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main_new.py
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main_new.py
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import code
import sys
from argparse import ArgumentParser, FileType
import numpy as np
import matplotlib.cm as cm
import matplotlib as mpl
import matplotlib.pyplot as plt
from SliderClass import Slider
import math
import sample
import analysis
import graph
import graph_edit
import load
import random
#import readLoopseq
from sklearn import linear_model
import Nucleosome_positioning as nuc
def display_graph (filenames1,
filenames2,
filenames3,
ref_fname,
ref_length,
dyad_offset,
stat_mode,
graph_mode,
norm_choice,
sample_mode):
dyad_axis = ref_length/2
# read files and load data
key_slider = load.load_files(filenames1, ref_length, dyad_axis, dyad_offset, filter_num = 30, fill='linear', load_ref = ref_fname)
#key_slider1 = load.load_files(filenames1, ref_length, dyad_axis, dyad_offset, filter_num = 30, fill='linear', load_ref = ref_fname)
#key_slider2 = load.load_files(filenames2, ref_length, dyad_axis, dyad_offset, filter_num = 30, fill='linear', load_ref = ref_fname)
#code.interact(local=locals())
"""
# save positioning data
def tuple_cmp(a,b):
win1, st1 = a.split('-')[0], a.split('-')[1]
win2, st2 = b.split('-')[0], b.split('-')[1]
if len(win1) < len(win2):
return -1
elif len(win1) > len(win2):
return 1
else:
if int(st1) < int(st2):
return -1
elif int(st1) > int(st2):
return 1
else:
return 0
f = open("after.txt", 'w')
for key in sorted(key_slider.keys(), cmp=tuple_cmp):
slider = key_slider[key]
dyadmap = slider.dyadmap
print >> f, '>%s' % (key)
s = ""
for i in range(len(dyadmap)):
value = dyadmap[i]
if i == 0:
s += str(value)
continue
s += "," + str(value)
print >> f, s
f.close()
"""
# left/right scattering plot
left, right = [], []
for key in key_slider:
win, loc = key.split('-')
size, loc = len(win), int(loc)
if size % 2 != 0:
loc = loc + size/2
else:
loc = loc + size/2 - 0.5
if loc < dyad_axis:
left.append(key)
elif loc > dyad_axis:
right.append(key)
sample_list = [left, right]
graph.plot_corr2(key_slider, Slider.Amer_len, Slider.median_pos, xlabel='Poly-A length(bp)', ylabel='Mean position', sample_labels = ['PolyA in Left','PolyA in Right'], sample_list=sample_list)
"""
# mean position heat map
keys = list(set(key_slider1.keys()) & set(key_slider2.keys()))
map1 = [[np.nan for i in range(ref_length)] for i in range(3,26)]
map2 = [[np.nan for i in range(ref_length)] for i in range(3,26)]
map3 = [[np.nan for i in range(ref_length)] for i in range(3,26)]
origin1 = key_slider1["AAA-46"].median_pos()
origin2 = key_slider2["AAA-46"].median_pos()
for key in keys:
slider1, slider2 = key_slider1[key], key_slider2[key]
win, loc = key.split('-')
size, loc = len(win), int(loc)
median_pos1 = slider1.median_pos() #- origin1
median_pos2 = slider2.median_pos() #- origin2
median_pos3 = slider2.median_pos() #- slider1.median_pos() - (origin2 - origin1)
#median_pos = slider2.find_peaks(choice='dyad', num=1)[0][0] - slider1.find_peaks(choice='dyad', num=1)[0][0]
#median_pos = slider.find_peaks(choice='dyad', num=1)[0][0] - dyad_axis
map1[size-3][loc] = median_pos1
map2[size-3][loc] = median_pos2
map3[size-3][loc] = median_pos3
maps = [map1, map2, map3]
for k in range(len(maps)):
map = maps[k]
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_yticks(range(0,23))
ax1.set_yticklabels([str(i) for i in range(3,26)])
ax1.set_xticks([i+0.5 for i in range(0,225,16)])
ax1.set_xticklabels([str(i) for i in range(1,226,16)])
ax1.set_xlabel('Poly-A start location (bp)')
ax1.set_ylabel('Poly-A length (bp)')
ax2 = ax1.twiny()
cmap = cm.seismic
cmap.set_bad('white',1.)
#ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-50, vmax=50)
ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-20, vmax=20)
#ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-5, vmax=5)
#ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-100, vmax=100)
#ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-200, vmax=200)
for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
ax2.axvline(x, color='k', linestyle='--', linewidth=3)
ax2.set_xticks([dyad_axis-30, dyad_axis, dyad_axis+30])
ax2.set_xticklabels(['-30','Center','+30'])
plt.savefig('median_position_heatmap_' + str(k) + '.png')
#plt.show()
plt.close()
# sensitivity map (mean position)
smaps = []
for u in range(len(maps)):
smap = np.zeros((23, ref_length))
smap.fill(np.nan)
map = maps[u]
for i in range(3,26):
for j in range(ref_length):
if np.isnan(map[i-3][j]):
continue
for k in range(i):
if np.isnan(smap[i-3][j+k]):
smap[i-3][j+k] = 0.0
smap[i-3][j+k] += map[i-3][j]/float(i)
#smap[i-3][j+k] += map[i-3][j]
smaps.append(smap)
for u in range(len(smaps)):
smap = smaps[u]
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_yticks(range(0,23))
ax1.set_yticklabels([str(i) for i in range(3,26)])
ax1.set_xticks([i+0.5 for i in range(0,225,16)])
ax1.set_xticklabels([str(i) for i in range(1,226,16)])
ax1.set_xlabel('DNA template (bp)')
ax1.set_ylabel('Poly-A length (bp)')
ax1.xaxis.label.set_size(15)
ax1.yaxis.label.set_size(15)
ax1.tick_params(axis='both', which='major', labelsize=10)
ax2 = ax1.twiny()
cmap = cm.seismic
#cmap = cm.get_cmap('Spectral_r')
cmap.set_bad('white',1.)
ax2.imshow(smap, cmap=cmap, interpolation='none', aspect='auto', vmin=-20, vmax=20)
#ax2.imshow(smap, cmap=cmap, interpolation='none', aspect='auto', vmin=-15, vmax=15)
#ax2.imshow(smap, cmap=cmap, interpolation='none', aspect='auto', vmin=-30, vmax=30)
#ax2.imshow(smap, cmap=cmap, interpolation='none', aspect='auto', vmin=-50, vmax=50)
#ax2.imshow(smap, cmap=cmap, interpolation='none', aspect='auto', vmin=-200, vmax=200)
for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
ax2.axvline(x, color='k', linestyle='--', linewidth=3)
ax2.set_xticks([dyad_axis-30, dyad_axis, dyad_axis+30])
ax2.tick_params(axis='both', which='major', labelsize=10)
ax2.set_xticklabels(['-30','Center','+30'])
plt.tight_layout()
plt.savefig('median_position_smap_' + str(u+1) + '.png')
#plt.show()
plt.close()
# collapse the heat map (mean position)
#lines = []
#for u in range(len(maps)):
# line = np.zeros((1, ref_length))
# line.fill(np.nan)
# map = maps[u]
# for i in range(3,26):
# for j in range(ref_length):
# if np.isnan(map[i-3][j]):
# continue
# for k in range(i):
# if np.isnan(line[0][j+k]):
# line[0][j+k] = 0.0
# line[0][j+k] += map[i-3][j]
# lines.append(line)
#for i in range(len(lines)):
