├── .gitignore
├── D3EMergeOutput.py
├── README.md
├── D3EMakeContol.py
├── D3EAnalysis.py
├── D3ESplitData.py
├── D3EWeb.py
├── example.txt
├── D3ECmd.py
├── D3EUtil.py
└── LICENSE.md
/.gitignore:
--------------------------------------------------------------------------------
1 | *.pyc
2 | .DS_store
3 |
--------------------------------------------------------------------------------
/D3EMergeOutput.py:
--------------------------------------------------------------------------------
1 | '''
2 |
3 | D3E-Merge-Output
4 | Discrete Distributional Differential Expression Command Line Tool
5 |
6 | Author: Mihails Delmans (md656@cam.ac.uk)
7 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
8 | Version: 1.0
9 |
10 | Tested with:
11 | scipy 0.15.1
12 | numpy 1.8.0rc1
13 | sympy.mpmath 0.18
14 |
15 | '''
16 |
17 | import sys
18 | from os import listdir
19 |
20 | inDir = sys.argv[1]
21 | outFileName = sys.argv[2]
22 | label1 = sys.argv[3]
23 | label2 = sys.argv[4]
24 |
25 | outputFile = open(outFileName, 'w')
26 | hr = False
27 |
28 | for fileName in listdir(inDir):
29 | if fileName.startswith(label1+'_'+label2):
30 | inFile = open(inDir + fileName,'r')
31 | h = inFile.readline()
32 |
33 | if not hr:
34 | outputFile.write(h)
35 | hr = True
36 |
37 | inFile.readline()
38 |
39 | for line in inFile:
40 | if line.strip():
41 | outputFile.write(line)
42 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # D3E: Discrete Distributional Differential Expression
2 |
3 | ## About
4 | D3E is a tool for identifying differentially-expressed genes, based on single-cell RNA-seq data. D3E consists of two modules: one for identifying differentially expressed (DE) genes, and one for fitting the parameters of a Poisson-Beta distribution. A web-version which can be used for small samples can be accessed at http://www.sanger.ac.uk/sanger/GeneRegulation_D3E/
5 |
6 | ## Files
7 | - **D3EUtil.py** : a collection of methods for running the D3E analysis
8 | - **D3ECmd.py** : an actual D3E tool (requires **D3EUtil.py** for execution)
9 | - **D3EWed.py** : a light version of D3E, for running as a web-service (requires **D3EUtil.py** for execution)
10 | - **D3EMakeControl** : a script for making a control file for the analysis
11 | - **D3EAnalyse** : a script for filtering DE genes based on the result of control file analysis
12 |
13 | ## Getting Started
14 |
15 | Please visit our Wiki pages for User Guide and Example.
16 |
--------------------------------------------------------------------------------
/D3EMakeContol.py:
--------------------------------------------------------------------------------
1 | '''
2 |
3 | D3E-Cmd
4 | Discrete Distributional Differential Expression Command Line Tool
5 |
6 | Author: Mihails Delmans (md656@cam.ac.uk)
7 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
8 | Version: 1.0
9 |
10 | Tested with:
11 | scipy 0.15.1
12 | numpy 1.8.0rc1
13 | sympy.mpmath 0.18
14 |
15 | Copyright 2015 Mihails Delmans, Martin Hemberg
16 |
17 | This file is part of D3E.
18 |
19 | D3E is free software: you can redistribute it and/or modify
20 | it under the terms of the GNU General Public License as published by
21 | the Free Software Foundation, either version 3 of the License, or
22 | (at your option) any later version.
23 |
24 | D3E is distributed in the hope that it will be useful,
25 | but WITHOUT ANY WARRANTY; without even the implied warranty of
26 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
27 | GNU General Public License for more details.
28 |
29 | You should have received a copy of the GNU General Public License
30 | along with D3E. If not, see .
31 |
32 | '''
33 |
34 | import sys
35 | from D3EUtil import readData
36 |
37 | inFileName = sys.argv[1]
38 | outFileName = sys.argv[2]
39 | label = sys.argv[3]
40 |
41 | inFile = open(inFileName)
42 | outFileName = open(outFileName,'w')
43 |
44 | p1, p2, ids, lineStatus = readData(inFile, label, label, normalise=False, removeZeros=False, useSpikeIns = False, verbose = True)
45 |
46 | n = len(p1[0]) / 2
47 |
48 | outFileName.write('GeneID\t' + '\t'.join( [ label+'_1' ] * n + [label+'_2'] * (len(p1[0])-n) ) + '\n')
49 |
50 | for idx, p in zip(ids, p1):
51 | outFileName.write(idx + '\t' + '\t'.join( str(int(x)) for x in p ) + '\n')
52 |
--------------------------------------------------------------------------------
/D3EAnalysis.py:
--------------------------------------------------------------------------------
1 | '''
2 |
3 | D3E-Cmd
4 | Discrete Distributional Differential Expression Command Line Tool
5 |
6 | Author: Mihails Delmans (md656@cam.ac.uk)
7 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
8 | Version: 1.0
9 |
10 | Tested with:
11 | scipy 0.15.1
12 | numpy 1.8.0rc1
13 | sympy.mpmath 0.18
14 |
15 | Copyright 2015 Mihails Delmans, Martin Hemberg
16 |
17 | This file is part of D3E.
18 |
19 | D3E is free software: you can redistribute it and/or modify
20 | it under the terms of the GNU General Public License as published by
21 | the Free Software Foundation, either version 3 of the License, or
22 | (at your option) any later version.
23 |
24 | D3E is distributed in the hope that it will be useful,
25 | but WITHOUT ANY WARRANTY; without even the implied warranty of
26 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
27 | GNU General Public License for more details.
28 |
29 | You should have received a copy of the GNU General Public License
30 | along with D3E. If not, see .
31 |
32 | '''
33 |
34 | import sys
35 |
36 | tFileName = sys.argv[1]
37 | cFileName = sys.argv[2]
38 | oFileName = sys.argv[3]
39 | k = float(sys.argv[4])
40 |
41 |
42 | cFile = open(cFileName)
43 | cFile.readline()
44 | cFile.readline()
45 |
46 | pMin = float('Inf')
47 |
48 | for line in cFile:
49 | p = float(line.split()[21])
50 | if p < pMin:
51 | pMin = p
52 |
53 | tFile = open(tFileName)
54 | oFile = open(oFileName,'w')
55 |
56 | h = tFile.readline()
57 | tFile.readline()
58 |
59 | oFile.write(h + '\n')
60 |
61 | for line in tFile:
62 | tabs = line.split()
63 | p = float(tabs[21])
64 | if p < pMin * k:
65 | oFile.write(line)
66 |
--------------------------------------------------------------------------------
/D3ESplitData.py:
--------------------------------------------------------------------------------
1 | '''
2 | D3E-Split-Data
3 | Discrete Distributional Differential Expression Command Line Tool
4 |
5 | Author: Mihails Delmans (md656@cam.ac.uk)
6 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
7 | Version: 1.0
8 |
9 | Tested with:
10 | scipy 0.13.0b1
11 | numpy 1.8.0rc1
12 | sympy.mpmath 0.18
13 |
14 | Copyright 2015 Mihails Delmans, Martin Hemberg
15 |
16 | This file is part of D3E.
17 |
18 | D3E is free software: you can redistribute it and/or modify
19 | it under the terms of the GNU General Public License as published by
20 | the Free Software Foundation, either version 3 of the License, or
21 | (at your option) any later version.
