In [35]:
import getpass
from transmart_api import TransmartApi
api = TransmartApi(
host = 'http://localhost:8080',
user = raw_input('Username:'),
password = getpass.getpass('Password:'))
api.access()
Username:admin
Password:········
Out[35]:
'SUCCESS'
In [36]:
import pandas
pandas.set_option('max_colwidth', 1000)
pandas.set_option("display.max_rows",100)
In [37]:
from pandas.io.json import json_normalize
obs = api.get_observations(study = 'GSE8581')
obs_df1 = json_normalize(obs)
obs_df2 = obs_df1.pivot(index = 'subject.inTrialId', columns = 'label', values = 'value')
obs_df3 = obs_df2.convert_objects()
obs_df4 = obs_df3.rename(
columns = lambda c: c.replace('\Public Studies\GSE8581\\', '')[:-1],
inplace = False)
obs_df4
Out[37]:
label
Biomarker Data\GPL570
Endpoints\Diagnosis
Endpoints\FEV1
Endpoints\Forced Expiratory Volume Ratio
Subjects\Age
Subjects\Height (inch)
Subjects\Lung Disease
Subjects\Organism
Subjects\Race
Subjects\Sex
subject.inTrialId
GSE8581GSM210004
None
non-small cell squamous cell carcinoma
2.54
58.00
63
72
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM210005
None
non-small cell adenocarcinoma
1.69
83.66
84
60
control
Homo sapiens
Afro American
female
GSE8581GSM210006
None
non-small cell adenocarcinoma
1.41
51.00
65
66
chronic obstructive pulmonary disease
Homo sapiens
Afro American
female
GSE8581GSM210007
E
non-small cell adenocarcinoma
2.51
80.96
46
66
not specified
Homo sapiens
Caucasian
male
GSE8581GSM210008
E
non-small cell adenocarcinoma
1.64
57.00
53
65
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
female
GSE8581GSM210009
E
non-small cell squamous cell carcinoma
2.72
74.00
53
64
control
Homo sapiens
Caucasian
female
GSE8581GSM210010
E
non-small cell adenocarcinoma
1.45
73.00
77
63
not specified
Homo sapiens
Caucasian
female
GSE8581GSM210011
E
non-small cell squamous cell carcinoma
1.87
56.00
56
72
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM210012
E
non-small cell adenocarcinoma
2.76
70.58
61
69
not specified
Homo sapiens
Caucasian
male
GSE8581GSM210014
E
non-small cell adenocarcinoma
1.98
78.00
71
63
control
Homo sapiens
Caucasian
female
GSE8581GSM210015
E
non-small cell adenocarcinoma
2.59
74.00
68
65
control
Homo sapiens
Caucasian
male
GSE8581GSM210071
E
emphysema
1.16
43.00
68
67
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM210087
E
non-small cell adenocarcinoma
2.59
74.00
68
65
control
Homo sapiens
Caucasian
female
GSE8581GSM210090
E
hematoma
4.08
68.00
50
72
not specified
Homo sapiens
Caucasian
male
GSE8581GSM210188
E
metastatic non-small cell adenocarcinoma
1.20
72.00
65
63
not specified
Homo sapiens
Caucasian
female
GSE8581GSM210192
E
non-small cell adenocarcinoma
2.31
75.00
50
65
control
Homo sapiens
Caucasian
female
GSE8581GSM210193
E
Unknown
1.63
80.78
67
66
not specified
Homo sapiens
Caucasian
female
GSE8581GSM210194
E
non-small cell squamous cell carcinoma
0.52
58.00
56
60
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
female
GSE8581GSM210196
E
non-small cell adenocarcinoma
2.30
74.00
57
65
control
Homo sapiens
Caucasian
female
GSE8581GSM210978
E
non-small cell adenocarcinoma
1.53
74.00
73
66
not specified
Homo sapiens
Caucasian
female
GSE8581GSM210979
E
non-small cell adenocarcinoma
2.17
76.00
55
63
control
Homo sapiens
Caucasian
female
GSE8581GSM210992
E
non-small cell squamous cell carcinoma
0.90
55.90
70
71
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM210993
