-
Notifications
You must be signed in to change notification settings - Fork 3
/
correlation_MSD_3C.py
187 lines (148 loc) · 6.08 KB
/
correlation_MSD_3C.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 26 12:08:22 2016
@author: Axel KournaK
To confront measures of mobilities of Marco or Espeli with cumulative contacts signals.
"""
import numpy as np
import scipy
import domainogram_diag2
import directional_indice
import scn
import ice_mirny3
import comp_short
from pylab import *
import gc_content
import matplotlib.gridspec as gridspec
import pandas as pd
# Extraction from Marco data:
dm=loadtxt("/run/media/axel/RSG3/ECOLI_PROJECT_IN_PROGRESS/data_marco/fig3_MSD_time_condition.txt")
#dm=loadtxt("/home/axel/Bureau/fig3_MSD_time_condition_resent.txt")
#dm[np.isnan(dm)] = 0.0
dm0=dm[0,]
dm2=np.reshape(dm0, (10,27))
# Computation of 3C cumulative signal:
data1=loadtxt("/run/media/axel/RSG3/ECOLI_PROJECT_IN_PROGRESS/matrices_NextSeqs_june_October2015/mat_temp_5000_fused4banks_30C_MM_ppmm.dat");
#data1=loadtxt("run/media/axel/RSG3/ECOLI_PROJECT_IN_PROGRESS/matrices_NextSeqs_june_October2015/mat_temp_1000_fused4banks_30C_MM.dat");
times_laps=loadtxt("/run/media/axel/RSG3/ECOLI_PROJECT_IN_PROGRESS/data_marco/fig3_time_lags.txt")
BIN=5000;
matscn1=ice_mirny3.ice_func(data1,100);
matscn1= scn.scn_func(matscn1,0);
n1 = matscn1.shape[0];
ii=0;
scales=range(0,400);
comp_scale1 = np.zeros( (len(scales), n1) );
for nw in scales :
print(nw);
sc = nw*BIN
c=comp_short.comp_func(matscn1,nw);
comp_scale1[ii,] = c.T;
ii=ii+1
imshow(comp_scale1,interpolation="none")
# MSD measures:
msd=loadtxt('/run/media/axel/RSG3/ECOLI_PROJECT_IN_PROGRESS/data_marco/fig1b_MSD_10s.txt')
df=pd.read_table('/run/media/axel/RSG3/ECOLI_PROJECT_IN_PROGRESS/data_marco/names_positions_probes.txt',header=None)
v=np.array(df[1]);
# look for the points in the scalogram cumulative:
scale_chosen=200000;
ii=0;
#comp_local = zeros((msd.shape[0],0));
comp_local = [];
indices = [];
names=[];
for i in msd[:,0] *1000/5 :
ind = int(i+0.5);
comp_local.append(comp_scale1[scale_chosen/BIN,ind]);
indices.append(ind);
#names.append();
ii=ii+1;
# Correlation between both signals:
c = scipy.stats.spearmanr(comp_local,msd[:,1]);
#plot(comp_local,msd[:,1],'o');
plot(comp_local,msd[:,1],'o', markersize=8.0);
#ylabel("MSD with tau = 10 sec",fontsize=16);
#xlabel("Cumulative 3C Contacts signal below 200kb",fontsize=16);
text(0.6,0.014,"Spearman Coef = -0.85");
text(0.6,0.012,"p-value = 1.77e-08");
# Linear regression:
x = comp_local;
y = msd[:,1];
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y);
x=np.array(x);
plot(x, x*slope + intercept, 'r',linewidth = 4.0);
xticks(fontsize = 16);
yticks(fontsize = 16);
na=["Ter1","NSR2","NSR1","NSR4","OriC","Ori1","Ter6","Ori2","Ori3","Ori4","NSR3","NSR5","Right1","Right2","Right4","Ter2","Ter5","Ter7","Left1","NSL3","NSL2","NSL1","Left3","Ter8","Left2","Ter3","Ter4"]
for i in range(0,len(comp_local) ) :
text(comp_local[i]+0.007,msd[i,1], na[i],fontsize = 14)