22 |
23 | D3E is distributed in the hope that it will be useful,
24 | but WITHOUT ANY WARRANTY; without even the implied warranty of
25 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
26 | GNU General Public License for more details.
27 |
28 | You should have received a copy of the GNU General Public License
29 | along with D3E. If not, see .
30 |
31 | '''
32 |
33 | from D3EUtil import readData
34 | import sys
35 |
36 | inFileName = sys.argv[1]
37 | label1 = sys.argv[2]
38 | label2 =sys.argv[3]
39 | outDir = sys.argv[4]
40 | n = int(sys.argv[5])
41 |
42 | inFile = open(inFileName)
43 |
44 | data1, data2, ids, lineStatus = readData(inFile, label1, label2, normalise = True)
45 |
46 | i = 0
47 |
48 | for p1, p2, idx in zip(data1, data2, ids):
49 | if i % n == 0:
50 | ouputFile = open( outDir + label1 + '_' + label2 + '_' + str(i/n) + '.txt', 'w' )
51 | ouputFile.write('GeneID\t' + '\t'.join( [label1] * len(data1[0]) + [label2] * len(data2[0]) ) + '\n' )
52 | ouputFile.write('\t'.join ( [idx] + [str(x) for x in p1+p2] ) + '\n')
53 | i = i + 1
--------------------------------------------------------------------------------
/D3EWeb.py:
--------------------------------------------------------------------------------
1 | '''
2 | D3E-Web
3 | Discrete Distributional Differential Expression Web Tool
4 |
5 | Author: Mihails Delmans (md656@cam.ac.uk)
6 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
7 | Version: 1.0
8 |
9 | Tested with:
10 | scipy 0.15.1
11 | numpy 1.8.0rc1
12 | sympy.mpmath 0.18
13 |
14 | Copyright 2015 Mihails Delmans, Martin Hemberg
15 |
16 | This file is part of D3E.
17 |
18 | D3E is free software: you can redistribute it and/or modify
19 | it under the terms of the GNU General Public License as published by
20 | the Free Software Foundation, either version 3 of the License, or
21 | (at your option) any later version.
22 |
23 | D3E is distributed in the hope that it will be useful,
24 | but WITHOUT ANY WARRANTY; without even the implied warranty of
25 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
26 | GNU General Public License for more details.
27 |
28 | You should have received a copy of the GNU General Public License
29 | along with D3E. If not, see .
30 |
31 | '''
32 |
33 | from __future__ import division
34 | from D3EUtil import readData, getParamsMoments, cramerVonMises, logStatus, goodnessOfFit
35 | from D3EUtil import Params, BioParams, Status
36 |
37 | from numpy import log2, mean
38 | from scipy.stats import variation
39 |
40 | import StringIO
41 | import json
42 | import sys
43 | import fileinput
44 |
45 | def getBioParams(params):
46 | alpha = params.alpha
47 | beta = params.beta
48 | gamma = params.gamma
49 |
50 | return BioParams( gamma / beta, alpha, alpha * beta / (alpha + beta) )
51 |
52 | inputString = fileinput.input()[0].rstrip('\n')
53 |
54 | inputJSON = json.loads(inputString)
55 | inputFile = StringIO.StringIO(inputJSON['input']);
56 | label1 = inputJSON['label1']
57 | label2 = inputJSON['label2']
58 | normalise = int(inputJSON['normalise'])
59 | removeZeros = inputJSON['removeZeros']
60 | useSpikeIns = inputJSON['useSpikeIns']
61 |
62 | data1, data2, ids, lineStatus = readData(inputFile, label1, label2, normalise, removeZeros, useSpikeIns)
63 |
64 | errors = []
65 | rows = []
66 | cols = []
67 |
68 | for status in lineStatus:
69 | if status.code == 1:
70 | errors.append( {'geneId': status.idx, 'message': 'Warning: ' + status.message} )
71 | elif status.code == 2:
72 | errors.append( {'geneId': status.idx, 'message': 'Error: ' + status.message} )
73 |
74 |
75 | rowKeys = ["geneId"]
76 | rowKeys.extend( ["col" + str(i) for i in range(1,21)] )
77 |
78 | labels = ["Gene ID", "a1", "b1", "g1", "a2", "b2", "g2",
79 | "s1", "f1", "d1", "s2", "f2", "d2",
80 | "Rs", "Rf", "Rd", "mu1", "mu2", "cv1", "cv2", "p"]
81 |
82 | for key, label in zip(rowKeys, labels):
83 | cols.append( {"key" : key, "label" : label} )
84 |
85 |
86 |
87 | for p1,p2,idx in zip(data1, data2, ids):
88 |
89 | difference = cramerVonMises(p1,p2)
90 |
91 | if difference == -1:
92 | errors.append({"geneId" : idx, "type" : "Warning",
93 | "message" : "Could not estimate Cramer-von Mises statistic. Further analysis aborted." })
94 | continue
95 |
96 | params1 = getParamsMoments(p1)
97 | params2 = getParamsMoments(p2)
98 |
99 | if (params1.alpha <= 0 or params2.alpha <= 0 or params1.beta <= 0 or params2.beta <= 0 or
100 | params1.gamma <= 0 or params2.gamma <= 0):
101 | errors.append({"geneId" : idx, "type" : "Warning",
102 | "message" : "Could not estimate parameters for atleast one cell type." })
103 | else:
104 | bioParams1 = getBioParams(params1)
105 | bioParams2 = getBioParams(params2)
106 |
107 | rowString = ('{0:s}\t{1:2.2f}\t{2:2.2f}\t{3:2.2f}\t{4:2.2f}\t{5:2.2f}\t{6:2.2f}\t'
108 | '{7:2.2f}\t{8:2.2f}\t{9:2.2f}\t{10:2.2f}\t{11:2.2f}\t{12:2.2f}\t'
109 | '{13:2.2f}\t{14:2.2f}\t{15:2.2f}\t{16:2.2f}\t{17:2.2f}\t{18:2.2f}\t{19:2.2f}\t'
110 | '{20:2.2e}\n').format(idx,
111 | params1.alpha, params1.beta, params1.gamma,
112 | params2.alpha, params2.beta, params2.gamma,
113 | bioParams1.size, bioParams1.freq, bioParams1.duty,
114 | bioParams2.size, bioParams2.freq, bioParams2.duty,
115 | log2( bioParams1.size / bioParams2.size),
116 | log2( bioParams1.freq / bioParams2.freq),
117 | log2( bioParams1.duty / bioParams2.duty),
118 | mean(p1), mean(p2), variation(p1), variation(p2),
119 | difference)
120 | rowVals = rowString.split('\t')
121 |
122 | rows.append( dict( zip( rowKeys, rowVals ) ) )
123 |
124 |
125 | jsonOutput = { "rows" : rows, "columns" : cols, "errors" : errors }
126 | print json.dumps(jsonOutput)
127 |
128 |
--------------------------------------------------------------------------------
/example.txt:
--------------------------------------------------------------------------------
1 | GeneID G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G1 G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M G2M
2 | ENSMUSG00000000088 317 419 396 39 466 159 343 280 506 124 57 420 142 30 246 316 227 73 34 71 124 257 426 84 52 78 27 87 25 131 11 13 12 126 59 204 30 49 55 15 62 106 79 70 294 21 61 27 100 24 463 275 256 176 553 349 1044 295 334 488 700 652 350 188 220 350 360 1570 594 836 481 395 1479 165 158 190 251 186 149 157 229 517 376 349 143 157 252 47 168 99 166 101 93 560 300 132 346 403 125 136
