In [1]:
import theano
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
In [2]:
src_dir='../src' # source directory
run_dir_prefix='../MDBN_run/AML_Batch_' # directory with the results of previous runs
data_dir='../data' # directory with the data files
In [3]:
import sys
sys.path.insert(0, src_dir)
import MDBN
import main
In [4]:
run=0
uuid='eb6856a251bb8680da6593de98db7b5a'
timestamp='20170220T205506' # specify the date and time of the run in the format YYYYmmddTHHMMSS
run_dir=run_dir_prefix+timestamp
runfile='Exp_%s_run_%s.npz' % (uuid, run) # location of the experiment results
mdbn = MDBN.MDBN()
config, dbn_dict = mdbn.load_network(runfile, run_dir)
In [5]:
config
Out[5]:
{u'dbns': {u'GE': {u'batchSize': 1,
u'datafile': u'AML/AML_gene_expression_table2.csv.gz',
u'epochs': [20, 800],
'inputNodes': 19937,
u'k': 1,
u'lambdas': [0.0, 1.0],
u'layersNodes': [400, 40],
u'lr': [0.0005, 0.1],
u'persistent': False},
u'ME': {u'batchSize': 1,
u'datafile': u'AML/AML_miRNA_Seq_table2.csv.gz',
u'epochs': [20],
'inputNodes': 559,
u'k': 10,
u'lambdas': [0.0, 0.1],
u'layersNodes': [40],
u'lr': [0.005],
u'persistent': False},
u'SM': {u'batchSize': 1,
u'datafile': u'AML/AML_somatic_mutations_table2.csv.gz',
u'epochs': [20, 800],
'inputNodes': 1686,
u'k': 1,
u'lambdas': [0.0, 0.1],
u'layersNodes': [200, 20],
u'lr': [0.01, 0.1],
u'persistent': False}},
u'name': u'aml_config_20_1_05.json',
u'p': 0.5,
u'pathways': [u'ME', u'GE', u'SM'],
u'runs': 4,
u'seed': 1234,
u'top': {u'batchSize': 1,
u'epochs': [800, 800],
'inputNodes': 100,
u'k': 1,
u'layersNodes': [24, 3],
u'lr': [0.1, 0.1],
u'persistent': False},
u'uuid': u'eb6856a251bb8680da6593de98db7b5a'}
In [6]:
datafiles = main.prepare_TCGA_datafiles("AML",config,data_dir)
In [7]:
dbn_output_list=[]
for key in config['pathways']:
dbn_output, _ = dbn_dict[key].MLP_output_from_datafile(datafiles[key], datadir=data_dir)
dbn_output_list.append(dbn_output)
In [8]:
joint_layer = np.concatenate(dbn_output_list,axis=1)
plt.imshow(joint_layer, interpolation='none', cmap='gray')
plt.axis('tight')
plt.colorbar()
Out[8]:
<matplotlib.colorbar.Colorbar at 0x12a04a550>
In [9]:
joint_layer[1:10,1:10]
Out[9]:
array([[ 3.54498532e-03, 9.68264043e-01, 1.58789571e-05,
1.43725076e-04, 5.65388203e-01, 1.58122092e-01,
1.91909494e-03, 9.29199278e-01, 9.75213706e-01],
[ 6.87696517e-01, 9.99528527e-01, 3.80846590e-01,
9.58603173e-02, 1.69651618e-03, 9.99985158e-01,
9.99352038e-01, 6.14848472e-02, 1.26754312e-04],
[ 9.97018397e-01, 7.49598071e-02, 6.05323445e-03,
4.89641214e-04, 7.28196892e-06, 8.40969086e-01,
7.51088619e-01, 5.30721784e-01, 4.58242446e-02],
[ 9.99294937e-01, 8.15736413e-01, 1.17231735e-07,
6.16892422e-08, 7.21758965e-07, 7.03742588e-03,
5.02763875e-03, 8.83783996e-01, 9.35936987e-01],
[ 9.98012245e-01, 6.69994056e-02, 9.99994397e-01,
9.99994874e-01, 9.99123991e-01, 9.98598158e-01,
9.97912526e-01, 2.36577198e-01, 9.24622524e-04],
[ 1.00000000e+00, 1.24667636e-07, 1.60419949e-13,