# line = lines[i]
# fig = plt.figure()
# ax1 = fig.add_subplot(111)
# ax2 = ax1.twiny()
# cmap = cm.seismic
# cmap.set_bad('white',1.)
# ax2.imshow(line, cmap=cmap, interpolation='none', aspect='auto', vmin=-3000, vmax=3000)
# for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
# ax2.axvline(x, color='k', linestyle='--', linewidth=3)
# ax2.set_xticks([dyad_axis-30, dyad_axis, dyad_axis+30])
# ax2.set_xticklabels(['-30','Center','+30'])
# #plt.imshow([line], interpolation='none', aspect='auto')
# plt.show()
# plt.close()
# calculate effective force map
force_map1, force_map2, force_map3 = {}, {}, {}
energy_map1, energy_map2, energy_map3 = {}, {}, {}
keys = list(set(key_slider1.keys()) & set(key_slider2.keys()))
for key in keys:
win, loc = key.split('-')
size, loc = len(win), int(loc)
slider1 = key_slider1[key]
slider2 = key_slider2[key]
dforce1 = slider1.force_profile()
dforce2 = slider2.force_profile()
dforce3 = dforce2 - dforce1
denergy1 = slider1.energy_profile()
denergy2 = slider2.energy_profile()
denergy3 = denergy2 - denergy1
for i in range(66, len(dforce1)-66):
pos = loc - i
new_key = (size, pos)
if new_key not in force_map1:
force_map1[new_key] = []
if new_key not in force_map2:
force_map2[new_key] = []
if new_key not in force_map3:
force_map3[new_key] = []
if new_key not in energy_map1:
energy_map1[new_key] = []
if new_key not in energy_map2:
energy_map2[new_key] = []
if new_key not in energy_map3:
energy_map3[new_key] = []
force_map1[new_key].append(dforce1[i])
force_map2[new_key].append(dforce2[i])
force_map3[new_key].append(dforce3[i])
energy_map1[new_key].append(denergy1[i])
energy_map2[new_key].append(denergy2[i])
energy_map3[new_key].append(denergy3[i])
# mean force heat map
fm_list = [force_map1, force_map2, force_map3]
#map = [[np.nan for i in range(ref_length)] for i in range(3,26)]
maps = []
for k in range(len(fm_list)):
#map_list = []
map = np.zeros((23, ref_length))
map.fill(np.nan)
force_map = fm_list[k]
for key in force_map:
mean = np.mean(force_map[key])
size, pos = key
map[size - 3, pos + dyad_axis] = mean
maps.append(map)
#graph.plot_heatmap3(map_list, cmap, vmin = -30, vmax = 30, yticks=yticks, vlines=vlines, upticks=upticks, xlabels = ["", "Poly-A start location (bp)", ""], ylabels = ["Poly-A length (bp)"]*3, titles = ["Before", "After", "After-Before"], aspect=10, note="MDP")
#for u in range(len(maps)):