3 | ENSMUSG00000000740 404 327 334 47 336 132 690 592 584 234 568 327 546 260 290 455 365 264 198 225 258 556 1097 265 205 272 120 163 183 338 75 92 143 324 262 813 266 48 273 48 436 342 351 125 330 136 94 72 442 98 920 577 508 155 1467 744 2097 1209 868 858 728 1033 445 454 418 421 790 2873 938 1408 867 180 2465 331 717 237 274 420 309 327 353 993 923 781 730 424 494 297 385 502 509 388 415 1317 836 661 498 1370 550 509
4 | ENSMUSG00000001403 113 198 208 55 704 342 852 1252 1268 580 448 858 1029 155 872 510 79 996 200 412 248 344 1113 168 285 420 349 11 980 804 1202 272 111 323 356 358 498 91 34 105 773 146 614 67 688 996 111 130 642 157 3985 1185 358 917 5374 604 4256 3613 548 1000 1989 1850 1577 2717 1353 1928 3115 4660 3517 4971 2688 1432 5116 527 3375 834 949 1443 1272 2525 1923 2376 1528 2259 1911 1603 1046 1114 1792 1668 1615 808 1212 6004 3741 889 4178 5500 1638 1587
5 | ENSMUSG00000002257 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 9 0 0 0 0 0 0 0 0 0 0 0 17 0 0 0 0 1 0 0 0 0 0 0 0 0 0 6 8 0 0 0 158 0 0 1 0 6 192 0 56 91 0 0 2 9 87 148 0 174 1 7 22 28 0 0 0 1 79 0 0 1 0 0 121 0 1 0 1 0 0 1 1 14 26 0 0 2 2 0
6 | ENSMUSG00000003779 678 133 1 208 692 0 1074 518 526 10 431 912 360 345 727 639 348 190 307 366 364 1047 152 715 423 491 631 163 15 320 23 133 110 182 10 748 97 0 431 0 526 471 14 2732 6 903 743 0 73 0 6 1432 409 725 1636 2835 2365 1167 740 934 1457 1418 678 607 618 21 791 25 1355 690 1149 5 1144 617 1429 1557 6 514 510 281 361 2038 655 975 611 635 763 244 162 451 1401 1027 280 690 1659 126 2763 1955 1650 1342
7 | ENSMUSG00000005397 2 2 1 98 178 46 122 45 5 768 12 1 530 132 612 9 70 386 8 1105 1 2831 461 17 32 267 231 307 12 45 308 1518 42 154 0 508 405 11 11 17 1 772 0 11 119 1112 224 147 1406 147 85 1436 768 327 960 4634 62 263 1191 797 2488 445 361 646 427 347 672 4953 534 2099 1800 669 2363 596 1062 533 653 597 1523 13 666 970 281 1093 1094 1518 870 776 1226 534 504 199 1258 640 798 809 2141 3072 209 1023
8 | ENSMUSG00000005410 722 16 1127 419 600 84 635 2228 478 660 943 2411 494 505 1497 1520 597 1504 881 579 1254 709 4582 846 1570 2047 344 9 7 443 2282 1303 29 95 1469 1087 185 1036 27 1107 532 3183 1974 879 85 907 487 1235 446 245 144 1944 2826 988 3294 0 3159 1048 2470 862 1155 1773 190 1099 694 375 1437 7611 2326 4615 2198 3442 933 1107 2180 2684 634 306 2145 455 4479 1327 1162 1085 164 2489 1665 906 759 445 2308 655 1020 5586 188 1976 2261 2437 2304 1693
9 | ENSMUSG00000006056 183 707 536 278 17 28 2 287 2788 909 254 391 602 1 877 864 180 406 715 508 456 343 1964 488 906 127 791 269 51 564 5 442 291 857 433 1041 32 65 564 11 1351 598 795 34 772 399 138 136 594 9 2831 467 1267 47 3198 74 4658 1039 612 2177 926 1672 1705 544 652 115 879 1863 1610 1418 2170 647 2037 327 2131 330 397 799 93 460 211 1050 1436 1549 2141 2355 1259 55 1351 480 1251 1757 272 1024 1531 1490 138 1574 711 665
10 | ENSMUSG00000006276 1 0 1 1 153 123 176 1 127 2 7 0 262 33 4 67 12 1 4 1 38 13 38 0 0 1 183 0 598 108 0 20 1 100 718 0 59 89 14 6 102 147 74 23 83 537 35 271 6 1 4 730 135 1 42 0 693 426 630 500 295 310 7 13 40 102 0 8 1227 614 2 104 448 136 1007 49 300 155 0 0 1516 2 822 230 907 22 263 40 10 28 433 148 289 1134 307 121 164 617 525 344
11 | ENSMUSG00000000001 43 82 5 0 2 87 49 855 45 137 733 1647 1036 1 715 643 1083 5 1250 36 541 608 114 153 38 524 177 1 502 501 0 6 836 471 435 696 0 36 169 42 0 214 1076 517 0 200 1 0 65 185 2815 562 4 529 2033 113 476 137 5 1110 848 107 15 366 269 25 175 4411 31 226 257 2 1283 417 121 15 326 124 141 0 64 238 130 268 604 624 594 1 93 7 448 32 814 547 656 17 589 573 525 568
12 | ENSMUSG00000000003 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
13 | ENSMUSG00000000028 89 598 10 249 135 36 72 6 482 374 782 669 241 703 359 595 12 1242 21 804 97 664 962 348 134 4 761 2 170 775 1013 115 7 43 5 1000 242 866 5 1414 50 702 991 3 39 978 886 256 2 500 1 3 427 252 1300 882 1218 480 908 903 522 139 287 324 665 21 49 0 917 479 80 19 533 204 433 257 1 58 726 3 906 965 299 291 703 97 289 273 865 270 4 810 194 1118 331 330 0 873 761 220
14 | ENSMUSG00000000031 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 34 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
15 | ENSMUSG00000000037 1 0 0 0 0 391 3 0 118 0 32 0 0 1 1 0 0 0 0 0 81 0 0 0 0 31 7 4 1 34 1 12 0 65 1 8 5 333 0 0 0 1 192 22 0 239 23 50 2 6 13 0 55 1 243 1 0 264 2 1 606 19 0 5 5 6 0 1 1 0 386 3 0 6 0 0 1 1 8 3 6 9 1049 1 27 53 4 14 110 3 0 0 279 3 98 9 1 1 1 449
16 | ENSMUSG00000000049 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
17 | ENSMUSG00000000056 0 19 0 15 0 10 5 4 0 6 2 1057 0 0 4 4 0 2 5 0 0 0 65 0 1 1 15 142 32 1 0 0 1 420 29 0 3 2 1 2 0 5 1 1 0 0 35 0 15 0 0 0 5 3 63 0 0 1 0 0 0 197 45 2 9 0 521 297 78 15 226 9 42 0 0 0 0 128 0 0 0 3 1120 0 100 997 0 0 0 0 16 0 0 0 3 3 0 0 3 0
18 | ENSMUSG00000000058 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
19 | ENSMUSG00000000078 3 514 1 154 8 65 283 318 58 39 3 0 1 63 169 4 1 1 18 8 43 514 11 1 109 17 1 325 11 9 92 23 39 19 1 27 7 14 653 33 2 352 1 12 304 795 6 139 13 107 9 125 58 3 52 9 1879 310 93 50 352 40 175 462 4 9 697 2275 902 6 21 39 110 63 195 5 4 8 41 7 4 2 252 326 295 383 11 174 13 59 27 5 1 574 840 244 7 561 124 2
20 | ENSMUSG00000000085 2 11 0 0 0 0 0 0 138 64 6 0 0 0 5 158 0 1 0 4 163 0 0 0 52 7 8 90 9 2 0 175 2 67 174 14 0 0 0 286 1 0 66 1352 0 0 0 2 0 45 26 472 45 0 8 0 378 11 352 0 0 14 43 0 0 1 0 3 59 14 4 0 2 178 1 0 0 0 0 0 7 6 617 2 0 132 0 0 0 0 0 3 0 0 55 248 0 35 0 143
21 |
--------------------------------------------------------------------------------
/D3ECmd.py:
--------------------------------------------------------------------------------
1 | '''
2 | D3E-Cmd
3 | Discrete Distributional Differential Expression Command Line Tool
4 |
5 | Author: Mihails Delmans (md656@cam.ac.uk)
6 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
7 | Version: 1.0
8 |
9 | Tested with:
10 | scipy 0.13.0b1
11 | numpy 1.8.0rc1
12 | sympy.mpmath 0.18
13 |
14 | Copyright 2015 Mihails Delmans, Martin Hemberg
15 |
16 | This file is part of D3E.