#-----------------------------------------------------------------------------------------
# Computation of matrices of scales
# Spatial scales versus temporal scales
#scales = range(0,400);
#scales = (100,200,300)
#for i in range(321,322) :
# for j in range(6,10) :
#for i in (50,50) :
# for j in (8,8) :
MAT_SCALES = np.zeros( (len(scales) , 10) );
for i in scales:
for j in range(0,10):
scale_spatial = i;
scale_temporal = j;
comp_local_i = comp_scale1[scale_spatial, indices ];
#msd_j = msd[:,1]
msd_j = msd[ scale_temporal , range(1,msd.shape[1]) ] * 4 * msd[ scale_temporal ,0 ];
z_comp_local_i = scipy.stats.mstats.zscore(comp_local_i);
z_msd_j = scipy.stats.mstats.zscore( msd_j);
x= comp_scale1[scale_spatial, sort(indices) ]
y= dm2[scale_temporal,:]
#plot(x, y,'o');
#xlabel("Local Compaction from 3C");
#ylabel("MSDXY / sum ");
c= scipy.stats.pearsonr( x, y );
#c= scipy.stats.spearmanr( x, y );
#c_rounded=c[0];
#c_rounded=round(c_rounded,2);
#print i,c[0],c[1];
#slope, intercept, r_value, p_value, std_err = stats.linregress(x,y);
#plot(x, x*slope + intercept, 'r',linewidth = 2.0);
#text(0.9*max(x), 0.9*max(y), "Spearman Coefficient = "+ str(c_rounded) );
#text(0.9*max(x), 0.8*max(y), "p-value = "+ str(c[1]) );
#print i,j,abs(c);
MAT_SCALES[i,j] = - c[0]
# if MAT_SCALES[i,j] > 0.75 :
# print i,j,MAT_SCALES[i,j];
imshow(MAT_SCALES,interpolation="none",extent= (0, 100, 400, 0))
imshow(MAT_SCALES[:,times_laps.argsort()],interpolation="none",extent= (0, 100, 400, 0))
ylabel("Spatial scales")
xlabel(r'Temporal scales $\tau$ in sec')
cbar = colorbar()
cbar.set_label('Spearman Correlation Coefficient', rotation=270, fontsize = 14)
plt.xticks(rotation=70)
# Plot with one temporal scale:
plot(MAT_SCALES[:,0],linewidth=3.0)
ylabel("Spearman Correlation Coefficient"+"\n" +"between MSD measured at 10.00 sec (Javer et al. data)" +"\n"+" and 3C cumulative signal (MM, 30($^\circ$C)",fontsize=14)
xlabel("Cumulative 3C contact signal below a specific spatial scale (in kb)",fontsize=14)
plt.axvline(40,linewidth=3.,color="red")
grid()
# PLOT of matrix of scales:
gs = gridspec.GridSpec(1,1);
ax1 = plt.subplot(gs[0]);
extent=(0,1100,0,400);
ax1.imshow(MAT_SCALES, interpolation='none', extent=extent);
#contourf(MAT_SCALES, interpolation='none', extent=extent);
im = imshow(MAT_SCALES, interpolation='none', extent=extent);
#imshow(MAT_SCALES, interpolation='none');
ax1.set_ylabel("Spatial scales used in 3C (in kb)");
ax1.set_xlabel("Temporal scales used in microscopy (in sec)")
tick_locs = list( np.array(range(0,msd.shape[0] ) ) *100 +50 ) ;
tick_lbls = msd[range(1,msd.shape[0] ),0];
ax1.set_xticks(tick_locs);
ax1.set_xticklabels(tick_lbls, fontdict=None, minor=False);
tick_locs = range(390,0,-20);
tick_locs = range(10,390,20);
tick_lbls = ((array(tick_locs))*5).tolist();
tick_locs = range(390,0,-20);
ax1.set_yticks(tick_locs);
ax1.set_yticklabels(tick_lbls, fontdict=None, minor=False);
plt.colorbar(im,shrink = 0.2,orientation="vertical");