# map = maps[u]
# fig = plt.figure()
# ax1 = fig.add_subplot(111)
# ax1.set_yticks(range(0,23))
# ax1.set_yticklabels([str(i) for i in range(3,26)])
# ax1.set_xticks([i+0.5 for i in range(0,225,16)])
# ax1.set_xticklabels([str(i) for i in range(-112, 113, 16)])
# ax1.set_xlabel('Location w.r.t. Dyad (bp)')
# ax1.set_ylabel('Poly-A length (bp)')
# ax2 = ax1.twiny()
# cmap = cm.seismic
# cmap.set_bad('white',1.)
# ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-10, vmax=10)
# #ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-0.4, vmax=0.4)
# for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
# ax2.axvline(x, color='k', linestyle='--', linewidth=3)
# ax2.set_xticks([dyad_axis-30, dyad_axis, dyad_axis+30])
# ax2.set_xticklabels(['-SHL3','Dyad','+SHL3'])
# #plt.savefig('mean_force_heatmap_' + str(i) + '.png')
# plt.show()
# plt.close()
# sensitivity map (mean force)
smaps = []
for u in range(len(maps)):
smap = np.zeros((23, ref_length))
smap.fill(np.nan)
map = maps[u]
for i in range(3,26):
for j in range(ref_length):
if np.isnan(map[i-3][j]):
continue
for k in range(i):
if np.isnan(smap[i-3][j+k]):
smap[i-3][j+k] = 0.0
#smap[i-3][j+k] += map[i-3][j]/float(i)
smap[i-3][j+k] += map[i-3][j]
smaps.append(smap)
for u in range(len(smaps)):
map = smaps[u]
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_yticks(range(0,23))
ax1.set_yticklabels([str(i) for i in range(3,26)])
ax1.set_xticks([i+0.5 for i in range(0,225,16)])
ax1.set_xticklabels([str(i) for i in range(-112, 113, 16)])
ax1.set_xlabel('Location w.r.t. Dyad (bp)')
ax1.set_ylabel('Poly-A length (bp)')
ax2 = ax1.twiny()
cmap = cm.seismic
cmap.set_bad('white',1.)
#cmap.set_under('lightcyan')
#cmap.set_over('lightpink')
ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-1, vmax=1)
#ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-0.4, vmax=0.4)
for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
ax2.axvline(x, color='k', linestyle='--', linewidth=3)
ax2.set_xticks([dyad_axis-30, dyad_axis, dyad_axis+30])
ax2.set_xticklabels(['-SHL3','Dyad','+SHL3'])
plt.savefig('mean_force_smap_' + str(u) + '.png')
plt.show()
plt.close()
# collapes the heat map (mean force)
map_list = []
for k in range(len(fm_list)):
position_map = np.zeros((1,ref_length))
position_map.fill(np.nan)
force_map = fm_list[k]
for key in force_map:
size, pos = key
if size > 12:
continue
mean = np.mean(force_map[key])
for i in range(pos + dyad_axis, pos + dyad_axis + size):
if np.isnan(position_map[0, i]):
position_map[0, i] = 0.0
position_map[0, i] += mean
map_list.append(position_map)
cmap = cm.seismic
cmap.set_bad('black',1.)
xticks = [range(0, ref_length, 16), [str(i) for i in range(-112, 113, 16)]]
vlines = [dyad_axis - 65, dyad_axis - 20, dyad_axis, dyad_axis + 20, dyad_axis + 65]
upticks = [ [dyad_axis - 65, dyad_axis - 20, dyad_axis, dyad_axis + 20, dyad_axis + 65], ['SHL6.5', 'SHL2', 'dyad', 'SHL2','SHL6.5'] ]