E
giant bullae
0.40
45.00
61
73
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM210994
E
carcinoid
1.69
76.00
64
63
not specified
Homo sapiens
Caucasian
female
GSE8581GSM211007
E
non-small cell squamous cell carcinoma
1.70
59.00
61
66
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM211008
E
non-small cell adenocarcinoma
3.66
72.00
71
71
control
Homo sapiens
Caucasian
male
GSE8581GSM211009
E
non-small cell adenocarcinoma
1.64
41.00
75
71
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM211010
E
non-small cell adenocarcinoma
1.44
86.00
61
60
not specified
Homo sapiens
Caucasian
female
GSE8581GSM211865
E
non-small cell squamous cell carcinoma
2.13
71.00
69
67
not specified
Homo sapiens
Caucasian
male
GSE8581GSM211872
E
non-small cell squamous cell carcinoma
2.61
70.00
74
70
not specified
Homo sapiens
Caucasian
male
GSE8581GSM212067
E
inflammation
4.04
79.00
55
69
control
Homo sapiens
Caucasian
male
GSE8581GSM212068
E
metastatic renal cell carcinoma
1.99
85.00
78
63
control
Homo sapiens
Caucasian
female
GSE8581GSM212069
E
no malignancy
1.60
77.00
81
66
not specified
Homo sapiens
Caucasian
female
GSE8581GSM212070
E
carcinoid
3.70
81.86
40
67
control
Homo sapiens
Caucasian
male
GSE8581GSM212074
E
non-small cell squamous cell carcinoma
2.06
54.00
64
65
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM212075
E
non-small cell squamous cell carcinoma
1.30
78.00
79
63
chronic obstructive pulmonary disease
Homo sapiens
Afro American
female
GSE8581GSM212787
E
NSC-Mixed
3.66
78.00
78
70
control
Homo sapiens
Caucasian
male
GSE8581GSM212788
E
non-small cell squamous cell carcinoma
1.29
53.00
77
67
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
female
GSE8581GSM212789
E
non-small cell squamous cell carcinoma
1.94
86.00
62
62
control
Homo sapiens
Caucasian
female
GSE8581GSM212790
E
non-small cell squamous cell carcinoma
2.83
75.00
54
75
control
Homo sapiens
Caucasian
female
GSE8581GSM212809
E
non-small cell adenocarcinoma
1.28
89.00
61
65
not specified
Homo sapiens
Caucasian
female
GSE8581GSM212810
E
non-small cell squamous cell carcinoma
0.88
52.00
52
66
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
female
GSE8581GSM212811
E
non-small cell squamous cell carcinoma
1.37
82.00
77
58
control
Homo sapiens
Caucasian
female
GSE8581GSM212848
E
non-small cell squamous cell carcinoma
0.55
44.00
59
58
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
female
GSE8581GSM212849
E
non-small cell adenocarcinoma
2.73
80.00
39
73
not specified
Homo sapiens
Caucasian
male
GSE8581GSM212850
E
carcinoid
2.32
76.00
71
69
not specified
Homo sapiens
Caucasian
male
GSE8581GSM212852
E
Giant Cell Tumor
0.99
63.00
72
60
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
female
GSE8581GSM212853
E
lymphoma
2.42
80.00
71
66
control
Homo sapiens
Caucasian
female
GSE8581GSM212854
E
non-small cell adenocarcinoma
2.26
83.00
64
66
not specified
Homo sapiens
Caucasian
male
GSE8581GSM212855
E
non-small cell adenocarcinoma
2.43
69.00
73
68
not specified
Homo sapiens
Caucasian
male
GSE8581GSM213017
E
non-small cell adenocarcinoma
2.12
73.00
79
69
not specified
Homo sapiens
Caucasian
male
GSE8581GSM213018
E
non-small cell adenocarcinoma
2.34
81.00
82
68
not specified
Homo sapiens
Caucasian
male
GSE8581GSM213019
E
non-small cell adenocarcinoma
1.70
72.00
71
71
not specified
Homo sapiens
Caucasian
female
GSE8581GSM213020
E
non-small cell adenocarcinoma
0.67
42.00