17 |
18 | D3E is free software: you can redistribute it and/or modify
19 | it under the terms of the GNU General Public License as published by
20 | the Free Software Foundation, either version 3 of the License, or
21 | (at your option) any later version.
22 |
23 | D3E is distributed in the hope that it will be useful,
24 | but WITHOUT ANY WARRANTY; without even the implied warranty of
25 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
26 | GNU General Public License for more details.
27 |
28 | You should have received a copy of the GNU General Public License
29 | along with D3E. If not, see .
30 |
31 | '''
32 | from __future__ import division
33 |
34 | from D3EUtil import readData, getParamsBayesian, getParamsMoments, cramerVonMises, logStatus, goodnessOfFit, distributionTest
35 | from D3EUtil import Params, BioParams, Status, likelihoodRatio
36 |
37 | from argparse import ArgumentParser, FileType
38 | from numpy import mean, log2
39 | from scipy.stats import variation
40 |
41 | def checkCramerVonMises(pValue, comment, idx):
42 | if pValue == -1:
43 | logStatus( Status(1, idx, "Could not estimate Cramer-von Mises statistic(" + comment + ")") )
44 |
45 | parser = ArgumentParser(description='3DExpress')
46 | parser.add_argument(action = 'store', type=FileType('r'), dest = 'inputFile', help='Input file...')
47 | parser.add_argument(action = 'store', type=FileType('w'), dest = 'outputFile', help='Output file...')
48 | parser.add_argument(action = 'store', type = str, nargs = 1, dest = 'label1', help = 'Label for the first cell type / condition')
49 | parser.add_argument(action = 'store', type = str, nargs = 1, dest = 'label2', help = 'Label for the second cell type / condition')
50 |
51 | parser.add_argument('-m', '--mode', action = 'store', type = int, dest='mode', default = 1, choices =[0,1], help = 'Mode of operation\n '\
52 | '0: Apply Method of moments\n'
53 | '1: Apply Bayesian approach (default)\n')
54 |
55 | parser.add_argument('-t', '--test', action = 'store', type = int, dest = 'test', default = 0, choices = [0,1,2,3], help = 'Statistical test\n '\
56 | '0: Cramer-von Mises test (default)\n'
57 | '1: Kolmogorov-Smirnov test\n'
58 | '2: Anderson-Darling test\n'
59 | '3: Likelihood Ratio test\n')
60 | parser.add_argument('-n','--normalise', action = 'store', type = int, dest='normalise', default = 1, choices = [0,1], help='Normalise the data')
61 | parser.add_argument('-z', '--removeZeros', action = 'store', type = int, dest='removeZeros', default = 0, choices = [0,1], help='Remove zeros')
62 | parser.add_argument('-s', '--useSpikeIns', action = 'store', type = int, dest='useSpikeIns', default = 0, choices = [0,1], help='Use spike-ins for normalisation. Requires at least one row with id starting with "spike" or the string set with --spikeInStart ')
63 | parser.add_argument('--spikeInStart', action = 'store', type = str, nargs = 1, dest = 'spikeInStart', default = 'spike', help = 'A string to identify spike-ins. (spike)', metavar = 'spike')
64 | parser.add_argument('-v', action = 'store_const', const = True, dest = 'verbose', default = True, help = 'verbose')
65 | parser.add_argument('-f', '--fdr', action = 'store', type = float, dest = 'fdr', default = 1, help='FDR parameter')
66 | args = parser.parse_args()
67 |
68 | data1, data2, ids, lineStatus = readData(args.inputFile, args.label1[0], args.label2[0], args.normalise, args.removeZeros, args.useSpikeIns, args.spikeInStart)
69 |
70 | if args.verbose:
71 | for status in lineStatus:
72 | logStatus(status)
73 |
74 |
75 | if args.mode == 0:
76 | args.outputFile.write('#GeneID\ta1\tb1\tg1\tGOF1\ta2\tb2\tg2\tGOF2\ts1\tf1\td1\ts2\tf2\td2\tRs\tRf\tRd\tp-value\tmu1\tcv1\tmu2\tcv2\n\n')
77 | elif args.mode == 1:
78 | args.outputFile.write('#GeneID\ta1\tb1\tg1\tGOF1\ta2\tb2\tg2\tGOF2\ts1\tf1\td1\ts2\tf2\td2\tRs\tRf\tRd\tpSize\tpFreq\tpDuty\tp-value\tmu1\tcv1\tmu2\tcv2\n\n')
79 |
80 | pVals = []
81 |
82 | for p1,p2,idx in zip(data1, data2, ids):
83 |
84 | difference = distributionTest(p1,p2, args.test)
85 |
86 | if difference == -1:
87 | logStatus( Status(1, idx, "Could not estimate p-value. Further analysis aborted.") )
88 | continue
89 |
90 | if args.mode == 0:
91 | params1 = getParamsMoments(p1)
92 | params2 = getParamsMoments(p2)
93 |
94 | if (params1.alpha <=0 or params1.beta <= 0 or params1.gamma <=0 or
95 | params2.alpha <=0 or params2.beta <= 0 or params2.gamma <=0):
96 | logStatus( Status(1, idx, "Could not estimate parameters.") )
97 |
98 | params1 = Params(alpha = float('nan'), beta = float('nan'), gamma = float('nan'), c = float('nan') )
99 | params2 = Params(alpha = float('nan'), beta = float('nan'), gamma = float('nan'), c = float('nan') )
100 | bioParams1 = BioParams( size = float('nan'), freq = float('nan'), duty = float('nan') )
101 | bioParams2 = BioParams( size = float('nan'), freq = float('nan'), duty = float('nan') )
102 | else:
103 | bioParams1 = BioParams(size = params1.gamma / params1.beta, freq = 1 / params1.alpha, duty = params1.alpha / (params1.alpha + params1.beta) )
104 | bioParams2 = BioParams(size = params2.gamma / params2.beta, freq = 1 / params2.alpha, duty = params2.alpha / (params2.alpha + params2.beta) )
105 |
106 | elif args.mode == 1:
107 |
108 | params1, bioParams1 = getParamsBayesian(p1)
109 | params2, bioParams2 = getParamsBayesian(p2)
110 |
111 | sizeP = cramerVonMises(bioParams1.size.sample, bioParams2.size.sample)
112 | checkCramerVonMises(sizeP, 'for size', idx)
113 |
114 | freqP = cramerVonMises(bioParams1.freq.sample, bioParams2.freq.sample)
115 | checkCramerVonMises(freqP, 'for f', idx)
116 |
117 | dutyP = cramerVonMises(bioParams1.duty.sample, bioParams2.duty.sample)
118 | checkCramerVonMises(dutyP, 'for t', idx)
119 |
120 | if args.test == 3:
121 | difference = likelihoodRatio(idx, p2, params1, params2)
122 |
123 | if difference == -1:
124 | logStatus( Status(1, idx, "Could not estimate p-value. Further analysis aborted.") )
125 | continue
126 |
127 | pVals.append(difference)
128 |
129 | gof1 = goodnessOfFit(p1, params1)
130 | checkCramerVonMises(gof1, 'Goodnes of fit for a first cell type', idx)
131 |
132 | gof2 = goodnessOfFit(p2, params2)
133 | checkCramerVonMises(gof1, 'Goodnes of fit for a second cell type', idx)
134 |
135 | if args.mode == 0:
136 | args.outputFile.write('{0:s}\t{1:4.4f}\t{2:4.4f}\t{3:4.4f}\t{4:4.4e}\t'
137 | '{5:4.4f}\t{6:4.4f}\t{7:4.4f}\t{8:4.4e}\t'
138 | '{9:4.4f}\t{10:4.4f}\t{11:4.4f}\t{12:4.4f}\t{13:4.4f}\t{14:4.4f}\t'
139 | '{15:4.4f}\t{16:4.4f}\t{17:4.4f}\t'
140 | '{18:4.4e}\t{19:4.4f}\t{20:4.4f}\t{21:4.4f}\t{22:4.4f}\n'.format(idx,
141 | params1.alpha, params1.beta, params1.gamma, gof1,
142 | params2.alpha, params2.beta, params2.gamma, gof2,
143 | bioParams1.size, bioParams1.freq, bioParams1.duty,
144 | bioParams2.size, bioParams2.freq, bioParams2.duty,
145 | log2( bioParams1.size / bioParams2.size ),
146 | log2( bioParams1.freq / bioParams2.freq ),
147 | log2( bioParams1.duty / bioParams2.duty ),
148 | difference, mean(p1), variation(p1), mean(p2), variation(p2)) )
149 | elif args.mode == 1:
150 | args.outputFile.write('{0:s}\t{1:4.4f}\t{2:4.4f}\t{3:4.4f}\t{4:4.4e}\t'
151 | '{5:4.4f}\t{6:4.4f}\t{7:4.4f}\t{8:4.4e}\t'
152 | '{9:4.4f}\t{10:4.4f}\t{11:4.4f}\t{12:4.4f}\t{13:4.4f}\t{14:4.4f}\t'
153 | '{15:4.4f}\t{16:4.4f}\t{17:4.4f}\t'
154 | '{18:4.4e}\t{19:4.4e}\t{20:4.4e}\t{21:4.4e}\t{22:4.4f}\t{23:4.4f}\t{24:4.4f}\t{25:4.4f}\n'.format(idx,
155 | params1.alpha.mean(), params1.beta.mean(), params1.gamma.mean(), gof1,
156 | params2.alpha.mean(), params2.beta.mean(), params2.gamma.mean(), gof2,
157 | bioParams1.size.mean(), bioParams1.freq.mean(), bioParams1.duty.mean(),
158 | bioParams2.size.mean(), bioParams2.freq.mean(), bioParams2.duty.mean(),
159 | log2( bioParams2.size.mean() / bioParams1.size.mean()),
160 | log2( bioParams2.freq.mean() / bioParams1.freq.mean()),
161 | log2( bioParams2.duty.mean() / bioParams1.duty.mean()),
162 | sizeP,freqP, dutyP, difference, mean(p1), variation(p1), mean(p2), variation(p2) ) )
163 |
164 | logStatus( Status(0, idx, "Analysis complete.") )
165 |
166 | pVals.sort(reverse = True)
167 | n = len(pVals)
168 |
169 | for i in range(1,n+1):
170 | if pVals[i-1] <= args.fdr * i / len(pVals):
171 | logStatus(Status(0, "D3E", "Significance threshold with given fdr(" + str(args.fdr) + ") is: " + str(pVals[i-1]) ))
172 | break
173 |
174 | args.outputFile.close()
175 |
--------------------------------------------------------------------------------
/D3EUtil.py:
--------------------------------------------------------------------------------
1 | '''
2 |
3 | D3E-Cmd
4 | Discrete Distributional Differential Expression Command Line Tool
5 |
6 | Author: Mihails Delmans (md656@cam.ac.uk)
7 | Advisor: Martin Hemberg (mh26@sanger.ac.uk)
8 | Version: 1.0
9 |
10 | Tested with:
11 | scipy 0.15.1
12 | numpy 1.8.0rc1
13 | sympy.mpmath 0.18
14 |
15 | Copyright 2015 Mihails Delmans, Martin Hemberg
16 |
17 | This file is part of D3E.
18 |
19 | D3E is free software: you can redistribute it and/or modify
20 | it under the terms of the GNU General Public License as published by
21 | the Free Software Foundation, either version 3 of the License, or
22 | (at your option) any later version.
23 |
24 | D3E is distributed in the hope that it will be useful,
25 | but WITHOUT ANY WARRANTY; without even the implied warranty of
26 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
27 | GNU General Public License for more details.
28 |
29 | You should have received a copy of the GNU General Public License
30 | along with D3E. If not, see .
31 |
32 | '''
33 |
34 | from __future__ import division
35 | from scipy.special import kv, gammaln, gamma, hyp1f1, factorial
36 | from scipy.stats import gmean, ks_2samp, anderson_ksamp, chi2
37 | from scipy.stats import poisson as poissonF
38 | from decimal import Decimal, getcontext
39 | from collections import namedtuple
40 | from numpy import log, array, zeros, median, rint, power, hstack, hsplit, seterr, mean, isnan, floor, divide, exp, round, where
41 | from numpy.random import beta, poisson, random
42 | import mpmath as mp
43 |
44 | seterr(all='ignore')
45 |
46 | mp.mp.dps = 30
47 | mp.mp.pretty = True
48 |
49 | Params = namedtuple("Params", ["alpha", "beta", "gamma", "c"])
50 | BioParams = namedtuple("BioParams", ["size", "freq", "duty"])
51 | Status = namedtuple("LineStatus", ["code", "idx", "message"])
52 |
53 |
54 | class RVar:
55 |
56 | def __init__(self, value=0):
57 | self.value = value
58 | self.leftLimit = 0
59 | self.rightLimit = float('Inf')
60 | self.sample = []
61 |
62 | def mean(self):
63 | return mean(self.sample)
64 |
65 | def setSampleFunction(self, function):
66 | self.sampleFunction = function
67 |
68 | def draw(self, maxSteps=1000, saveToSample = False):
69 |
70 | x0 = self.value
71 | w = abs(self.value / 2)
72 |
73 | f = self.sampleFunction;
74 |
75 | logPx = f(x0)
76 | logSlice = log(random()) + logPx
77 |
78 | xLeft = x0 - random() * w
79 | xRight = xLeft + w
80 |
81 | if xLeft < self.leftLimit:
82 | xLeft = self.leftLimit
83 | if xRight > self.rightLimit:
84 | xRight = self.rightLimit
85 |
86 | v = random()
87 |
88 | j = floor(maxSteps*v)
89 | k = maxSteps-1 - j
90 |
91 | while j > 0 and logSlice < f(xLeft) and xLeft - w > self.leftLimit:
92 | j = j-1
93 | xLeft = xLeft - w
94 |
95 |
96 | while k > 0 and logSlice < f(xRight) and xRight + w < self.rightLimit:
97 | k = k - 1
98 | xRight = xRight + w
99 |
100 | n = 10000
101 |
102 | while 1:
103 | n = n - 1
104 | if n < 0 :
105 | print "Warning: Can't find a new value."