graph.plot_heatmap4(map_list, dim = [3,1], cmap_list = [cmap]*3, vmin_list = [-10,-10,-5], vmax_list = [10,10,5], yticks_list = [[[],[]], [[],[]], [[],[]]], xticks_list = [[[],['']*len(xticks[1])],[[],['']*len(xticks[1])],xticks], vlines_list=[vlines]*3, upticks_list=[upticks,[[],[]], [[],[]]], ylabels = ["Before", "After", "After-Before"], aspect_list=[25]*3, note="MDP", xlabels=['','','Location w.r.t. Dyad (bp)'])
#fig = plt.figure()
#img = plt.imshow(position_map, cmap=cmap, interpolation='none', aspect='auto', vmin=-5, vmax=5)
#img = plt.imshow(position_map, cmap=cmap, interpolation='none', aspect='auto', vmin=-10, vmax=10)
#for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
# plt.axvline(x, color='k', linestyle='--', linewidth=2)
#xlabels = [str(i) for i in range(-112, 113, 16)]
#plt.xticks(range(0, ref_length, 16), xlabels)
#plt.xlabel('Location w.r.t. Dyad (bp)')
#plt.savefig("sensitivitymap.png")
#plt.close()
#plt.show()
#linear regression
def read_ref (ref_fname):
key_seq = {}
for line in open(ref_fname):
line = line.strip()
if line.startswith(">"):
key = line[1:]
continue
else:
assert key not in key_seq
key_seq[key] = line
return key_seq
def Amer_len(seq, pos=False):
num = []
num_pos = {}
i = 0
while i < len(seq):
if seq[i] in 'AT':
nt = seq[i]
count = 1
j = i + 1
while j < len(seq):
if seq[j] != nt:
break
count +=1
j +=1
num.append(count)
if count not in num_pos:
num_pos[count] = []
num_pos[count].append(i)
i = j
else:
i +=1
if pos:
return num_pos
if len(num) == 0:
return 0
return max(num)
key_seq = read_ref("polyAscanlib.ref")
keys = list(set(key_slider1.keys()) & set(key_slider2.keys()))
y1, y2, y3 = [], [], []
X = []
smX = []
for key in keys:
slider1 = key_slider1[key]
slider2 = key_slider2[key]
mpos1 = slider1.median_pos() - origin1
mpos2 = slider2.median_pos() - origin2
mpos3 = mpos2 - mpos1 - (origin2 - origin1)
y1.append(mpos1)
y2.append(mpos2)
y3.append(mpos3)
seq = key_seq[key]
Anum_pos = Amer_len(seq, pos=True)
x = []
for i in range(3, 26):
temp = [0]* ref_length
try:
Apos = Anum_pos[i]
except:
x += temp
continue
for j in range(ref_length):
if j not in Apos:
continue
for u in range(j, j+i):
temp[u] = 1
x += temp
X.append(x)
reg1 = linear_model.Ridge (alpha = .5)
reg2 = linear_model.Ridge (alpha = .5)
reg3 = linear_model.Ridge (alpha = .5)
reg1.fit(X, y1)
reg2.fit(X, y2)
reg3.fit(X, y3)
prey1 = reg1.predict(X)
prey2 = reg2.predict(X)
prey3 = reg3.predict(X)
ys = [y1,y2,y3]
preys = [prey1, prey2, prey3]
titles = ["before", "after", "after-before"]
for i in range(3):
fig = plt.figure()
plt.plot(ys[i], preys[i], 'k.')
plt.xlabel("Experiment")
plt.ylabel("Prediction")
plt.title("Poly-A linear model: " + titles[i])
plt.xlim([-20,30])
plt.ylim([-20,30])
plt.show()
plt.close()
regs = [reg1, reg2, reg3]
maps = []
for i in range(len(regs)):
reg = regs[i]
map = reg.coef_.reshape((23, ref_length))
maps.append(map)
for u in range(len(maps)):
map = maps[u]
fig = plt.figure()
ax1 = fig.add_subplot(111)
ax1.set_yticks(range(0,23))
ax1.set_yticklabels([str(i) for i in range(3,26)])
ax1.set_xticks([i+0.5 for i in range(0,225,16)])
ax1.set_xticklabels([str(i) for i in range(1,226,16)])
ax1.set_xlabel('DNA template (bp)')
ax1.set_ylabel('Poly-A length (bp)')
ax2 = ax1.twiny()
cmap = cm.seismic
cmap.set_bad('white',1.)
ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-5, vmax=5)
#ax2.imshow(map, cmap=cmap, interpolation='none', aspect='auto', vmin=-200, vmax=200)
for x in [dyad_axis-30, dyad_axis, dyad_axis+30]:
ax2.axvline(x, color='k', linestyle='--', linewidth=3)
ax2.set_xticks([dyad_axis-30, dyad_axis, dyad_axis+30])
ax2.set_xticklabels(['-30','Center','+30'])
plt.savefig('median_linear_smap_' + str(u+1) + '.png')
plt.show()
plt.close()
# linear model with fewer variables
y1, y2, y3 = [], [], []
smX = []
for key in keys:
slider1 = key_slider1[key]
slider2 = key_slider2[key]
mpos1 = slider1.median_pos()
mpos2 = slider2.median_pos()
mpos3 = mpos2 - mpos1
y1.append(mpos1)
y2.append(mpos2)
y3.append(mpos3)
seq = key_seq[key]
Anum_pos = Amer_len(seq, pos=True)
temp = [0] * ref_length
for num, poslist in Anum_pos.items():
for k in range(len(poslist)):
pos = poslist[k]
for i in range(pos, pos+num):
temp[i] = 1
smX.append(temp)
reg1 = linear_model.Ridge (alpha = .5)
reg2 = linear_model.Ridge (alpha = .5)
reg3 = linear_model.Ridge (alpha = .5)
reg1.fit(smX, y1)
reg2.fit(smX, y2)
reg3.fit(smX, y3)
prey1 = reg1.predict(smX)
prey2 = reg2.predict(smX)
prey3 = reg3.predict(smX)
ys = [y1,y2,y3]
preys = [prey1, prey2, prey3]
titles = ["before", "after", "after-before"]
for i in range(3):
fig = plt.figure()
plt.plot(ys[i], preys[i], 'k.')
plt.xlabel("Experiment")
plt.ylabel("Prediction")
plt.title("Poly-A linear model: " + titles[i])
plt.xlim([-20,20])
plt.ylim([-20,20])
plt.show()
plt.close()
regs = [reg1, reg2, reg3]
lines = []
for i in range(len(regs)):
reg = regs[i]
line = reg.coef_
lines.append([line])
cmap = cm.seismic
cmap.set_bad('white',1.)
xticks = [range(0, ref_length, 16), [str(i) for i in range(-112, 113, 16)]]
vlines = [dyad_axis - 65, dyad_axis - 20, dyad_axis, dyad_axis + 20, dyad_axis + 65]
upticks = [ [dyad_axis - 65, dyad_axis - 20, dyad_axis, dyad_axis + 20, dyad_axis + 65], ['SHL6.5', 'SHL2', 'dyad', 'SHL2','SHL6.5'] ]
graph.plot_heatmap4(lines, dim = [3,1], cmap_list = [cmap]*3, vmin_list = [-10,-10,-5], vmax_list = [10,10,5], yticks_list = [[[],[]], [[],[]], [[],[]]], xticks_list = [[[],['']*len(xticks[1])],[[],['']*len(xticks[1])],xticks], vlines_list=[vlines]*3, upticks_list=[upticks,[[],[]], [[],[]]], ylabels = ["Before", "After", "After-Before"], aspect_list=[25]*3, note="MDP", xlabels=['','','Location w.r.t. Dyad (bp)'])
"""
# plot colorbar
#fig = plt.figure()
#ax1 = fig.add_subplot(111)
#ax1 = fig.add_axes([0.05, 0.80, 0.8, 0.15])
#cmap = cm.seismic
#norm = mpl.colors.Normalize(vmin=-200, vmax=200)
#norm = mpl.colors.Normalize(vmin=-1, vmax=1)
#bounds = np.linspace(-1,1,5)
#bounds = [-1., -.5, 0., .5, 1.]
