forked from Xinglab/rMATS-STAT
-
Notifications
You must be signed in to change notification settings - Fork 0
/
MATS_LRT.py
357 lines (319 loc) · 16.3 KB
/
MATS_LRT.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
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
#This script generates the WinBug input files by Simulated RNA-Seq data
import re,os,sys,warnings,numpy,scipy,math,itertools;
from scipy import stats;
from numpy import *;
from multiprocessing import Pool;
from scipy.optimize import fmin_cobyla
from scipy.optimize import fmin_l_bfgs_b
from math import log;
numpy.random.seed(1231);
warnings.filterwarnings('ignore');
#ReadLength
#Dummy length here. Adapt to the new rMATS structure
read_length=50;
#JunctionLength
#Dummy length here. Adapt to the new rMATS structure
junction_length=84;
#Output folder
if len(sys.argv)<3:
print("Error: Less than two input parameters.");
else:
output_folder=sys.argv[2];
#MultiProcessor
MultiProcessor=1;
if len(sys.argv)>=4:
MultiProcessor=int(sys.argv[3]);
#splicing difference cutoff
cutoff=0.1;
if len(sys.argv)>=5:
cutoff=float(sys.argv[4]);
rho=0.9
#binomial MLE optimization functions
def logit(x):
if x<0.01:
x=0.01;
if x>0.99:
x=0.99;
return(log(x/(1-x)));
def logit_list(x_list):
res=[];
for x in x_list:
if x<0.01:
x=0.01;
if x>0.99:
x=0.99;
res.append(log(x/(1-x)));
return(res);
#Not use multivar in the MATS LRT
def myfunc_multivar(x,*args):
psi1=args[0];psi2=args[1];var1=args[2];var2=args[3];
sum1=0;sum2=0;
for i in range(len(psi1)):
sum1+=pow(logit((psi1[i]-psi2[i])/2+0.5)-logit((x[0]-x[1])/2+0.5),2)
sum1=sum1/var2/2;
return(sum1+0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(pow(stats.norm.ppf(x[0]),2)+pow(stats.norm.ppf(x[1]),2)-2*rho*stats.norm.ppf(x[0])*stats.norm.ppf(x[1])));
#return(sum1);
#Not use multivar in the MATS LRT
def myfunc_multivar_der(x,*args):
psi1=args[0];psi2=args[1];var1=args[2];var2=args[3];
sum1=0;sum2=0;
for i in range(len(psi1)):
sum1+=-2*(logit((psi1[i]-psi2[i])/2+0.5)-logit((x[0]-x[1])/2+0.5))/((x[0]-x[1])/2+0.5)/(0.5-(x[0]-x[1])/2)*0.5;
sum1=sum1/var1/2;
res1=sum1+0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x[0])-2*rho*stats.norm.ppf(x[1]))/stats.norm.pdf(stats.norm.ppf(x[0]));
for i in range(len(psi2)):
sum2+=-2*(logit((psi1[i]-psi2[i])/2+0.5)-logit((x[0]-x[1])/2+0.5))/((x[0]-x[1])/2+0.5)/(0.5-(x[0]-x[1])/2)*(-0.5);
sum2=sum2/var2/2;
res2=sum2+0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x[1])-2*rho*stats.norm.ppf(x[0]))/stats.norm.pdf(stats.norm.ppf(x[1]));
return(numpy.array([res1,res2]));
#note to me: change this in unpaired rMATS
def myfunc_1(x,*args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];effective_inclusion_length=args[4];effective_skipping_length=args[5];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
new_psi1=inclusion_length*(x+cutoff)/(inclusion_length*(x+cutoff)+skipping_length*(1-(x+cutoff)));new_psi2=inclusion_length*x/(inclusion_length*x+skipping_length*(1-x));
binomial_sum=-1*(I1*log(new_psi1)+S1*log(1-new_psi1)+I2*log(new_psi2)+S2*log(1-new_psi2));
multivar_sum=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(pow(stats.norm.ppf(x+cutoff),2)+pow(stats.norm.ppf(x),2)-2*rho*stats.norm.ppf(x+cutoff)*stats.norm.ppf(x))