2.64621380e-10, 1.13940445e-07, 5.65073435e-07,
9.93333876e-01, 1.60389409e-05, 9.99953687e-01],
[ 9.97021735e-01, 9.89549339e-01, 9.96154070e-01,
9.99971509e-01, 9.92925167e-01, 9.99905944e-01,
9.99880314e-01, 7.12417662e-01, 6.23832864e-04],
[ 7.81966466e-03, 4.85493103e-03, 1.51464967e-02,
9.59480405e-01, 1.73771873e-01, 2.22010468e-03,
1.33509012e-02, 8.66687655e-01, 9.99066710e-01],
[ 2.29438782e-01, 9.82405245e-01, 9.99983728e-01,
9.99998987e-01, 9.94098127e-01, 9.99984562e-01,
9.98172224e-01, 9.87781107e-01, 5.64975140e-04]], dtype=float32)
In [10]:
plt.hist(dbn_dict['GE'].rbm_layers[0].W.get_value())
Out[10]:
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5.58200000e+03, 6.69700000e+03, 4.25800000e+03,
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0.00000000e+00]),
array([ 0., 41., 1218., 5717., 6785., 4301., 1641., 234.,
0., 0.]),
array([ 1.00000000e+00, 1.77000000e+02, 2.00400000e+03,
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0.00000000e+00]),
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0.00000000e+00]),
array([ 0., 72., 1516., 5723., 7162., 4414., 990., 60.,
0., 0.]),
array([ 1.00000000e+00, 1.94000000e+02, 1.79800000e+03,
6.25500000e+03, 7.26000000e+03, 3.64300000e+03,
7.65000000e+02, 2.10000000e+01, 0.00000000e+00,
0.00000000e+00]),
array([ 3.00000000e+00, 1.81000000e+02, 1.28300000e+03,
5.02400000e+03, 7.23800000e+03, 4.65400000e+03,
1.35000000e+03, 1.96000000e+02, 8.00000000e+00,
0.00000000e+00]),
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4.28000000e+03, 7.57700000e+03, 5.57100000e+03,
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0.00000000e+00]),
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array([ 0., 43., 1439., 5808., 7668., 3982., 976., 21.,
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0.00000000e+00]),
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4.02200000e+03, 7.19100000e+03, 5.90900000e+03,
2.18200000e+03, 1.73000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 70., 1173., 4069., 6495., 5199., 2512., 403.,
16., 0.]),
array([ 3.00000000e+00, 2.19000000e+02, 1.39800000e+03,
5.55200000e+03, 6.58400000e+03, 4.39200000e+03,
1.56900000e+03, 2.13000000e+02, 6.00000000e+00,
1.00000000e+00]),
array([ 0., 84., 1013., 4305., 6639., 5499., 2027., 344.,
26., 0.]),
array([ 3.00000000e+00, 9.30000000e+01, 1.17600000e+03,
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0.00000000e+00]),
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0.00000000e+00]),
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0.00000000e+00]),
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1.82900000e+03, 2.13000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 2.00000000e+00, 1.23000000e+02, 1.43800000e+03,
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0.00000000e+00]),
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2.16200000e+03, 5.62000000e+02, 6.70000000e+01,
3.00000000e+00]),
array([ 0.00000000e+00, 1.49000000e+02, 2.07600000e+03,