106 | return x0
107 |
108 |
109 | x1 = (xRight - xLeft) * random() + xLeft
110 |
111 | if logSlice <= f(x1):
112 | break
113 | if x1 < x0:
114 | xLeft = x1
115 | else:
116 | xRight = x1
117 | self.value = x1
118 |
119 | if saveToSample:
120 | self.sample.append(x1)
121 |
122 | return x1
123 |
124 |
125 | def logStatus(status):
126 | statusType = ['Log','Warning','Error']
127 | print status.idx + ' - ' + statusType[status.code] + ': ' + status.message
128 |
129 | # Read a header of an input file, get indeces of colums that match specified labels
130 | def _readHeader(header, label1, label2):
131 |
132 | if header.lower().startswith('geneid'):
133 | tabs = header.split('\t')
134 |
135 | colIdx1 = [i for i,x in enumerate(tabs) if x == label1 ]
136 | colIdx2 = [i for i,x in enumerate(tabs) if x == label2 ]
137 | else:
138 | return [],[], 1
139 |
140 | return colIdx1, colIdx2, 0
141 |
142 |
143 | def _normalisationWeights(data):
144 | nGenes = data.shape[0]
145 | nCells = data.shape[1]
146 | geneGM = gmean(data+1, 1)
147 | weights = array( [median( (data[:,i]+1) / geneGM) for i in range(nCells)] )
148 | return weights
149 |
150 | # Read input data file, extract read counts, normalise, report errors
151 | def readData(inputFile, label1, label2, normalise=True, removeZeros=False, useSpikeIns = False, verbose = False, spikeInStart = 'spike'):
152 |
153 | data1 = []
154 | data2 = []
155 | spikeIns = []
156 | lineStatus = []
157 | ids = []
158 | empty = []
159 |
160 | header = inputFile.readline()
161 |
162 | colIdx1, colIdx2, status = _readHeader(header, label1, label2)
163 |
164 | if status == 1:
165 | lineStatus.append( Status(2, 'Header', 'Invalid header format') )
166 | return [],[],[],lineStatus
167 | else:
168 | if not colIdx1:
169 | lineStatus.append( Status(2, 'Header', "No colums with label '" + label1 + "' found") )
170 | return [],[],[],lineStatus
171 | elif not colIdx2:
172 | lineStatus.append( Status(2, 'Header', "No colums with label '" + label2 + "' found") )
173 | return [],[],[],lineStatus
174 | else:
175 | lineStatus.append( Status(0, 'Header', "Read OK") )
176 |
177 | for line in inputFile:
178 | if line.strip():
179 | cols = line.split()
180 |
181 | idx = cols[0]
182 |
183 | p1 = [ float(cols[x]) for x in colIdx1 ]
184 | p2 = [ float(cols[x]) for x in colIdx2 ]
185 |
186 | if max(p1) == 0 and max(p2) == 0:
187 | lineStatus.append( Status(1, idx, "Null expression detected") )
188 | continue
189 | else:
190 | lineStatus.append( Status(0, idx, "Line read OK") )
191 |
192 |
193 | if idx.lower().startswith(spikeInStart):
194 | spikeIns.append( p1 + p2 )
195 | lineStatus.append( Status(0, idx, "Spike-in detected") )
196 | continue
197 |
198 | data1.append(p1)
199 | data2.append(p2)
200 | ids.append(idx)
201 |
202 | if normalise:
203 |
204 | if useSpikeIns:
205 | if len(spikeIns) == 0:
206 | lineStatus.append(Status(2, 'Spike-ins','No spike-ins data detected') )
207 | return [],[],[],lineStatus
208 |
209 | weights = _normalisationWeights(array(spikeIns))
210 |
211 | else:
212 | weights = _normalisationWeights( hstack( ( array(data1), array(data2) ) ) )
213 |
214 | splitColumn = array(data1).shape[1]
215 |
216 | data1 = [ (array(readLine) / weights[:splitColumn]).tolist() for readLine in data1 ]
217 | data2 = [ (array(readLine) / weights[splitColumn:]).tolist() for readLine in data2 ]
218 |
219 | if removeZeros:
220 |
221 | dataFiltered1 = []
222 | dataFiltered2 = []
223 | idsFiltered = []
224 |
225 | for p1, p2, idx in zip(data1,data2,ids):
226 | p1 = filter(lambda x: x!=0, p1)
227 | p2 = filter(lambda x: x!=0, p2)
228 |
229 | if len(p1) != 0 and len(p2) != 0:
230 | dataFiltered1.append(p1)
231 | dataFiltered2.append(p2)
232 | idsFiltered.append(idx)
233 | else:
234 | lineStatus.append( Status(1, idx, "Empty expression after zero removal") )
235 |
236 | data1 = dataFiltered1
237 | data2 = dataFiltered2
238 | ids = idsFiltered
239 |
240 | inputFile.close()
241 |
242 | return data1, data2, ids, lineStatus
243 |
244 |
245 | # Sorts set x and returns a rank of elements in a sorted set
246 | def _sortRank(x):
247 |
248 | xs = sorted(x)
249 | r = []
250 |
251 | i = 1
252 | while i <= len(xs):
253 | rStart = i
254 | rEnd = i
255 |
256 | if i < len(xs):
257 | while xs[i-1] == xs[i]:
258 | i += 1
259 | rEnd = i
260 | if i >= len(xs):
261 | break
262 |
263 | i += 1
264 | n = (rEnd-rStart + 1)
265 | r.extend([ (rStart + rEnd) / 2 for j in range(n)])
266 |
267 | return xs, r
268 |
269 | # Perform a Cramer - von Mises test of two samples x and y. H0: samples x and y are drawn from the same distribution. Returns a p-value.
270 | # Anderson, Theodore W., On the Distribution of the Two-Sample Cramer-von Mises Criterion, 1962, The Annals of Mathematical Statistics 33, 1148-1159
271 | # Anderson, Theodore W., Donald A. Darling, 1952, Asymptotic Theory of Certain Goodness of Fit Criteria Based on Stochastic Processes, The Annals of Mathematical Statistics 23, 193-212.
