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visualisation.py
executable file
·545 lines (496 loc) · 24 KB
/
visualisation.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import os
import sys
import matplotlib
matplotlib.use("Agg")
import matplotlib.font_manager as fm
import matplotlib.colors as colors
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import math
import shutil
import genbank_viewer as gv
from Bio import SeqIO
from weblogolib import *
import warnings
#warnings.filterwarnings('ignore')
template = {'legend.numpoints': 1, 'axes.axisbelow': True, 'axes.labelcolor': '.15', 'ytick.major.width': 1.0, 'ytick.major.size': 4.0, 'axes.grid': False, 'ytick.minor.size': 0.0, 'legend.scatterpoints': 1, 'axes.edgecolor': "black", 'grid.color': 'white', 'legend.frameon': False, 'ytick.color': '.15', 'xtick.major.size': 4.0, 'xtick.major.width': 1.0, 'figure.facecolor': "#EAEAF2", 'xtick.color': '.15', 'xtick.minor.size': 3.0, 'xtick.direction': u'out', 'lines.solid_capstyle': u'round', 'grid.linestyle': u'-', 'image.cmap': u'Greys', 'axes.facecolor': "white", 'text.color': '.15', 'ytick.direction': u'out', 'axes.linewidth': 1.0}
fonts = [font.split("/")[-1] for font in fm.findSystemFonts()]
if "Helvetica.ttf" in fonts:
sns.set(font = "Helvetica")
else:
try:
sns.set(font = "Arial")
except:
pass
sns.set_context("poster", font_scale=1.4, rc={"lines.linewidth": 1.0})
sns.set_style(template)
def save_data(matrix, kmer_count_signal, repeat_unit_array, gap_repeat_unit_array, matrix_sumx, periods, candidate, k=10, thresh=20, peak_period_list=[],xlabel_a="Position (bp)", fig_name="hoge", key="", gb="None", dtype="DNA", p_start=0, p_end=0,strand=0):
peak_matrix_list = []
matrix_sumx_list = []
for i, peak in enumerate(peak_period_list):
p_min = peak-5 if peak-5 > 0 else 0
p_max = peak+5 if peak+5 < 1000 else 1000
peak_matrix_list.append(matrix[p_min:p_max,:])
matrix_sumx_list.append(matrix_sumx[p_min:p_max])
np.savetxt("peak_matrix_" + str(peak) + ".txt",matrix[p_min:p_max,:].astype(np.int),delimiter="\t", fmt="%.0f")
np.savetxt("rpt_unit_signal_" + str(peak) + ".txt",repeat_unit_array[i],delimiter="\t", fmt="%.0f")
np.savetxt("avg_intensity_" + str(peak) + ".txt",matrix_sumx[p_min:p_max],delimiter="\t", fmt="%.0f")
np.savetxt("kmer_count_signal.txt",kmer_count_signal,delimiter="\t", fmt="%.0f")
np.savetxt("periods.txt",periods,delimiter="\t", fmt="%.0f")
return peak_matrix_list, matrix_sumx_list
def make_figure(peak_matrix_list, kmer_count_signal, repeat_unit_array, gap_repeat_unit_array, aln_repeat_unit_array, unit_length_list, matrix_sumx_list, periods, candidate, k=10, thresh=20, peak_period_list=[], xlabel_a="Position (bp)", fig_name="hoge", key="", gb="None", dtype="DNA", p_start=0, p_end=0, Format="pdf", html_path="../../../../script/html", strand=0):
if dtype == "protein":
xlabel_a = "Position (aa)"
start, end = candidate[2],candidate[3]
r_start, r_end = candidate[0], candidate[1]
#data sorting with max average intensity
zip_set = list(zip(peak_matrix_list, repeat_unit_array, matrix_sumx_list, peak_period_list))
zip_set.sort(key = lambda x: np.max(x[2]))
peak_matrix_list, repeat_unit_array, matrix_sumx_list, peak_period_list = list(zip(*zip_set))
#create figure opbject
fig = plt.figure(figsize=(9,10))
#make ax1, It is for K-mer cumulative count.