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7.79000000e+02, 6.00000000e+01, 1.00000000e+00,
0.00000000e+00]),
array([ 1.00000000e+00, 4.90000000e+01, 9.00000000e+02,
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array([ 2.00000000e+00, 1.63000000e+02, 1.27000000e+03,
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0.00000000e+00]),
array([ 2.00000000e+00, 7.40000000e+01, 1.32900000e+03,
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1.41000000e+03, 2.14000000e+02, 1.00000000e+01,
0.00000000e+00]),
array([ 0., 59., 1280., 6480., 6876., 4044., 1121., 77.,
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array([ 8., 108., 742., 3034., 5818., 6279., 3433., 492.,
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array([ 3.00000000e+00, 1.72000000e+02, 1.76400000e+03,
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array([ 0., 51., 706., 3466., 6402., 5904., 2738., 644.,
26., 0.]),
array([ 1.00000000e+00, 1.41000000e+02, 2.05700000e+03,
6.83400000e+03, 6.92800000e+03, 3.29600000e+03,
6.42000000e+02, 3.80000000e+01, 0.00000000e+00,
0.00000000e+00]),
array([ 4.00000000e+00, 1.26000000e+02, 9.54000000e+02,
3.60500000e+03, 5.90500000e+03, 5.64700000e+03,
2.82600000e+03, 7.59000000e+02, 1.11000000e+02,
0.00000000e+00]),
array([ 10., 134., 1080., 4379., 6957., 4951., 2044., 371.,
11., 0.]),
array([ 0., 84., 1623., 6586., 7616., 3268., 731., 29.,
0., 0.]),
array([ 0.00000000e+00, 1.19000000e+02, 1.78500000e+03,
6.31800000e+03, 6.46700000e+03, 3.70400000e+03,
1.32900000e+03, 2.13000000e+02, 2.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 1.84000000e+02, 1.83000000e+03,
5.00600000e+03, 6.85900000e+03, 4.11200000e+03,
1.77700000e+03, 1.68000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 14., 843., 4391., 7063., 5410., 2049., 167.,
0., 0.]),
array([ 1.00000000e+00, 1.20000000e+02, 1.45300000e+03,
5.87500000e+03, 7.35500000e+03, 3.93600000e+03,
1.08900000e+03, 1.06000000e+02, 2.00000000e+00,
0.00000000e+00]),
array([ 5.00000000e+00, 9.70000000e+01, 1.00000000e+03,
3.81400000e+03, 6.61900000e+03, 5.66700000e+03,
2.34200000e+03, 3.82000000e+02, 1.10000000e+01,
0.00000000e+00]),
array([ 1.20000000e+01, 2.73000000e+02, 1.84100000e+03,
5.87500000e+03, 6.79300000e+03, 3.77100000e+03,
1.22600000e+03, 1.41000000e+02, 5.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 1.43000000e+02, 1.57700000e+03,
5.92000000e+03, 6.70200000e+03, 4.09100000e+03,
1.33000000e+03, 1.70000000e+02, 4.00000000e+00,
0.00000000e+00]),
array([ 2.00000000e+00, 1.07000000e+02, 1.57300000e+03,
5.94400000e+03, 6.30000000e+03, 4.11600000e+03,
1.62800000e+03, 2.60000000e+02, 7.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 2.20000000e+01, 8.50000000e+02,
4.43800000e+03, 7.17400000e+03, 4.91700000e+03,
2.22600000e+03, 3.09000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 5.80000000e+01, 1.16500000e+03,
5.40100000e+03, 7.11600000e+03, 4.53400000e+03,