272 | def cramerVonMises(x, y):
273 |
274 | try:
275 | x = sorted(x);
276 | y = sorted(y);
277 |
278 | pool = x + y;
279 |
280 | ps, pr = _sortRank(pool)
281 |
282 | rx = array ( [ pr[ind] for ind in [ ps.index(element) for element in x ] ] )
283 | ry = array ( [ pr[ind] for ind in [ ps.index(element) for element in y ] ] )
284 |
285 | n = len(x)
286 | m = len(y)
287 |
288 | i = array(range(1, n+1))
289 | j = array(range(1, m+1))
290 |
291 | u = n * sum ( power( (rx - i), 2 ) ) + m * sum ( power((ry - j), 2) )
292 |
293 | t = u / (n*m*(n+m)) - (4*n*m-1) / (6 * (n+m))
294 | Tmu = 1/6 + 1 / (6*(n+m))
295 | Tvar = 1/45 * ( (m+n+1) / power((m+n),2) ) * (4*m*n*(m+n) - 3*(power(m,2) + power(n,2)) - 2*m*n) / (4*m*n)
296 | t = (t - Tmu) / power(45*Tvar, 0.5) + 1/6
297 |
298 | if t < 0:
299 | return -1
300 | elif t <= 12:
301 | a = 1-mp.nsum(lambda x : ( mp.gamma(x+0.5) / ( mp.gamma(0.5) * mp.fac(x) ) ) *
302 | mp.power( 4*x + 1, 0.5 ) * mp.exp ( - mp.power(4*x + 1, 2) / (16*t) ) *
303 | mp.besselk(0.25 , mp.power(4*x + 1, 2) / (16*t) ), [0,100] ) / (mp.pi*mp.sqrt(t))
304 | return float(mp.nstr(a,3))
305 | else:
306 | return 0
307 | except Exception as e:
308 | print e
309 | return -1
310 |
311 | # Perform a Kolmogorov-Smirnov test of two samples x and y. H0: samples x and y are drawn from the same distribution. Returns a p-value.
312 | def KSTest(x, y):
313 | try:
314 | return ks_2samp(x,y)[1]
315 |
316 | except Exception as e:
317 | print e
318 | return -1
319 |
320 | # Perform a Anderson-Darling test of two samples x and y. H0: samples x and y are drawn from the same distribution. Returns am interpolated p-value.
321 | def ADTest(x,y):
322 | try:
323 | return anderson_ksamp([x,y])[2]
324 | except Exception as e:
325 | print e
326 | return -1
327 |
328 | def distributionTest(x,y,method):
329 | if method == 1:
330 | return KSTest(x,y)
331 | elif method == 2:
332 | return ADTest(x,y)
333 | elif method == 3:
334 | return 0
335 | else:
336 | return cramerVonMises(x,y)
337 |
338 | # Generation of a sample from Poisson-beta distribution with given parameters parms and size n
339 | def randPoissonBeta(params,n):
340 |
341 | x = beta( params.alpha, params.beta, n )
342 | p = poisson( x * params.gamma )
343 |
344 | return p
345 |
346 | # Estimation of the parameters of a sample drawn from a Poisson-beta distribution using momet matching technique from
347 | # Peccoud, Jean, Bernard Ycart, 1995, Markovian modelling of gene product synthesis, Theoretical Population Biology 48, 222-234.
348 | def getParamsMoments(p):
349 |
350 | try:
351 | rm1 = sum(p) / len(p)
352 | rm2 = sum( [pow(x,2) for x in p] ) / len(p)
353 | rm3 = sum( [pow(x,3) for x in p] ) / len(p)
354 |
355 | fm1 = rm1
356 | fm2 = rm2 - rm1
357 | fm3 = rm3 - 3*rm2 + 2*rm1
358 |
359 | r1 = fm1
360 | r2 = fm2 / fm1
361 | r3 = fm3 / fm2
362 |
363 | alpha = 2*r1 * (r3 - r2) / (r1*r2 - 2*r1*r3 + r2*r3)
364 | beta = 2 * (r2 - r1) * (r1 - r3) * (r3 - r2) / ((r1*r2 - 2*r1*r3 + r2*r3) *(r1 - 2*r2 + r3))
365 | gamma = (-r1*r2 + 2*r1*r3 - r2*r3) / (r1 - 2*r2 +r3)
366 | except:
367 | return Params(-1,-1,-1,-1)
368 |
369 | return Params(alpha, beta, gamma, 0)
370 |
371 | # Esimation of the parameters of a sample drawn from a Poisson-beta distribution using bayesian inference method
372 | # Kim, Jong Kyoung, John C. Marioni, 2013, Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data, Genome Biology 14, R7.
373 | def getParamsBayesian(p, iterN=1000):
374 |
375 | HyperParams = namedtuple("HyperParams", ["k_alpha", "theta_alpha", "k_beta", "theta_beta", "k_gamma", "theta_gamma"])
376 |
377 | parFit = getParamsMoments(p)
378 |
379 | hyperParams = HyperParams(k_alpha = 1, theta_alpha = 100, k_beta = 1, theta_beta = 100, k_gamma = 1, theta_gamma = max(p) )
380 |
381 | if parFit.alpha > 0 and parFit.beta > 0 and parFit.gamma > 0:
382 |
383 | params = Params(alpha = RVar(parFit.alpha), beta = RVar(parFit.beta), gamma = RVar(parFit.gamma), c = [ RVar(0.5) for i in range(len(p)) ] )
384 | else:
385 | params = Params(alpha = RVar(0.5), beta = RVar(0.5), gamma = RVar(mean(p)+1e6), c = [ RVar(0.5) for i in range(len(p)) ] )
386 |
387 | bioParams = BioParams(size = RVar(), freq = RVar(), duty = RVar() )
388 |
389 | save = False
390 |
391 | for i in range(iterN):
392 |
393 | if i > iterN / 2:
394 | save = True
395 |
396 | alpha = params.alpha.value
397 | beta = params.beta.value
398 | gamma = params.gamma.value
399 |
400 | bioParams.size.sample.append( gamma / beta )
401 | bioParams.freq.sample.append( alpha*beta / (alpha + beta) )
402 | bioParams.duty.sample.append( alpha / (alpha + beta) )
403 |
404 | for c,pi in zip(params.c,p):
405 | c.setSampleFunction(lambda x: (params.alpha.value - 1 ) * log(x) + (params.beta.value - 1) * log(1-x) + pi * log(x) - params.gamma.value * x)
406 | c.draw(saveToSample = save)
407 |
408 | params.gamma.setSampleFunction( lambda x: (hyperParams.k_gamma-1)*log(x) - x / hyperParams.theta_gamma + log(x) * sum(p) - x * sum( [c.value for c in params.c] ) )
409 | params.gamma.draw(saveToSample = save)
410 |
411 | params.alpha.setSampleFunction( lambda x: (hyperParams.k_alpha-1)*log(x) - x / hyperParams.theta_alpha + len(p)*(gammaln(x + params.beta.value) - gammaln(x)) + (x-1) * sum([ log(c.value) for c in params.c ]) )
412 | params.alpha.draw(saveToSample = save)
413 |
414 | params.beta.setSampleFunction( lambda x: (hyperParams.k_beta-1)*log(x) - x / hyperParams.theta_beta + len(p)*(gammaln(x + params.alpha.value) - gammaln(x)) + (x-1) * sum([ log(1-c.value) for c in params.c ]) )
415 | params.beta.draw(saveToSample = save)
416 |
417 | return params, bioParams
418 |
419 | def goodnessOfFit(p, params):
420 |
421 | try:
422 | alpha = params.alpha.mean()
423 | beta = params.beta.mean()
424 | gamma = params.gamma.mean()
425 | except:
426 | alpha = params.alpha
427 | beta = params.beta
428 | gamma = params.gamma
429 |
430 | pr = randPoissonBeta( Params(alpha,beta,gamma,0), len(p))
431 |
432 | return cramerVonMises(pr,p)
433 |
434 | # Find log of likelihood for sample 'p' with parameters 'params', doing poisson-beta sampling 'n' times
435 | def logLikelihood(p, params, n, idx):
436 |
437 | try:
438 | alpha = params.alpha.mean()
439 | beta_ = params.beta.mean()
440 | gamma = int(round(params.gamma.mean()))+1
441 | except:
442 | alpha = params.alpha
443 | beta_ = params.beta
444 | gamma = int(round(params.gamma))+1
445 |
446 |
447 | if n * gamma > 1e9:
448 | n = int( round(1e9 / gamma) )
449 | logStatus( Status(1, idx, "Reduced Poisson-Beta sample to " + str(n) + ".") )
450 |
451 | pVal = []
452 |
453 | for item in p:
454 | x = beta(alpha, beta_, n)
455 | pTemp = 0
456 |
457 | for i in range(n):
458 | pTemp += poissonF.pmf(item, gamma*x[i])
459 | pVal.append(pTemp / n)
460 |
461 | pVal = array(pVal)
462 | pVal[where(pVal==0)] = 1/n
463 |
464 | return sum(log(pVal))
465 |
466 | # Perform likelihood ratio test fitted parameters
467 | def likelihoodRatio(idx, p, params1, params2, n=100):
468 |
469 | try:
470 | alpha1 = params1.alpha.mean()
471 | beta1 = params1.beta.mean()