#cb1 = mpl.colorbar.ColorbarBase(ax1, cmap=cmap, norm=norm, orientation='horizontal', extend='both', ticks=bounds)
#cb1.set_label('Some Units')
#note = 'median_position'
#note = 'mean_force'
#plt.savefig('colorbar_' + note + '.png', bbox_inches='tight')
#plt.show()
#plt.close
"""
"""
#test = ["AAA-111", "AAA-91", "AAA-131",
# "AAAAA-110", "AAAAA-90", "AAAAA-130",
# "AAAAAAAAAA-107", "AAAAAAAAAA-87", "AAAAAAAAAA-127",
# "AAAAAAAAAAAAAAA-105", "AAAAAAAAAAAAAAA-87", "AAAAAAAAAAAAAAA-125",
# "AAAAAAAAAAAAAAAAAAAA-102", "AAAAAAAAAAAAAAAAAAAA-83", "AAAAAAAAAAAAAAAAAAAA-122"]
#sample_list = [test]
"""
"""
# select the subset of inserts
#sample_list = sample.sampling(key_slider, sample_mode)
#print sample_list
#sample_list1 = sample.sampling(key_slider1, sample_mode)
#sample_list2 = sample.sampling(key_slider2, sample_mode)
#sample_list = []
#assert len(sample_list1) == len(sample_list2)
#for i in range(len(sample_list1)):
# sample1 = sample_list1[i]
# sample2 = sample_list2[i]
# samplec = []
# for key in sample1:
# if key in sample2:
# samplec.append(key)
# sample_list.append(samplec)
#keys = list(set(key_slider1.keys()) & set(key_slider2.keys()))
#sample_list = [random.sample(keys, 30)]
#graph.plot_rmap(key_slider1, key_slider2, sample_list, norm_choice=False, note='_ratio_log', draw_key=True, draw_vert=False)
# plot cut/dyad map
#graph_edit.plot_map(key_slider1, sample_list, norm_choice=True, obs_func = Slider.energy_profile, draw = "key", slicing=80, note='_KDE_before')
#graph_edit.plot_map(key_slider2, sample_list, norm_choice=True, obs_func = Slider.energy_profile, draw = "key", slicing=80, note='_KDE_after')
#graph.plot_map(key_slider, sample_list, norm_choice=True, note='', draw_key=True, draw_vert=False)
#graph_edit.plot_map(key_slider, sample_list, norm_choice=True, obs_func = Slider.energy_profile, draw = "key", slicing=80, note='_KDE_before')
#graph.plot_map(key_slider_r, sample_list, norm_choice=True, note='norm_raw', draw_key=True, draw_vert=False)
#graph.plot_map(key_slider1, sample_list, norm_choice=True, note='_before', draw_key=True, draw_vert=False)
#graph.plot_map(key_slider2, sample_list, norm_choice=True, note='_After', draw_key=True, draw_vert=False)
# plot average cut/dyad signal
#graph_edit.plot_signal(key_slider, sample_list, norm_choice=False, obs_func = Slider.KDE, draw = "key")
#graph.plot_signal(key_slider, sample_list, note='_test')
#graph.plot_signal(key_slider, sample_list, show_key=True)
#graph.plot_signal(key_slider, note='test')
#graph.plot_signal(key_slider_r, note='all_raw')
#graph.plot_signal(key_slider1, note='before')
#graph.plot_signal(key_slider2, note='after')
# plot SeqID vs cut peaks
#graph.plot_cpeaks(key_slider, left_peak_num = 2, right_peak_num = 2)
# plot SeqID vs dyad peaks
#graph.plot_dpeaks(key_slider, peak_num = 3, st_rank = 1, sample_list=sample_list)
#graph.plot_dpeaks(key_slider, peak_num = 3, st_rank = 1, note='all')
# plot correlation
#graph.plot_corr2(key_slider2, Slider.Amer_len, Slider.mean_pos, xlabel='Poly-A length', ylabel='Mean position', sample_labels = ['Left','Right'], sample_list=sample_list)
#graph.plot_corr3(key_slider1, key_slider2, Slider.Amer_len, Slider.mean_pos, xlabel='Poly-A length', ylabel='Mean position', sample_labels = ['Left','Right'], sample_list=sample_list)
#graph.plot_corr(key_slider, Slider.Amer_len, Slider.mean_pos, xlabel='Poly-A length', ylabel='Mean position')
#graph.plot_corr(key_slider, Slider.Amer_len, Slider.max_dis, xlabel='Poly-A length', ylabel='Maximum displacement')
#graph.plot_energy(key_slider1, key_slider2)
"""
# Entropy difference
X, Y = [], []
Z = []
diff = []
diff2 = []
keys = list(set(key_slider1.keys()) & set(key_slider2.keys()))
for key in keys:
slider1 = key_slider1[key]
slider2 = key_slider2[key]
kde1 = slider1.KDE()
kde2 = slider2.KDE()
entropy1, entropy2 = 0.0, 0.0
entropy3 = 0.0
for i in range(len(kde1)):
entropy1 += -kde1[i]*np.log(kde1[i])
entropy2 += -kde2[i]*np.log(kde2[i])
entropy3 += -kde2[i]*np.log(kde1[i])
X.append(entropy1)
Y.append(entropy2)
Z.append(entropy3)
diff.append(entropy2 - entropy1)
diff2.append(entropy3 - entropy1)
diff = sorted(diff)
diff2 = sorted(diff2)
fig = plt.figure()
plt.plot(X,Y,'.')