return(binomial_sum+multivar_sum);
def myfunc_der_1(x,*args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];effective_inclusion_length=args[4];effective_skipping_length=args[5];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
new_psi1=inclusion_length*(x+cutoff)/(inclusion_length*(x+cutoff)+skipping_length*(1-(x+cutoff)));new_psi2=inclusion_length*x/(inclusion_length*x+skipping_length*(1-x));
new_psi1_der=inclusion_length*skipping_length/pow(inclusion_length*(x+cutoff)+skipping_length*(1-(x+cutoff)),2);
new_psi2_der=inclusion_length*skipping_length/pow(inclusion_length*x+skipping_length*(1-x),2);
res1=-1*(I1/new_psi1-S1/(1-new_psi1))*new_psi1_der;
res1+=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x+cutoff)-2*rho*stats.norm.ppf(x))/stats.norm.pdf(stats.norm.ppf(x+cutoff))
res2=-1*(I2/new_psi2-S2/(1-new_psi2))*new_psi2_der;
res2+=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x)-2*rho*stats.norm.ppf(x+cutoff))/stats.norm.pdf(stats.norm.ppf(x));
return(numpy.array(res1+res2));
def myfunc_2(x, *args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];effective_inclusion_length=args[4];effective_skipping_length=args[5];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
new_psi1=inclusion_length*(x)/(inclusion_length*(x)+skipping_length*(1-(x)));new_psi2=inclusion_length*(x+cutoff)/(inclusion_length*(x+cutoff)+skipping_length*(1-(x+cutoff)));
binomial_sum=-1*(I1*log(new_psi1)+S1*log(1-new_psi1)+I2*log(new_psi2)+S2*log(1-new_psi2));
multivar_sum=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(pow(stats.norm.ppf(x),2)+pow(stats.norm.ppf(x+cutoff),2)-2*rho*stats.norm.ppf(x)*stats.norm.ppf(x+cutoff))
return(binomial_sum+multivar_sum);
def myfunc_der_2(x,*args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];effective_inclusion_length=args[4];effective_skipping_length=args[5];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
new_psi1=inclusion_length*x/(inclusion_length*x+skipping_length*(1-x));new_psi2=inclusion_length*(x+cutoff)/(inclusion_length*(x+cutoff)+skipping_length*(1-(x+cutoff)));
new_psi1_der=inclusion_length*skipping_length/pow(inclusion_length*x+skipping_length*(1-x),2);
new_psi2_der=inclusion_length*skipping_length/pow(inclusion_length*(x+cutoff)+skipping_length*(1-(x+cutoff)),2);
res1=-1*(I1/new_psi1-S1/(1-new_psi1))*new_psi1_der;
res1+=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x)-2*rho*stats.norm.ppf((x+cutoff)))/stats.norm.pdf(stats.norm.ppf(x))
res2=-1*(I2/new_psi2-S2/(1-new_psi2))*new_psi2_der;
res2+=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf((x+cutoff))-2*rho*stats.norm.ppf(x))/stats.norm.pdf(stats.norm.ppf((x+cutoff)));
return(numpy.array(res1+res2));
def myfunc_individual(x,*args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];effective_inclusion_length=args[4];effective_skipping_length=args[5];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
new_psi1=inclusion_length*x[0]/(inclusion_length*x[0]+skipping_length*(1-x[0]));new_psi2=inclusion_length*x[1]/(inclusion_length*x[1]+skipping_length*(1-x[1]));
binomial_sum=-1*(I1*log(new_psi1)+S1*log(1-new_psi1)+I2*log(new_psi2)+S2*log(1-new_psi2));
multivar_sum=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(pow(stats.norm.ppf(x[0]),2)+pow(stats.norm.ppf(x[1]),2)-2*rho*stats.norm.ppf(x[0])*stats.norm.ppf(x[1]))