472 | gamma1 = round(params1.gamma.mean()) + 1
473 | except:
474 | alpha1 = params1.alpha
475 | beta1 = params1.beta
476 | gamma1 = round(params1.gamma) + 1
477 |
478 | try:
479 | alpha2 = params2.alpha.mean()
480 | beta2 = params2.beta.mean()
481 | gamma2 = round(params2.gamma.mean()) + 1
482 | except:
483 | alpha2 = params2.alpha
484 | beta2 = params2.beta
485 | gamma2 = round(params2.gamma) + 1
486 |
487 | sum1 = logLikelihood([int(x) for x in round(p)], params1, n, idx)
488 | sum2 = logLikelihood([int(x) for x in round(p)], params2, n, idx)
489 |
490 | ratio = sum1 - sum2
491 |
492 | if ratio >= 0:
493 | logStatus( Status(1, idx, "Could not perform likelihood ratio test.") )
494 | return float('nan')
495 |
496 | pVal = chi2.pdf(-2*ratio,3)
497 |
498 | return pVal
499 |
500 |
501 |
--------------------------------------------------------------------------------
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--------------------------------------------------------------------------------
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502 | publicly available network server or other readily accessible means,
503 | then you must either (1) cause the Corresponding Source to be so
504 | available, or (2) arrange to deprive yourself of the benefit of the
505 | patent license for this particular work, or (3) arrange, in a manner
506 | consistent with the requirements of this License, to extend the patent
507 | license to downstream recipients. "Knowingly relying" means you have
508 | actual knowledge that, but for the patent license, your conveying the
509 | covered work in a country, or your recipient's use of the covered work
510 | in a country, would infringe one or more identifiable patents in that
511 | country that you have reason to believe are valid.
512 |
513 | If, pursuant to or in connection with a single transaction or
514 | arrangement, you convey, or propagate by procuring conveyance of, a
515 | covered work, and grant a patent license to some of the parties
516 | receiving the covered work authorizing them to use, propagate, modify
517 | or convey a specific copy of the covered work, then the patent license
518 | you grant is automatically extended to all recipients of the covered
519 | work and works based on it.
520 |
521 | A patent license is "discriminatory" if it does not include within
522 | the scope of its coverage, prohibits the exercise of, or is
523 | conditioned on the non-exercise of one or more of the rights that are
524 | specifically granted under this License. You may not convey a covered
525 | work if you are a party to an arrangement with a third party that is
526 | in the business of distributing software, under which you make payment
527 | to the third party based on the extent of your activity of conveying
528 | the work, and under which the third party grants, to any of the
529 | parties who would receive the covered work from you, a discriminatory
530 | patent license (a) in connection with copies of the covered work
531 | conveyed by you (or copies made from those copies), or (b) primarily
532 | for and in connection with specific products or compilations that
533 | contain the covered work, unless you entered into that arrangement,
534 | or that patent license was granted, prior to 28 March 2007.
535 |
536 | Nothing in this License shall be construed as excluding or limiting
537 | any implied license or other defenses to infringement that may
538 | otherwise be available to you under applicable patent law.
539 |
540 | 12. No Surrender of Others' Freedom.
541 |
542 | If conditions are imposed on you (whether by court order, agreement or
543 | otherwise) that contradict the conditions of this License, they do not
544 | excuse you from the conditions of this License. If you cannot convey a
545 | covered work so as to satisfy simultaneously your obligations under this
546 | License and any other pertinent obligations, then as a consequence you may
547 | not convey it at all. For example, if you agree to terms that obligate you
548 | to collect a royalty for further conveying from those to whom you convey
549 | the Program, the only way you could satisfy both those terms and this
550 | License would be to refrain entirely from conveying the Program.
551 |
552 | 13. Use with the GNU Affero General Public License.
553 |
554 | Notwithstanding any other provision of this License, you have
555 | permission to link or combine any covered work with a work licensed
556 | under version 3 of the GNU Affero General Public License into a single
557 | combined work, and to convey the resulting work. The terms of this
558 | License will continue to apply to the part which is the covered work,
559 | but the special requirements of the GNU Affero General Public License,
560 | section 13, concerning interaction through a network will apply to the
561 | combination as such.
562 |
563 | 14. Revised Versions of this License.
564 |
565 | The Free Software Foundation may publish revised and/or new versions of
566 | the GNU General Public License from time to time. Such new versions will
567 | be similar in spirit to the present version, but may differ in detail to
568 | address new problems or concerns.
569 |
570 | Each version is given a distinguishing version number. If the
571 | Program specifies that a certain numbered version of the GNU General
572 | Public License "or any later version" applies to it, you have the
573 | option of following the terms and conditions either of that numbered
574 | version or of any later version published by the Free Software
575 | Foundation. If the Program does not specify a version number of the
576 | GNU General Public License, you may choose any version ever published
577 | by the Free Software Foundation.
578 |
579 | If the Program specifies that a proxy can decide which future
580 | versions of the GNU General Public License can be used, that proxy's
581 | public statement of acceptance of a version permanently authorizes you
582 | to choose that version for the Program.
583 |
584 | Later license versions may give you additional or different
585 | permissions. However, no additional obligations are imposed on any
586 | author or copyright holder as a result of your choosing to follow a
587 | later version.
588 |
589 | 15. Disclaimer of Warranty.
590 |
591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
599 |
600 | 16. Limitation of Liability.
601 |
602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
610 | SUCH DAMAGES.
611 |
612 | 17. Interpretation of Sections 15 and 16.
613 |
614 | If the disclaimer of warranty and limitation of liability provided
615 | above cannot be given local legal effect according to their terms,
616 | reviewing courts shall apply local law that most closely approximates
617 | an absolute waiver of all civil liability in connection with the
618 | Program, unless a warranty or assumption of liability accompanies a
619 | copy of the Program in return for a fee.
620 |
621 | END OF TERMS AND CONDITIONS
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