75
63
chronic obstructive pulmonary disease
Homo sapiens
Caucasian
male
GSE8581GSM213034
E
non-small cell adenocarcinoma
2.56
61.00
70
69
not specified
Homo sapiens
Caucasian
male
GSE8581GSM213035
E
non-small cell squamous cell carcinoma
3.11
73.00
67
71
control
Homo sapiens
Caucasian
male
GSE8581GSM213036
E
non-small cell squamous cell carcinoma
2.68
71.00
59
69
control
Homo sapiens
Caucasian
female
GSE8581GSM213037
E
non-small cell squamous cell carcinoma
2.25
72.00
77
66
not specified
Homo sapiens
Caucasian
male
In [38]:
control = obs_df4['Subjects\Lung Disease'] == 'control'
treatment = obs_df4['Subjects\Lung Disease'] == 'chronic obstructive pulmonary disease'
In [39]:
obs_df4[control].describe()
Out[39]:
Endpoints\FEV1
Endpoints\Forced Expiratory Volume Ratio
Subjects\Age
Subjects\Height (inch)
count
19.000000
19.000000
19.000000
19.000000
mean
2.618421
77.448421
64.105263
65.842105
std
0.737219
4.540892
11.503114
4.193423
min
1.370000
71.000000
40.000000
58.000000
25%
2.080000
74.000000
55.000000
63.000000
50%
2.590000
76.000000
67.000000
65.000000
75%
2.970000
80.930000
71.000000
69.000000
max
4.040000
86.000000
84.000000
75.000000
In [40]:
obs_df4[treatment].describe()
Out[40]:
Endpoints\FEV1
Endpoints\Forced Expiratory Volume Ratio
Subjects\Age
Subjects\Height (inch)
count
17.000000
17.000000
17.000000
17.000000
mean
1.265882
53.523529
65.058824
66.176471
std
0.594433
9.205130
8.554754
4.530939
min
0.400000
41.000000
52.000000
58.000000
25%
0.880000
45.000000
59.000000
63.000000
50%
1.290000
54.000000
64.000000
66.000000
75%
1.640000
58.000000
72.000000
71.000000
max
2.540000
78.000000
79.000000
73.000000
In [41]:
(hdHeader, hdRows) = api.get_hd_node_data(study = 'GSE8581',
node_name = 'Lung',
projection='zscore')
In [42]:
rows = [row.value[0].doubleValue._values for row in hdRows if row.bioMarker != 'null']
row_names = [row.bioMarker + "\\" + row.label for row in hdRows if row.bioMarker != 'null']
col_names = [assay.patientId for assay in hdHeader.assay]
In [43]:
from pandas import DataFrame
hd_df = DataFrame(rows, columns = col_names, index = row_names)
hd_df
Out[43]:
GSE8581GSM213034
GSE8581GSM212811
GSE8581GSM213036
GSE8581GSM212075
GSE8581GSM211008
GSE8581GSM210090
GSE8581GSM212855
GSE8581GSM212070
GSE8581GSM212810
GSE8581GSM210193
...
GSE8581GSM210979
GSE8581GSM213017
GSE8581GSM211007
GSE8581GSM210192
GSE8581GSM213020
GSE8581GSM210994
GSE8581GSM210010
GSE8581GSM210011
GSE8581GSM210007
GSE8581GSM213019
KIAA1377\235956_at
-2.374995e-01
7.941626e-01
0.222018
1.117909
6.363410e-01
-4.181891e-01
-1.823707e+00
1.354521
-2.500000
-2.198561e-02
...
1.234281e+00
-1.578853e-01
0.642150
-0.824503
-1.418721e-01
7.586432e-01
-1.102181e+00
-1.168079e+00
-6.064866e-01
2.864873e-01
ZNF358\226260_x_at
4.708822e-01
-2.500000e+00
0.551998
0.298824
-7.286189e-01
4.696274e-01
-2.807687e-01
0.299635
-2.500000
-3.315997e-01
...
3.100759e-01
1.628198e-01
0.695030
0.129990
1.036752e-01
-1.037046e+00
-3.647481e-01
3.708714e-01
-3.854062e-01
1.054395e+00
GPR135\214503_x_at
6.308876e-02
-1.971560e+00
0.429403
-0.606546
-7.913499e-17
-2.291924e-01
1.167696e+00
1.319348
-2.267266
9.553151e-01
...
5.955446e-01
6.090966e-01
1.238395
-0.078148
9.911758e-01
-7.427790e-01
-7.761581e-02
4.237702e-01
-2.863704e-01
-7.628633e-01
GPATCH2\242224_at
1.101496e-01
-3.358226e-01
-0.656001
-0.014406
-2.024513e-01
-1.037138e-02
-1.055835e+00
0.522693
-2.500000
4.395237e-01
...