pos_data = np.array(range(len(kmer_count_signal)))
ax1 = plt.axes([0.2, 0.805, 0.55, 0.10])
ax1.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x), ',')))
ax1.fill_between(pos_data,0,kmer_count_signal,facecolor="black")
ax1.set_ylim(0,1.1 * max(kmer_count_signal))
ax1.tick_params(axis='x', which='major')
if start >= 0 and end >= 0:
ticks = [i for i in range(int(math.floor(start/10.0) * 10.0),int(math.ceil(end/10.0) * 10.0),10)]
hl = len(kmer_count_signal) / 2.0
space = round(hl * 1.0 / math.pow(10,int(math.log10(hl)))) * math.pow(10,int(math.log10(hl)))
for tick in ticks:
if (tick-ticks[0]) * 1.0 / (2*hl) >= 0.15 and tick % math.pow(10,int(math.log10(hl))) == 0:
if end > 1.0e+5:
tick_labels = [tick, tick + space]
else:
tick_labels = [tick-0.5*space, tick, tick + 0.5*space, tick + space, tick + 1.5 * space]
if tick-0.5*space < start:
tick_labels.remove(tick-0.5*space)
if tick+1.5*space > end:
tick_labels.remove(tick+1.5*space)
break
ax1.xaxis.set_ticks(list(map(lambda x: x-start, tick_labels)))
ax1_xticklocs = ax1.xaxis.get_ticklocs()
ax1.xaxis.set_ticklabels(list(map(lambda x: format(int(x), ',') if x != "" else "", tick_labels)), fontsize=22)
ax1.tick_params(top=False,right=False,pad=2)
ax1_yticklocs = ax1.yaxis.get_ticklocs()
if len(ax1_yticklocs) > 5:
ax1_yticklocs = [int(ax1_yticklocs[i]) for i in range(len(ax1_yticklocs)) if i%2 == 0]
ax1.yaxis.set_ticks(ax1_yticklocs)
ax1.set_xlabel(xlabel_a, fontsize=22, labelpad=2)
ax1.set_ylabel("Cumulative\n%d-mer count" % (k), fontsize=22)
ax1.yaxis.set_ticklabels(list(map(str,ax1_yticklocs)),fontsize=22)
ax1.yaxis.set_ticklabels(list(map(lambda x: str(int(x)), ax1_yticklocs)), fontsize=22)
ax1.spines['right'].set_visible(False)
ax1.spines['top'].set_visible(False)
if type(gb) != "None":
GV = gv.GenbView(gb,fig)
if dtype == "nucl":
box_num = GV.make_parameter_list(0,len(kmer_count_signal))
if box_num == 0:
pass
else:
ax_gv = plt.axes([0.2,0.72-0.0175*(box_num-1),0.55,0.0175*box_num])
GV.view_feature(ax_gv)
elif dtype == "prot":
box_num = 0
ax1.set_xlim(0,len(pos_data))
start_y = 0.695 - 0.0160 * box_num
vmin = 0
vmax = np.max(list(map(np.max,peak_matrix_list)))
new_periods = periods
for i, peak in enumerate(peak_period_list):
matrix = peak_matrix_list[i]
matrix_sumx = matrix_sumx_list[i]
p_min = peak-5 if peak-5 > 0 else 0
p_max = peak+5 if peak+5 < 1000 else 1000
axn1 = plt.axes([0.2,start_y-i*0.13-0.09,0.55,0.08])
img = axn1.imshow(matrix, extent=[0, matrix.shape[1], p_max, p_min], aspect="auto", interpolation="nearest", cmap="YlGnBu_r", vmin=vmin, vmax=vmax)
axn1.set_xlim(0,matrix.shape[1])
axn1.set_ylabel("Period",fontsize=22)
axn1_yticklocs = axn1.yaxis.get_ticklocs()
if len(axn1_yticklocs) > 4:
axn1_yticklocs = [int(j) for j in axn1_yticklocs if j%4 == 0]
axn1.yaxis.set_ticks(axn1_yticklocs)
axn1.yaxis.set_ticklabels(list(map(lambda x: str(int(x)), axn1_yticklocs)), fontsize=22)
axn1.set_ylim(p_max,p_min)
axn1.tick_params(top=False)
axn1.tick_params(right=False)
axn1.tick_params(pad=4)
if i == 0:
axn2 = plt.axes([0.2, start_y-i*0.13 + 0.02, 0.55, 0.02])
else:
axn2 = plt.axes([0.2, start_y-i*0.13, 0.55, 0.02])