1.53700000e+03, 1.25000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 46., 1462., 6410., 6665., 4053., 1212., 89.,
0., 0.]),
array([ 0.00000000e+00, 9.00000000e+00, 6.64000000e+02,
4.17700000e+03, 7.08200000e+03, 5.53700000e+03,
2.24100000e+03, 2.26000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 4.00000000e+00, 1.33000000e+02, 1.68200000e+03,
6.41600000e+03, 6.87100000e+03, 3.65600000e+03,
1.05300000e+03, 1.20000000e+02, 2.00000000e+00,
0.00000000e+00]),
array([ 1.00000000e+00, 1.35000000e+02, 1.49700000e+03,
4.16400000e+03, 6.13400000e+03, 5.16000000e+03,
2.43600000e+03, 3.88000000e+02, 2.20000000e+01,
0.00000000e+00]),
array([ 0.00000000e+00, 4.80000000e+01, 1.29600000e+03,
5.02200000e+03, 8.10200000e+03, 4.08200000e+03,
1.26700000e+03, 1.19000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 23., 850., 4113., 7047., 5375., 2119., 402.,
8., 0.]),
array([ 0.00000000e+00, 7.70000000e+01, 1.11700000e+03,
4.70900000e+03, 7.63400000e+03, 4.81400000e+03,
1.41800000e+03, 1.66000000e+02, 2.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 6.30000000e+01, 1.86600000e+03,
7.51200000e+03, 6.87000000e+03, 2.81000000e+03,
7.36000000e+02, 7.50000000e+01, 5.00000000e+00,
0.00000000e+00]),
array([ 6., 210., 1976., 5708., 5854., 4146., 1713., 307.,
17., 0.]),
array([ 0., 29., 1251., 7126., 8383., 2631., 498., 19.,
0., 0.]),
array([ 0., 44., 744., 3378., 6334., 6195., 2568., 627.,
47., 0.]),
array([ 0.00000000e+00, 2.00000000e+01, 8.24000000e+02,
5.03600000e+03, 7.06100000e+03, 4.96200000e+03,
1.87200000e+03, 1.60000000e+02, 2.00000000e+00,
0.00000000e+00]),
array([ 0., 112., 1512., 5684., 6392., 4092., 1836., 301.,
8., 0.]),
array([ 0.00000000e+00, 1.70000000e+01, 7.53000000e+02,
4.47600000e+03, 7.38700000e+03, 5.28900000e+03,
1.82500000e+03, 1.87000000e+02, 3.00000000e+00,
0.00000000e+00]),
array([ 3.00000000e+00, 8.20000000e+01, 1.25500000e+03,
5.30900000e+03, 6.53600000e+03, 4.45500000e+03,
1.99500000e+03, 2.92000000e+02, 1.00000000e+01,
0.00000000e+00]),
array([ 0., 72., 1324., 6499., 7436., 3418., 1109., 79.,
0., 0.]),
array([ 0., 43., 880., 3846., 6795., 5601., 2300., 451.,
21., 0.]),
array([ 1.00000000e+00, 5.30000000e+01, 1.03900000e+03,
5.22100000e+03, 6.96600000e+03, 4.46300000e+03,
1.77500000e+03, 4.03000000e+02, 1.60000000e+01,
0.00000000e+00]),
array([ 1.00000000e+00, 9.20000000e+01, 1.16900000e+03,
4.55000000e+03, 6.53100000e+03, 4.74400000e+03,
2.42500000e+03, 4.01000000e+02, 2.40000000e+01,
0.00000000e+00]),
array([ 1.00000000e+00, 7.30000000e+01, 7.36000000e+02,
3.56500000e+03, 6.08700000e+03, 6.20300000e+03,
2.82800000e+03, 4.29000000e+02, 1.50000000e+01,
0.00000000e+00]),
array([ 0., 50., 1502., 6952., 7095., 3497., 803., 38.,
0., 0.]),
array([ 16., 280., 2072., 5982., 6088., 3737., 1474., 273.,
15., 0.]),
array([ 3.00000000e+00, 8.40000000e+01, 7.84000000e+02,
3.49500000e+03, 6.10400000e+03, 6.24400000e+03,
2.81700000e+03, 3.81000000e+02, 2.50000000e+01,
0.00000000e+00]),
array([ 0., 34., 676., 3374., 6571., 5994., 2877., 404.,