3.377338e-01
-1.245019e+00
0.809489
0.112782
1.517590e-01
5.940620e-01
-1.477183e-01
-1.779910e-01
1.171425e-01
1.539858e-01
RPL24\214143_x_at
1.868383e-01
1.476438e-02
0.003760
-0.026180
5.286380e-02
-2.765255e-15
1.267947e-01
0.125205
-2.500000
-8.126549e-02
...
5.964594e-02
-5.714644e-02
0.044090
-0.187937
1.330189e-01
1.035764e-01
-1.438904e-01
-2.244213e-01
-3.951937e-01
3.198973e-01
PALM2\1554640_at
-1.460607e-01
-2.671897e-01
-0.625874
0.321917
-2.148522e+00
-1.406787e+00
1.648780e+00
1.031431
-2.370058
-1.142304e-01
...
7.204935e-01
6.870224e-01
1.292842
-0.257120
5.744479e-01
3.985112e-01
4.682653e-02
2.494825e-01
1.336069e-01
9.463886e-01
LOC339316\242084_at
-8.695599e-02
-1.501149e-01
2.208113
-0.725581
1.596191e+00
5.343815e-02
1.648072e-01
0.454762
1.022814
-8.821926e-01
...
1.952618e-01
-6.714563e-01
2.500000
-0.980878
0.000000e+00
4.029939e-01
-8.271509e-01
-6.114075e-01
2.050778e+00
2.693141e-01
FECH\203115_at
3.809356e-01
1.352984e-01
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3.856423e-01
1.784519e-01
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0.720528
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2.179938e-01
...
1.106194e+00
-1.906898e-01
0.315945
-0.107265
-4.421974e-01
2.162255e-01
-9.024499e-02
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2.732963e-01
6.115749e-01
KCNJ9\207527_at
2.446787e-01
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0.221697
0.497712
-2.342015e-02
8.869240e-01
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0.204983
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1.110048e+00
...
7.171176e-01
9.340802e-02
0.381381
-0.367554
-1.282485e-15
5.520371e-01
3.760669e-01
-1.293106e+00
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6.951946e-01
C21orf96\220918_at
6.877719e-01
4.009709e-01
0.859799
1.189436
-1.291472e+00
-4.096633e-01
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0.340609
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6.170242e-01
...
1.322267e-01
-2.444516e+00
0.736984
-0.937333
1.411358e-01
-8.504788e-01
1.726166e-02
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-8.424365e-01
1.258965e+00
SAT2\225272_at
5.220952e-01
3.603069e-01
0.197867
0.709173
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...
-4.467455e-01
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-7.275942e-01
1.857135e-01
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5.608342e-01
EHD1\209038_s_at
-1.151206e-01
7.838067e-01
0.384543
0.581821
1.054911e-01
2.080865e-01
7.126174e-01
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-5.027341e-02
...
2.102385e-01
-9.875349e-01
-1.849258
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4.666680e-01
-2.500000e+00
-6.914566e-01
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-1.043725e-01
3.922175e-01
CBLB\208348_s_at
7.738033e-01
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0.082362
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1.340361e+00
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...
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8.274782e-01
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0.570401
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1.757159e-01
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5.032972e-01
1.024542e+00
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C20orf85\229542_at
-2.908508e-03
5.987500e-01
0.254282
0.753694
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3.341133e-02
-1.511821e+00
0.860719
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-2.410139e-01
...
-8.473255e-16
8.755296e-02
0.088931
-0.375043
2.624213e-01
-6.056148e-02
-1.464985e+00
6.121084e-02
1.220506e-01
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UGGT1\218257_s_at
1.782258e-01
-1.137344e-01
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0.513928
3.984019e-01
6.856900e-01
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4.515102e-01
...
4.807792e-01
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0.321934
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7.535896e-02
1.116358e-01
1.440283e-01
2.094979e-03
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LOC283585\1561442_at
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0.700004
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1.482911e+00
8.532391e-01
4.876661e-02
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4.831032e-01
...
3.031844e-01
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0.973158
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1.243125e+00
9.759445e-01
7.292454e-02
6.582480e-01
3.560563e-01
1.075439e+00
EMP2\204975_at
-1.648862e-02
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0.105847
2.403381e-02
2.640703e-01
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0.435395
-2.500000
-1.813071e-01
...
