self):
"""
plot_profile for plotly output
y_min, y_max, and color_list are set already
"""
fig = go.Figure()
cols = self.plots_per_row if self.numplots > self.plots_per_row else self.numplots
rows = np.ceil(self.numplots / float(cols)).astype(int)
fig['layout'].update(title=self.plot_title)
domainWidth = .9 / cols
domainHeight = .9 / rows
bufferHeight = 0.0
if rows > 1:
bufferHeight = 0.1 / (rows - 1)
bufferWidth = 0.0
if cols > 1:
bufferWidth = 0.1 / (cols - 1)
data = []
annos = []
yMin = None
yMax = None
for i in range(self.numplots):
row = rows - i / self.plots_per_row - 1
col = i % self.plots_per_row
xanchor = 'x{}'.format(i + 1)
yanchor = 'y{}'.format(i + 1)
base = row * (domainHeight + bufferHeight)
domain = [base, base + domainHeight]
titleY = base + domainHeight
base = col * (domainWidth + bufferWidth)
fig['layout']['yaxis{}'.format(i + 1)] = {'domain': domain, 'title': self.y_axis_label, 'anchor': xanchor, 'autorange': False}
domain = [base, base + domainWidth]
titleX = base + 0.5 * domainWidth
fig['layout']['xaxis{}'.format(i + 1)] = {'domain': domain, 'anchor': yanchor}
if self.per_group:
title = self.hm.matrix.group_labels[i]
else:
title = self.hm.matrix.sample_labels[i]
annos.append({'yanchor': 'bottom', 'xref': 'paper', 'xanchor': 'center', 'yref': 'paper', 'text': title, 'y': titleY, 'x': titleX, 'font': {'size': 16}, 'showarrow': False})
for j in range(self.numlines):
if self.per_group:
_row, _col = i, j
else:
_row, _col = j, i
sub_matrix = self.hm.matrix.get_matrix(_row, _col)
fig['layout']['xaxis{}'.format(i + 1)].update(range=[0, sub_matrix['matrix'].shape[1]])
if self.per_group:
label = sub_matrix['sample']
else:
label = sub_matrix['group']
if self.numlines > 1:
coloridx = j
else:
coloridx = i
color = self.color_list[coloridx]
traces = plotly_single(sub_matrix['matrix'],
self.averagetype,
color,
label,
plot_type=self.plot_type)
for trace in traces:
trace.update(xaxis=xanchor, yaxis=yanchor)
if yMin is None or min(trace['y']) < yMin:
yMin = min(trace['y'])
if yMax is None or max(trace['y']) > yMax:
yMax = max(trace['y'])
if row == col == 0:
traces[0].update(showlegend=True)
data.extend(traces)
totalWidth = sub_matrix['matrix'].shape[1]
xticks, xtickslabel = self.getTicks(i)
if np.ceil(max(xticks)) != float(totalWidth):
tickscale = float(totalWidth) / max(xticks)
xticks_use = [x * tickscale for x in xticks]
else:
xticks_use = xticks
xticks_use = [np.ceil(x) for x in xticks_use]
fig['layout']['xaxis{}'.format(i + 1)].update(tickmode='array', tickvals=xticks_use, ticktext=xtickslabel, tickangle=self.label_rotation)
# Set the y limits
for i in range(self.numplots):
yaxis = 'yaxis{}'.format(i + 1)
yRange = [yMin, yMax]
if self.y_min[i % len(self.y_min)] is not None:
yRange[0] = self.y_min[i % len(self.y_min)]
if self.y_max[i % len(self.y_max)] is not None:
yRange[1] = self.y_max[i % len(self.y_max)]
fig['layout'][yaxis].update(range=yRange)
fig['data'] = data
fig['layout']['annotations'] = annos
py.plot(fig, filename=self.out_file_name, auto_open=False)
def main(args=None):
args = process_args(args)
hm = heatmapper.heatmapper()
matrix_file = args.matrixFile.name
args.matrixFile.close()
hm.read_matrix_file(matrix_file)
if hm.parameters['min threshold'] is not None or hm.parameters['max threshold'] is not None:
filterHeatmapValues(hm, hm.parameters['min threshold'], hm.parameters['max threshold'])
if args.kmeans is not None:
hm.matrix.hmcluster(args.kmeans, method='kmeans')
else:
if args.hclust is not None:
print("Performing hierarchical clustering."
"Please note that it might be very slow for large datasets.\n")
hm.matrix.hmcluster(args.hclust, method='hierarchical')
group_len_ratio = np.diff(hm.matrix.group_boundaries) / float(len(hm.matrix.regions))
if np.any(group_len_ratio < 5.0 / 1000):
problem = np.flatnonzero(group_len_ratio < 5.0 / 1000)
sys.stderr.write("WARNING: Group '{}' is too small for plotting, you might want to remove it. \n".format(hm.matrix.group_labels[problem[0]]))
if args.regionsLabel:
hm.matrix.set_group_labels(args.regionsLabel)
if args.samplesLabel and len(args.samplesLabel):
hm.matrix.set_sample_labels(args.samplesLabel)
if args.outFileNameData:
hm.save_tabulated_values(args.outFileNameData, reference_point_label=args.refPointLabel,
start_label=args.startLabel,
end_label=args.endLabel,
averagetype=args.averageType)
if args.outFileSortedRegions:
hm.save_BED(args.outFileSortedRegions)
prof = Profile(hm, args.outFileName,
plot_title=args.plotTitle,
y_axis_label=args.yAxisLabel,
y_min=args.yMin, y_max=args.yMax,
averagetype=args.averageType,
reference_point_label=args.refPointLabel,
start_label=args.startLabel,
end_label=args.endLabel,
plot_height=args.plotHeight,
plot_width=args.plotWidth,
per_group=args.perGroup,
plot_type=args.plotType,
image_format=args.plotFileFormat,
color_list=args.colors,
legend_location=args.legendLocation,
plots_per_row=args.numPlotsPerRow,
label_rotation=args.label_rotation,
dpi=args.dpi)
if args.plotType == 'heatmap':
prof.plot_heatmap()
elif args.plotType == 'overlapped_lines':
prof.plot_hexbin()
else:
prof.plot_profile()