7., 0.]),
array([ 4.00000000e+00, 2.04000000e+02, 1.72600000e+03,
5.16700000e+03, 6.86000000e+03, 4.11000000e+03,
1.70300000e+03, 1.62000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 41., 1317., 5498., 7233., 4470., 1318., 60.,
0., 0.]),
array([ 0., 59., 1699., 5582., 7462., 3661., 1328., 146.,
0., 0.]),
array([ 3.00000000e+00, 1.10000000e+02, 1.22100000e+03,
4.93400000e+03, 6.84200000e+03, 4.54800000e+03,
1.83400000e+03, 4.16000000e+02, 2.80000000e+01,
1.00000000e+00]),
array([ 3.00000000e+00, 6.20000000e+01, 6.73000000e+02,
3.53500000e+03, 6.54800000e+03, 6.04300000e+03,
2.64700000e+03, 4.13000000e+02, 1.30000000e+01,
0.00000000e+00]),
array([ 2.00000000e+00, 9.90000000e+01, 1.51100000e+03,
5.93500000e+03, 6.28000000e+03, 4.29400000e+03,
1.60400000e+03, 2.08000000e+02, 4.00000000e+00,
0.00000000e+00]),
array([ 2.00000000e+00, 1.12000000e+02, 1.64900000e+03,
6.68000000e+03, 6.99200000e+03, 3.44800000e+03,
9.35000000e+02, 1.15000000e+02, 4.00000000e+00,
0.00000000e+00]),
array([ 0., 122., 1487., 4910., 7311., 4723., 1317., 67.,
0., 0.]),
array([ 0., 81., 1535., 6654., 7759., 3234., 644., 30.,
0., 0.]),
array([ 0., 45., 918., 3857., 7818., 5365., 1862., 72.,
0., 0.]),
array([ 2.00000000e+00, 9.20000000e+01, 8.45000000e+02,
4.03200000e+03, 7.15900000e+03, 5.33700000e+03,
2.04200000e+03, 4.16000000e+02, 1.20000000e+01,
0.00000000e+00]),
array([ 0.00000000e+00, 3.90000000e+01, 1.25600000e+03,
6.28400000e+03, 7.81700000e+03, 3.47000000e+03,
9.79000000e+02, 9.10000000e+01, 1.00000000e+00,
0.00000000e+00]),
array([ 1.00000000e+00, 1.10000000e+02, 1.34100000e+03,
5.29500000e+03, 6.80200000e+03, 4.74400000e+03,
1.49800000e+03, 1.45000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 37., 1018., 4829., 6688., 4889., 2097., 364.,
15., 0.]),
array([ 4.00000000e+00, 1.83000000e+02, 1.42200000e+03,
4.66200000e+03, 6.68800000e+03, 4.52200000e+03,
2.04800000e+03, 3.94000000e+02, 1.40000000e+01,
0.00000000e+00]),
array([ 3.00000000e+00, 1.63000000e+02, 1.77100000e+03,
5.50100000e+03, 6.99600000e+03, 3.90900000e+03,
1.32900000e+03, 2.47000000e+02, 1.80000000e+01,
0.00000000e+00]),
array([ 0.00000000e+00, 1.30000000e+02, 1.48900000e+03,
5.23100000e+03, 6.73900000e+03, 4.52100000e+03,
1.63500000e+03, 1.88000000e+02, 4.00000000e+00,
0.00000000e+00]),
array([ 0., 40., 1436., 5869., 7900., 3524., 1064., 104.,
0., 0.]),
array([ 5.00000000e+00, 2.48000000e+02, 1.41300000e+03,
5.05000000e+03, 7.22400000e+03, 3.97900000e+03,
1.73500000e+03, 2.78000000e+02, 5.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 2.80000000e+01, 7.88000000e+02,
3.62400000e+03, 7.09600000e+03, 5.97700000e+03,
2.21900000e+03, 2.00000000e+02, 5.00000000e+00,
0.00000000e+00]),
array([ 1.00000000e+00, 8.50000000e+01, 1.04900000e+03,
4.74800000e+03, 7.00400000e+03, 4.80400000e+03,
1.84000000e+03, 3.73000000e+02, 3.30000000e+01,
0.00000000e+00]),
array([ 12., 184., 1403., 4485., 6479., 4835., 1935., 503.,
93., 8.]),
array([ 1.00000000e+00, 1.03000000e+02, 1.13100000e+03,