cmap = matplotlib.cm.spring
norm = matplotlib.colors.Normalize(vmin=0.0,vmax=1.0)
axn2.patch.set_facecolor('#FFFFF5')
ul = unit_length_list[i]
for unitx, unity in repeat_unit_array[i]:
for x,y in zip(unitx, unity):
axn2.fill_between([x,x+1],[y,y],0,color=cmap(norm((unitx[-1]-x)*1.0/ul)))
x_array = []
y_array = []
for unitx, unity in gap_repeat_unit_array[i]:
axn2.fill_between(unitx,unity,0,color="#FFFFF5",lw=0.5)
x_array.extend(unitx)
y_array.extend(unity)
x_array = []
y_array = []
for unitx, unity in aln_repeat_unit_array[i]:
axn2.add_patch(matplotlib.patches.Rectangle((unitx[0], 0.04), unitx[-1]-unitx[0], 0.96, alpha=1, facecolor='none', lw=0.5,edgecolor="k"))
x_array.extend(unitx)
y_array.extend(unity)
x_array2 = [0] + x_array + [matrix.shape[1]]
y_array2 = [0] + y_array + [0]
axn2.fill_between(x_array2,y_array2,1.05,color="#FFFFF5",lw=0)
axn2.set_ylim(0.025,1.05)
axn2.set_xlim(0,matrix.shape[1])
axn2.tick_params(top=False)
axn2.tick_params(right=False)
axn2.yaxis.set_label_coords(-0.09,0.5)
axn2.spines['right'].set_visible(False)
axn2.spines['left'].set_visible(False)
axn2.spines['top'].set_visible(False)
axn2.spines['bottom'].set_visible(False)
axn2.yaxis.set_ticks([])
axn2.yaxis.set_ticklabels([])
axn2.xaxis.set_ticks([])
axn2.xaxis.set_ticklabels([])
axn2.tick_params(pad=4)
axn3 = plt.axes([0.760,start_y-i*0.13-0.09,0.1,0.08])
axn3.barh(new_periods[p_min:p_max+1],matrix_sumx,facecolor="lightgrey",align="center",linewidth=0.5,edgecolor="k")
axn3.set_xlim(0,np.max(matrix_sumx) * 1.1)
axn3.set_ylim(p_max,p_min)
axn3.tick_params(top=False)
axn3.tick_params(right=False)
axn3.tick_params(left=False)
axn3.tick_params(bottom=False)
axn3.yaxis.set_ticks_position('right')
axn3.yaxis.set_ticks([peak])
axn3.yaxis.set_ticklabels(["%d" %(peak)], fontsize=22)
axn3.tick_params(pad=4)
if i == len(peak_period_list) - 1:
axn1.set_xlabel(xlabel_a, fontsize=22)
#axn1.xaxis.set_ticks(ax1_xticklocs)
axn1.xaxis.set_ticks([])
axn1.xaxis.set_ticklabels([])
axn3_xticklocs = axn3.xaxis.get_ticklocs()
axn3.xaxis.set_ticks([])
axn3.xaxis.set_ticklabels([])
axn3.tick_params(bottom=True,pad=6)
axn3.set_xlabel("Average\n Intensity (a.u.)",fontsize=22)
cbar_ax = fig.add_axes([0.120, start_y - 0.120 - i * 0.13, 0.10, 0.018])
fig.colorbar(img, cax=cbar_ax, orientation="horizontal", ticks=[0, int(np.max(matrix))])
cbar_ax.xaxis.set_ticks([])
cbar_ax.xaxis.set_ticklabels([])
cbar_ax.set_xlabel("Intensity (a.u.)", fontsize=22, labelpad=8)
cbar_ax.tick_params(pad=6)
else:
axn1.xaxis.set_ticks([])
axn1.xaxis.set_ticklabels([])
axn3.xaxis.set_ticks([])
axn3.xaxis.set_ticklabels([])
ax3_title = plt.axes([0.2, start_y + 0.4, 0.55, 0.03])
ax3_title.text(0.5,0.47, "Periodic mortif area", horizontalalignment='center',verticalalignment='center', fontsize=22)
ax3_title.xaxis.set_ticks([])
ax3_title.xaxis.set_ticklabels([])
ax3_title.yaxis.set_ticks([])
ax3_title.yaxis.set_ticklabels([])
ax3_title.spines['right'].set_visible(False)
ax3_title.spines['top'].set_visible(False)
ax3_title.spines['left'].set_visible(False)
ax3_title.spines['bottom'].set_visible(False)