return(binomial_sum+multivar_sum);
def myfunc_individual_der(x,*args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];effective_inclusion_length=args[4];effective_skipping_length=args[5];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
new_psi1=inclusion_length*x[0]/(inclusion_length*x[0]+skipping_length*(1-x[0]));new_psi2=inclusion_length*x[1]/(inclusion_length*x[1]+skipping_length*(1-x[1]));
new_psi1_der=inclusion_length*skipping_length/pow(inclusion_length*x[0]+skipping_length*(1-x[0]),2);
new_psi2_der=inclusion_length*skipping_length/pow(inclusion_length*x[1]+skipping_length*(1-x[1]),2);
res1=-1*(I1/new_psi1-S1/(1-new_psi1))*new_psi1_der;
res1+=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x[0])-2*rho*stats.norm.ppf(x[1]))/stats.norm.pdf(stats.norm.ppf(x[0]))
res2=-1*(I2/new_psi2-S2/(1-new_psi2))*new_psi2_der;
res2+=0.1*0.5*(pow(rho,2))/(1-pow(rho,2))*(2*stats.norm.ppf(x[1])-2*rho*stats.norm.ppf(x[0]))/stats.norm.pdf(stats.norm.ppf(x[1]));
return(numpy.array([res1,res2]));
def myfunc_likelihood(x, args):
I1=args[0][0];I2=args[0][1];S1=args[1][0];S2=args[1][1];beta1=args[2][0];beta2=args[2][1];var=args[3];
sum=0;N1=I1+S1;N2=I2+S2;
if (N1+N2)==0:
return(0);
sum+=-0.5*((I1-N1*x[0])*(I1-N1*x[0])/(N1*x[0])+(S1-N1*(1-x[0]))*(S1-N1*(1-x[0]))/(N1*(1-x[0])));
sum+=-0.5*((I2-N2*x[1])*(I2-N2*x[1])/(N2*x[1])+(S2-N2*(1-x[1]))*(S2-N2*(1-x[1]))/(N2*(1-x[1])));
sum+=pow(logit(beta1)-logit(beta2)-logit(x[0])+logit(x[1]),2);
return(sum);
def MLE_iteration_constrain(i1,i2,s1,s2,effective_inclusion_length,effective_skipping_length):
psi1=vec2psi(i1,s1,effective_inclusion_length,effective_skipping_length);psi2=vec2psi(i2,s2,effective_inclusion_length,effective_skipping_length);
iter_cutoff=1;iter_maxrun=100;count=0;previous_sum=0;
while((iter_cutoff>0.01)&(count<=iter_maxrun)):
count+=1;
#iteration of beta
beta_0=sum(psi1)/len(psi1);
beta_1=sum(psi2)/len(psi2);
var1=0;var2=0;
current_sum=0;likelihood_sum=0;
new_psi1=[];new_psi2=[];
if (sum(psi1)/len(psi1))>(sum(psi2)/len(psi2)):#minize psi2 if this is the case
xopt = fmin_l_bfgs_b(myfunc_1,[sum(psi2)/len(psi2)],myfunc_der_1,args=[[i1[0],i2[0]],[s1[0],s2[0]],[beta_0,beta_1],var1,effective_inclusion_length,effective_skipping_length],bounds=[[0.001,0.999-cutoff]],iprint=-1)
theta2 = max(min(float(xopt[0]),1-cutoff),0);theta1=theta2+cutoff;
else:#minize psi1 if this is the case
xopt = fmin_l_bfgs_b(myfunc_2,[sum(psi1)/len(psi1)],myfunc_der_2,args=[[i1[0],i2[0]],[s1[0],s2[0]],[beta_0,beta_1],var1,effective_inclusion_length,effective_skipping_length],bounds=[[0.001,0.999-cutoff]],iprint=-1)
theta1 = max(min(float(xopt[0]),1-cutoff),0);theta2=theta1+cutoff;
#Debug;print('constrain_1xopt');print('theta');print(theta1);print(theta2);print(xopt);
current_sum+=float(xopt[1]);
new_psi1.append(theta1);new_psi2.append(theta2);
psi1=new_psi1;psi2=new_psi2;
if count>1:
iter_cutoff=abs(previous_sum-current_sum)/abs(previous_sum);
previous_sum=current_sum;
#Debug;print('constrain');print(theta1);print(theta2);print(psi1);print(psi2);print(current_sum);print(likelihood_sum);
print('constrain');print(xopt);print(theta1);print(theta2);