4.64700000e+03, 6.47500000e+03, 4.87800000e+03,
2.26900000e+03, 4.26000000e+02, 7.00000000e+00,
0.00000000e+00]),
array([ 2.00000000e+00, 1.40000000e+02, 1.25000000e+03,
4.47200000e+03, 5.92000000e+03, 5.29000000e+03,
2.29300000e+03, 5.38000000e+02, 3.20000000e+01,
0.00000000e+00]),
array([ 0.00000000e+00, 1.00000000e+00, 4.39000000e+02,
4.49200000e+03, 7.89200000e+03, 5.29900000e+03,
1.66500000e+03, 1.46000000e+02, 3.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 1.80000000e+01, 9.96000000e+02,
4.53500000e+03, 7.55400000e+03, 4.96400000e+03,
1.70500000e+03, 1.64000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 20., 1030., 5001., 6704., 4988., 1915., 272.,
7., 0.]),
array([ 1.00000000e+00, 2.02000000e+02, 1.45500000e+03,
5.28900000e+03, 6.84100000e+03, 4.53900000e+03,
1.41700000e+03, 1.86000000e+02, 7.00000000e+00,
0.00000000e+00]),
array([ 0.00000000e+00, 1.41000000e+02, 1.49800000e+03,
5.53100000e+03, 6.63900000e+03, 4.46600000e+03,
1.49700000e+03, 1.64000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 40., 637., 3701., 6879., 5532., 2379., 622.,
139., 8.]),
array([ 0., 59., 1064., 5075., 7034., 4996., 1532., 177.,
0., 0.]),
array([ 3.00000000e+00, 1.84000000e+02, 1.65400000e+03,
5.30600000e+03, 6.93800000e+03, 4.23100000e+03,
1.40400000e+03, 2.05000000e+02, 1.20000000e+01,
0.00000000e+00]),
array([ 0., 16., 540., 3133., 6629., 6434., 2833., 343.,
9., 0.]),
array([ 0., 46., 1589., 7362., 7427., 2826., 662., 25.,
0., 0.]),
array([ 0.00000000e+00, 4.90000000e+01, 8.79000000e+02,
3.57700000e+03, 6.33100000e+03, 6.02400000e+03,
2.82000000e+03, 2.55000000e+02, 2.00000000e+00,
0.00000000e+00]),
array([ 3.00000000e+00, 1.43000000e+02, 1.22000000e+03,
4.37800000e+03, 6.45300000e+03, 5.48400000e+03,
1.89700000e+03, 3.30000000e+02, 2.90000000e+01,
0.00000000e+00]),
array([ 2.00000000e+00, 1.71000000e+02, 1.31300000e+03,
4.08200000e+03, 6.16600000e+03, 5.34200000e+03,
2.36600000e+03, 4.58000000e+02, 3.70000000e+01,
0.00000000e+00]),
array([ 0.00000000e+00, 5.30000000e+01, 1.03200000e+03,
4.40300000e+03, 6.59000000e+03, 5.58600000e+03,
2.07800000e+03, 1.94000000e+02, 1.00000000e+00,
0.00000000e+00]),
array([ 0., 70., 1054., 3963., 7577., 5390., 1770., 113.,
0., 0.]),
array([ 0., 74., 860., 3706., 6717., 5953., 2339., 288.,
0., 0.])],
array([-0.04220241, -0.03288393, -0.02356545, -0.01424696, -0.00492848,
0.00439001, 0.01370849, 0.02302697, 0.03234546, 0.04166394,
0.05098243]),
<a list of 400 Lists of Patches objects>)
In [11]:
top_output = dbn_dict['top'].get_output(theano.shared(joint_layer,borrow=True))
plt.imshow((top_output>0.8)*np.ones_like(top_output)-(top_output<0.2)*np.ones_like(top_output),interpolation='none',extent=[0,3,385,0])
plt.colorbar()
plt.axis('tight')
plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))
Out[11]:
([<matplotlib.axis.XTick at 0x12dbd2c90>,
<matplotlib.axis.XTick at 0x12dd04e10>,
<matplotlib.axis.XTick at 0x12dab8a90>],
<a list of 3 Text xticklabel objects>)