axn1_title = plt.axes([0.2, start_y, 0.55, 0.02])
axn1_title.text(0.5,0.15,"Position-Period Matrix", horizontalalignment='center',verticalalignment='center', fontsize=22)
axn1_title.xaxis.set_ticks([])
axn1_title.xaxis.set_ticklabels([])
axn1_title.yaxis.set_ticks([])
axn1_title.yaxis.set_ticklabels([])
axn1_title.spines['right'].set_visible(False)
axn1_title.spines['top'].set_visible(False)
axn1_title.spines['left'].set_visible(False)
axn1_title.spines['bottom'].set_visible(False)
ax7 = plt.axes([0.2,0.21,0.55,0.73])
ax7.set_xlim(0, len(pos_data))
ax7.set_ylim(0.17,0.90)
ax7.patch.set_alpha(0.0)
ax7.xaxis.set_ticks([])
ax7.xaxis.set_ticklabels([])
ax7.yaxis.set_ticks([])
ax7.yaxis.set_ticklabels([])
ax7.spines['right'].set_visible(False)
ax7.spines['top'].set_visible(False)
ax7.spines['left'].set_visible(False)
ax7.spines['bottom'].set_visible(False)
ax7.tick_params(pad=4)
fig.patch.set_alpha(0.0)
rptunit_ax = fig.add_axes([0.760, 0.7-0.0160 * (box_num-1), 0.1, 0.018])
cmap = matplotlib.cm.spring_r
cmaplist = [cmap(i) for i in range(256)]
bounds = np.linspace(0,255,256)
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
cmap = cmap.from_list('Custom cmap', cmaplist, 256)
cb = matplotlib.colorbar.ColorbarBase(rptunit_ax, cmap=cmap, norm=norm, spacing='proportional', ticks=[], boundaries=bounds, orientation='horizontal')
rptunit_ax.tick_params(pad=20,)
cb.ax.text(-0.3,-1.32,"Repeat unit",fontsize=22)
cb.outline.set_linewidth(0.5)
if Format == "svg":
path = os.getcwd()
shutil.copytree(html_path, path + "/html")
os.chdir("html")
plt.savefig(fig_name + "." + Format, transparent=False)
svg = open(fig_name + "." + Format)
url_list = [url.rstrip() for url in open("../annotaiton_url_list.txt","r")]
new_svg = set_url(svg,url_list)
html_temp = open("template.html").read()
new_html = html_temp % (fig_name,new_svg)
open(fig_name + ".html","w").write(new_html)
else:
plt.savefig(fig_name + "." + Format, transparent=False)
plt.close()
def set_url(svg,url_list):
url_num = 0
new_svg = ""
for line in svg:
if "Annotation_" in line:
new_svg += '<a href="%s" target="_blank" class="livepreview" data-position="bottom">\n' % url_list[url_num]
url_num += 1
else:
new_svg += line
return new_svg
def motif_logo(dtype):
fasta_name = "align.unit_seq.fasta"
fasta = open(fasta_name)
seqs = read_seq_data(fasta)
data = LogoData.from_seqs(seqs)
if dtype == "nucl":
data.alphabet = std_alphabets["dna"]
options = LogoOptions()
options.logo_font = "Arial_Bold"
options.number_fontsize = 34
options.fontsize = 34
options.logo_margin = 30
options.stacks_per_line = 150
options.scale_width = 0
options.stack_width = 15
options.stack_aspect_ratio = 5
options.stroke_width = 1.5
options.tic_length = 7
options.fineprint = ""
options.color_scheme = std_color_schemes["classic"]
options.yaxis_label = "Bits"
options.yaxis_tic_interval = 1
options.yaxis_minor_tic_ratio = 2
format = LogoFormat(data, options)
fout = open("weblogo.pdf","wb")
try:
fout = open("weblogo.pdf","wb")
fout.write(pdf_formatter(data, format))
fout.close()
except:
fout.close()