plt.xlabel("Entropy (Before)")
plt.ylabel("Entropy (After)")
plt.xlim([4,5])
plt.ylim([4,5])
plt.plot([4,5],[4,5],'--')
plt.savefig("entropy.png")
plt.show()
plt.close()
fig = plt.figure()
plt.plot(range(len(diff)), diff, '.')
plt.xlabel("sequences")
plt.ylabel("Entropy change")
plt.savefig("entropy_change.png")
plt.show()
plt.close()
fig = plt.figure()
plt.plot(X,Z,'.')
plt.xlabel("Entropy (Before)")
plt.ylabel("Entropy (After)")
plt.xlim([4,5])
plt.ylim([4,5])
plt.plot([4,5],[4,5],'--')
plt.savefig("entropy_gen.png")
plt.show()
plt.close()
fig = plt.figure()
plt.plot(range(len(diff2)), diff2, '.')
plt.xlabel("sequences")
plt.ylabel("Entropy change")
plt.savefig("entropy_change_gen.png")
plt.show()
plt.close()
"""
if __name__ == '__main__':
parser = ArgumentParser(description='graphical analysis of slide-seq')
parser.add_argument(metavar='-f1',
dest="filenames1",
type=str,
nargs='+',
help='sort filenames for one condition')
parser.add_argument('-f2',
dest="filenames2",
type=str,
nargs='+',
help='sort filenames for another condition')
parser.add_argument('-f3',
dest="filenames3",
type=str,
nargs='+',
help='sort filenames for another condition')
parser.add_argument('-x',
dest="ref_fname",
type=str,
help='ref filename')
parser.add_argument('--ref-length',
dest="ref_length",
default=225,
type=int,
help='length of reference template')
parser.add_argument('--dyad-offset',
dest="dyad_offset",
default=52,
type=int,
help='off-set length from cut site to dyad position')
parser.add_argument('--stat',
dest="stat_mode",
default=False,
type=bool,
help='stat mode')
parser.add_argument('--graph-mode',
dest="graph_mode",
default=1,
type=int,
help='graph mode')
parser.add_argument('-n',
dest="norm_choice",
default=False,
type=bool,
help='normalize intesnity for each sequence')
parser.add_argument('--sample',
dest="sample_mode",
default='r',
type=str,
help='sampling mode \nRandom r:num \nUserInput k:key1,key2.. \nReadcounts counts:order/div_num/num \nGCcontents GC:order/div_num/num PolyAlen polyA:order/div_num/num \nMeanpos mpos:order/div_num/num \nMaxDis mdis:order/div_num/num \nBinEnrich bin#:order/div_num/num \nCluster cluster:obs/div_num/num'
)
args = parser.parse_args()
if not args.ref_fname:
ref_fname = False
else:
ref_fname = args.ref_fname
display_graph(args.filenames1,
args.filenames2,
args.filenames3,
ref_fname,
args.ref_length,
args.dyad_offset,
args.stat_mode,
args.graph_mode,
args.norm_choice,
args.sample_mode)