In [12]:
plt.imshow(top_output, interpolation='none',extent=[0,3,385,0],cmap='gray')
plt.axis('tight')
plt.colorbar()
plt.xticks(np.arange(0.5,3.5,1),('0','1','2'))
Out[12]:
([<matplotlib.axis.XTick at 0x132f8e090>,
<matplotlib.axis.XTick at 0x12e7e4e50>,
<matplotlib.axis.XTick at 0x12f0ba810>],
<a list of 3 Text xticklabel objects>)
In [13]:
plt.hist(top_output)
Out[13]:
([array([ 135., 1., 1., 1., 0., 0., 1., 2., 5., 24.]),
array([ 137., 0., 0., 0., 3., 0., 1., 0., 1., 28.]),
array([ 129., 0., 0., 0., 0., 0., 0., 2., 1., 38.])],
array([ 9.31479253e-26, 9.99985456e-02, 1.99997091e-01,
2.99995637e-01, 3.99994183e-01, 4.99992728e-01,
5.99991274e-01, 6.99989820e-01, 7.99988365e-01,
8.99986911e-01, 9.99985456e-01]),
<a list of 3 Lists of Patches objects>)
In [14]:
code = (top_output[:,0:3] > 0.5) * np.ones_like(top_output[:,0:3])
In [15]:
from utils import find_unique_classes
U = find_unique_classes(code)
cl = U[0]
In [16]:
max_cl = np.max(cl)
plt.hist(cl,bins=np.arange(-0.5,max_cl + 1.5,1))
Out[16]:
(array([ 73., 41., 24., 26., 6.]),
array([-0.5, 0.5, 1.5, 2.5, 3.5, 4.5]),
<a list of 5 Patch objects>)
In [17]:
import csv
import gzip
id=[]
filename='../data/'+datafiles['ME']
if filename.endswith('.gz'):
openfun=gzip.open
else:
openfun=open
with openfun(filename) as f:
my_csv = csv.reader(f,delimiter='\t')
id = my_csv.next()
In [18]:
stat={}
with open('../data/AML/AML_clinical_data2.csv') as f:
reader = csv.reader(f, delimiter=',')
for row in reader:
patient_id=row[0]
stat[patient_id]=(row[4],row[7],row[6])
In [19]:
import re
time_list = []
event_list = []
group_list = []
DEAD=1
ALIVE=0
print('The following case IDs were not found in clinical data')
for index, key in enumerate(id[1:]):
m = re.match('TCGA-\w+-\d+', key)
patient_id = m.group(0)
if patient_id in stat:
patient_stat = stat[patient_id]
add_group = True
try:
time_list.append(float(patient_stat[2]))
event_list.append(DEAD)
except ValueError:
try:
time_list.append(float(patient_stat[1]))
event_list.append(ALIVE)
except ValueError:
print('No data for %s' % patient_id)
add_group = False
if add_group:
group_list.append(cl[index])
else:
print(patient_id)
The following case IDs were not found in clinical data
No data for TCGA-AB-2887
No data for TCGA-AB-2891
No data for TCGA-AB-2918
No data for TCGA-AB-2921
No data for TCGA-AB-2930
No data for TCGA-AB-2940
No data for TCGA-AB-2943
No data for TCGA-AB-2946
No data for TCGA-AB-2975
In [20]:
from lifelines import KaplanMeierFitter
kmf = KaplanMeierFitter()
kmf.fit(time_list,event_observed=event_list)
kmf.plot()
Out[20]:
<matplotlib.axes._subplots.AxesSubplot at 0x134585e90>
In [21]:
T=np.array(time_list)
E=np.array(event_list)
ix = (np.array(group_list) == 0)
kmf.fit(T[ix], E[ix], label='group 0')
ax=kmf.plot()
for i in range(1,4):
ix=(np.array(group_list)==i)
kmf.fit(T[ix], E[ix], label='group %d' % i)
kmf.plot(ax=ax)
Content source: glgerard/cloud-mdbn
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