return([current_sum,[psi1,psi2,beta_0,beta_1,var1,var2]]);
def MLE_iteration(i1,i2,s1,s2,effective_inclusion_length,effective_skipping_length):
psi1=vec2psi(i1,s1,effective_inclusion_length,effective_skipping_length);psi2=vec2psi(i2,s2,effective_inclusion_length,effective_skipping_length);
iter_cutoff=1;iter_maxrun=100;count=0;previous_sum=0;
while((iter_cutoff>0.01)&(count<=iter_maxrun)):
count+=1;
#iteration of beta
beta_0=sum(psi1)/len(psi1);
beta_1=sum(psi2)/len(psi2);
var1=0;var2=0;
current_sum=0;likelihood_sum=0;
new_psi1=[];new_psi2=[];
#Debug;print('unconstrain_1xopt');
for i in range(len(psi1)):
xopt = fmin_l_bfgs_b(myfunc_individual,[psi1[i],psi2[i]],myfunc_individual_der,args=[[i1[i],i2[i]],[s1[i],s2[i]],[beta_0,beta_1],var1,effective_inclusion_length,effective_skipping_length],bounds=[[0.01,0.99],[0.01,0.99]],iprint=-1);
new_psi1.append(float(xopt[0][0]));current_sum+=float(xopt[1]);
new_psi2.append(float(xopt[0][1]));
#Debug;print(xopt);
likelihood_sum+=myfunc_likelihood([new_psi1[i],new_psi2[i]],[[i1[i],i2[i]],[s1[i],s2[i]],[beta_0,beta_1],var1]);
psi1=new_psi1;psi2=new_psi2;
#Debug;print('count');print(count);print('previous_sum');print(previous_sum);print('current_sum');print(current_sum);
if count>1:
iter_cutoff=abs(previous_sum-current_sum)/abs(previous_sum);
previous_sum=current_sum;
if count>iter_maxrun:
return([current_sum,[psi1,psi2,0,0,var1,var2]]);
print('unconstrain');print(xopt);
return([current_sum,[psi1,psi2,beta_0,beta_1,var1,var2]]);
#Random Sampling Function
def likelihood_test(i1,i2,s1,s2,effective_inclusion_length,effective_skipping_length,flag):
if flag==0:
return(1);
else:
res=MLE_iteration(i1,i2,s1,s2,effective_inclusion_length,effective_skipping_length);
if abs(res[1][2]-res[1][3])<=cutoff:
#Debug;print('1<=cutoff');print(res);print((res[1][2]-res[1][3]));
return(1);
else:
res_constrain=MLE_iteration_constrain(i1,i2,s1,s2,effective_inclusion_length,effective_skipping_length);
#Debug;print('2>cutoff');print('res');print(res);print('res_constrain');print(res_constrain);
#Debug;print(abs(res_constrain[0]-res[0]));print('2end');
return(1-scipy.stats.chi2.cdf(2*(abs(res_constrain[0]-res[0])),1));
#MultiProcessorFunction
def MultiProcessorPool(n_original_diff):
i1=n_original_diff[0];i2=n_original_diff[1];s1=n_original_diff[2];s2=n_original_diff[3];effective_inclusion_length=n_original_diff[4];effective_skipping_length=n_original_diff[5];flag=n_original_diff[6];
P=likelihood_test(i1,i2,s1,s2,effective_inclusion_length,effective_skipping_length,flag);
return(P);
#Function for vector handling
def vec2float(vec):
res=[];
for i in vec:
res.append(float(i));
return(res);
def vec2sum(vec):
res=0;
for i in vec:
res+=float(i);
return([res]);
def vecprod(vec):
res=1;
for i in vec:
res=res*i;
return(res);
def vec2remove0psi(inc,skp):
res1=[];res2=[];
for i in range(len(inc)):
if (inc[i]!=0) | (skp[i]!=0):
res1.append(inc[i]);res2.append(skp[i]);
return([res1,res2]);
def vec2psi(inc,skp,effective_inclusion_length,effective_skipping_length):
psi=[];
inclusion_length=effective_inclusion_length;
skipping_length=effective_skipping_length;
for i in range(len(inc)):
if (float(inc[i])+float(skp[i]))==0:
psi.append(0.5);
else:
psi.append(float(inc[i])/inclusion_length/(float(inc[i])/inclusion_length+float(skp[i])/skipping_length));
return(psi);
def vec210(vec):
res=[];
for i in vec:
if i>0:
res.append(1);
else:
res.append(-1);
return(res);
def myttest(vec1,vec2):
if (len(vec1)==1) & (len(vec2)==1):
res=stats.ttest_ind([vec1[0],vec1[0]],[vec2[0],vec2[0]]);
else:
res=stats.ttest_ind(vec1,vec2);
return(res);
ifile=open(sys.argv[1]);
title=ifile.readline();
#analyze the title of the inputed data file to find the information of how much simulation are involved
#the min simulated round is 10, each time it increases by 10 times
element=re.findall('[^ \t\n]+',title);
ofile=open(output_folder+'/rMATS_Result_P.txt','w');
ofile.write(title[:-1]+'\tPValue'+'\n');
list_n_original_diff=[];probability=[];psi_list_1=[];psi_list_2=[];rho_list=[];psi1_for_rho_list=[];psi2_for_rho_list=[];
ilines=ifile.readlines();
for i in range(len(ilines)):
element=re.findall('[^ \t\n]+',ilines[i]);
inc1=re.findall('[^,]+',element[1]);skp1=re.findall('[^,]+',element[2]);inc2=re.findall('[^,]+',element[3]);skp2=re.findall('[^,]+',element[4]);
#Dummy effective_inclusion_length and flanking exon length here. Adapt to the new rMATS structure
effective_inclusion_length=int(element[5]);
effective_skipping_length=int(element[6]);
#inc1=vec2float(inc1);skp1=vec2float(skp1);inc2=vec2float(inc2);skp2=vec2float(skp2);
inc1=vec2sum(inc1);skp1=vec2sum(skp1);inc2=vec2sum(inc2);skp2=vec2sum(skp2);
if ((vecprod(inc1)+vecprod(skp1))==0) | ((vecprod(inc2)+vecprod(skp2))==0):
list_n_original_diff.append([inc1,inc2,skp1,skp2,effective_inclusion_length,effective_skipping_length,0]);
else:
psi1=vec2psi(inc1,skp1,effective_inclusion_length,effective_skipping_length);psi2=vec2psi(inc2,skp2,effective_inclusion_length,effective_skipping_length);
for i in range(len(psi1)):
if len(psi1_for_rho_list)<=i:
psi1_for_rho_list.append([]);
psi1_for_rho_list[i].append(psi1[i]);
for i in range(len(psi2)):
if len(psi2_for_rho_list)<=i:
psi2_for_rho_list.append([]);
psi2_for_rho_list[i].append(psi2[i]);
psi_list_1.append(sum(inc1)/(sum(inc1)+sum(skp1)));
psi_list_2.append(sum(inc2)/(sum(inc2)+sum(skp2)));
#temp1=vec2remove0psi(inc1,skp1);temp2=vec2remove0psi(inc2,skp2);
#inc1=temp1[0];skp1=temp1[1];inc2=temp2[0];skp2=temp2[1];
list_n_original_diff.append([inc1,inc2,skp1,skp2,effective_inclusion_length,effective_skipping_length,1]);
#if i>2:
# break;
#rho_list for paired data
for i in range(len(psi1_for_rho_list)):
this_rho=stats.pearsonr(numpy.array(psi1_for_rho_list[i]),numpy.array(psi2_for_rho_list[i]));this_rho=this_rho[0];
if this_rho>0.9:
this_rho=0.9;
rho_list.append(this_rho);
rho=stats.pearsonr(numpy.array(psi_list_1),numpy.array(psi_list_2));rho=rho[0];
if rho>0.9:
rho=0.9;
#rho_list=[0.95,0.95,0.95,0.95];
#rho_list=[0,0,0,0];
rho=0.9;
#print('rho');print(rho);
if MultiProcessor>1:
pool=Pool(processes=MultiProcessor);
probability=pool.map(MultiProcessorPool,list_n_original_diff);
else:
for i in range(len(list_n_original_diff)):
#print(list_n_original_diff[i]);
probability.append(MultiProcessorPool(list_n_original_diff[i]));
#print(probability);
index=0;
for i in range(len(ilines)):
element=re.findall('[^ \t\n]+',ilines[i]);
ofile.write(ilines[i][:-1]+'\t'+str(probability[i])+'\n');
ofile.close();