warnings.warn("Ghostscript was not found. If you want to generate LOGO in pdf format, please install Ghostscript.\n For the present, LOGO was output in EPS format")
os.system("rm -rf weblogo.pdf")
fout = open("weblogo.eps","wb")
fout.write(eps_formatter(data, format))
fout.close()
try:
fout = open("weblogo.txt","wb")
fout.write(txt_formatter(data, format).decode("utf-8"))
except:
fout = open("weblogo.txt","wb")
fout.write(txt_formatter(data, format))
fout.close()
else:
data.alphabet = std_alphabets["protein"]
options = LogoOptions()
options.logo_font = "Arial_Bold"
options.text_foint = "Helvetica"
options.stacks_per_line = 150
options.number_fontsize = 34
options.fontsize = 34
options.logo_margin = 30
options.stacks_per_line = 150
options.scale_width = 0
options.stack_width = 15
options.stack_aspect_ratio = 5
options.stroke_width = 1.5
options.tic_length = 7
options.fineprint = ""
options.color_scheme = std_color_schemes["chemistry"]
options.yaxis_label = "Bits"
options.yaxis_tic_interval = 2
options.yaxis_minor_tic_ratio = 2
format = LogoFormat(data, options)
try:
fout = open("weblogo.pdf","wb")
fout.write(pdf_formatter(data, format))
fout.close()
except:
fout.close()
warnings.warn("Ghostscript was not found. If you want to generate LOGO in pdf format, please install Ghostscript.\n For the present, LOGO was output in EPS format")
os.system("rm -rf weblogo.pdf")
fout = open("weblogo.eps","wb")
fout.write(eps_formatter(data, format))
fout.close()
try:
fout = open("weblogo.txt","wb")
fout.write(txt_formatter(data, format).decode("utf-8"))
except:
fout = open("weblogo.txt","wb")
fout.write(txt_formatter(data, format))
fout.close()
def load_data(dtype, strand, ksize ,thresh, Format="pdf"):
dir_list = os.listdir("./")
peak_list = []
unit_length_list = []
peak_matrix_list = []
rpt_unit_array_list = []
gap_rpt_unit_array_list = []
aln_rpt_unit_array_list = []
avg_intensity_list = []
for file_name in dir_list:
kmer_count_signal = []
kmer_file = open("kmer.tsv")
kmer_file.readline()
for line in kmer_file:
line = line.rstrip().split("\t")
kmer_count_signal.append(int(line[1]))
kmer_coount_signal = np.array(kmer_count_signal)
gbk = SeqIO.read("repeat.gbk","genbank")
for feature in gbk.features:
if "note" in feature.qualifiers.keys():
if "SPADE" in feature.qualifiers["note"][0]:
peak_list.append(int(feature.qualifiers["period"][0]))
candidate = list(map(int,feature.qualifiers["hrr_range"][0].split(",")))
if dtype == "prot":
p_start = feature.location.start
p_end = feature.location.end
for peak in peak_list:
for file_name in dir_list:
if "align.unit_seq.fasta" == file_name:
se_sets = []
gap_se_sets = []
aln_se_sets = []
for line in open(file_name).readlines():
if line[0] == ">":
line = line.rstrip().split("_")
s = int(line[1])
s = s if s > 0 else 0
else:
unit_length = len(line.rstrip())
unit_length = peak if unit_length > peak else unit_length
seq = line.rstrip()
e = s + unit_length
for m, char in enumerate(seq):
if char == "-":
pass
else:
break
for n, char in enumerate(reversed(seq)):
if char == "-":
pass
else:
break
se_sets.append([s,e])
aln_se_sets.append([s+m,e-n])
if m > 0 and len(gap_se_sets) == 0:
gap_se_sets.append([s,s+m])
if n > 0:
gap_se_sets.append([e-n,e])
repeat_unit_array_x = []
repeat_unit_array_y = []
for start, end in se_sets:
unit_x = [start]
unit_y = [0]
for j in range(start, end+1):
unit_x.append(j)
unit_y.append(1)
unit_x.append(j)
unit_y.append(1)
unit_x.append(j)
unit_y.append(0)
repeat_unit_array_x.append(unit_x)
repeat_unit_array_y.append(unit_y)
rpt_unit_array = zip(repeat_unit_array_x, repeat_unit_array_y)
rpt_unit_array_list.append(rpt_unit_array)
repeat_unit_array_x = []
repeat_unit_array_y = []
for start, end in gap_se_sets:
unit_x = [start]
unit_y = [0]
for j in range(start, end+1):
unit_x.append(j)
unit_y.append(1)
unit_x.append(j)
unit_y.append(1)
unit_x.append(j)
unit_y.append(0)
repeat_unit_array_x.append(unit_x)
repeat_unit_array_y.append(unit_y)
rpt_unit_array = zip(repeat_unit_array_x, repeat_unit_array_y)
gap_rpt_unit_array_list.append(rpt_unit_array)
repeat_unit_array_x = []
repeat_unit_array_y = []
for start, end in aln_se_sets:
unit_x = [start]
unit_y = [0]
for j in range(start, end+1):
unit_x.append(j)
unit_y.append(1)
unit_x.append(j)
unit_y.append(1)
unit_x.append(j)
unit_y.append(0)
repeat_unit_array_x.append(unit_x)
repeat_unit_array_y.append(unit_y)
rpt_unit_array = zip(repeat_unit_array_x, repeat_unit_array_y)
aln_rpt_unit_array_list.append(rpt_unit_array)
unit_length_list.append(unit_length)
avg_intensity = []
pdist_file = open("pdist.tsv")
pdist_file.readline()
for line in pdist_file:
avg_intensity.append(float(line.rstrip().split("\t")[1]))
avg_intensity = np.array(avg_intensity)
if peak > 25:
avg_intensity_list.append(avg_intensity[int(len(avg_intensity)/2)-5:int(len(avg_intensity)/2)+5+1])
else:
avg_intensity_list.append(avg_intensity)
matrix_file = open("ppm4vis.tsv")
matrix_file.readline()
matrix_file.readline()
peak_matrix = []
periods = []
for line in matrix_file:
line = list(map(int, line.rstrip().split("\t")))
peak_matrix.append(line[1:])
periods.append(line[0])
periods = list(range(0,periods[-1]+1))
peak_matrix = np.array(peak_matrix)
if peak > 25:
peak_matrix = peak_matrix[int(peak_matrix.shape[0]/2)-5:int(peak_matrix.shape[0]/2)+5+1,:]
else:
pass
if strand == -1:
peak_matrix = peak_matrix[:,::-1]
peak_matrix_list.append(peak_matrix)
else:
pass
if dtype == "nucl":
make_figure(peak_matrix_list, kmer_count_signal, rpt_unit_array_list, gap_rpt_unit_array_list, aln_rpt_unit_array_list, unit_length_list, avg_intensity_list, periods, candidate, peak_period_list=peak_list, gb=gbk, fig_name="periodic_repeat", thresh=thresh, dtype=dtype ,Format=Format, k=ksize, strand=strand)
elif dtype == "prot":
make_figure(peak_matrix_list, kmer_count_signal, rpt_unit_array_list, gap_rpt_unit_array_list, aln_rpt_unit_array_list, unit_length_list, avg_intensity_list, periods, candidate, peak_period_list=peak_list, gb=gbk, fig_name="periodic_repeat", thresh=thresh, dtype=dtype ,Format=Format, p_start=p_start, p_end=p_end, k=ksize, strand=strand)
return peak_list
if __name__ == "__main__":
if sys.argv[1] == "nucl":
peak_list = load_data("nucl",1,10,0)
motif_logo("nucl")
elif sys.argv[1] == "prot":
peak_list = load_data("prot",1,3,0)
motif_logo("prot")