3.241176e-01
4.882364e-02
0.215725
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4.261064e-01
1.583930e-01
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2.027803e-01
LHFP\218656_s_at
-1.672177e-01
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0.082007
-0.039112
2.256442e-01
-1.412568e-15
-1.941996e-01
0.587759
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6.075659e-01
...
6.274778e-01
-2.734732e-01
0.124004
0.031477
2.239015e-01
4.204985e-01
2.847553e-02
-6.270991e-01
-2.628937e-01
3.536542e-01
SNCAIP\237834_at
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In [44]:
design_df = DataFrame({'control': control[col_names], 'treatment': treatment[col_names]})
design_df
Out[44]:
control
treatment
subject.inTrialId
GSE8581GSM213034
False
False
GSE8581GSM212811
True
False
GSE8581GSM213036
True
False
GSE8581GSM212075
False
True
GSE8581GSM211008
True
False
GSE8581GSM210090
False
False
GSE8581GSM212855
False
False
GSE8581GSM212070
True
False
GSE8581GSM212810
False
True
GSE8581GSM210193
False
False
GSE8581GSM210071
False
True
GSE8581GSM212069
False
False
GSE8581GSM212849
False
False
GSE8581GSM212787
True
False
GSE8581GSM210015
True
False
GSE8581GSM212853
True
False
GSE8581GSM212790
True
False
GSE8581GSM212067
True
False
GSE8581GSM211865
False
False
GSE8581GSM210196
True
False
GSE8581GSM212809
False
False
GSE8581GSM210992
False
True
GSE8581GSM211009
False
True
GSE8581GSM210014
True
False
GSE8581GSM213018
False
False
GSE8581GSM212789
True
False
GSE8581GSM210978
False
False
GSE8581GSM213037
False
False
GSE8581GSM212074
False
True
GSE8581GSM210993
False
True
GSE8581GSM210087
True
False
GSE8581GSM212068
True
False
GSE8581GSM210012
False
False
GSE8581GSM212854
False
False
GSE8581GSM212852
False
True
GSE8581GSM212848
False
True
GSE8581GSM211010
False
False
GSE8581GSM213035
True
False
GSE8581GSM210194
False
True
GSE8581GSM210008
False
True
GSE8581GSM212788
False
True
GSE8581GSM211872
False
False
GSE8581GSM212850
False
False
GSE8581GSM210188
False
False
GSE8581GSM210009
True
False
GSE8581GSM210979
True
False
GSE8581GSM213017
False
False
GSE8581GSM211007
False
True
GSE8581GSM210192
True
False
GSE8581GSM213020
False
True
GSE8581GSM210994
False
False
GSE8581GSM210010
False
False
GSE8581GSM210011
False
True
GSE8581GSM210007
False
False
GSE8581GSM213019
False
False
In [45]:
%load_ext rpy2.ipython
The rpy2.ipython extension is already loaded. To reload it, use:
%reload_ext rpy2.ipython
In [46]:
%%R -i hd_df -i design_df -o top_fit
library(limma)
design.matrix <- sapply(design_df, as.numeric)
contrast.matrix <- makeContrasts(control - treatment, levels = design.matrix)
fit <- lmFit(hd_df, design.matrix)
fit <- contrasts.fit(fit, contrast.matrix)
fit <- eBayes(fit)
contr=1
top_fit = data.frame(
probe = rownames(fit$coefficients),
log_fc = fit$coefficients[, contr],
t = fit$t[, contr],
p_value = fit$p.value[, contr],
b = fit$lods[, contr],
stringsAsFactors = FALSE
)
volcanoplot(fit)
In [47]:
top_probes = top_fit[top_fit.p_value < 0.001] \
.set_index(['probe']).sort(['p_value'], ascending = True)
top_probes
Out[47]:
log_fc
t
p_value
b
probe
DNM3\1558502_s_at
1.375778
4.806308
0.000009
1.945957
CASC4\1552719_at
1.130824
4.687018
0.000014
1.650206
ZNF30\232014_at
1.224315
4.522312
0.000026
1.246204
LOC389634\1560119_at
1.204393
4.320760
0.000053
0.759543
RP9\227852_at
0.921346
4.312707
0.000055
0.740289
SLITRK6\232481_s_at
1.113788
4.223494
0.000075
0.528059
AGTPBP1\204499_at
1.030825
4.211223
0.000078
0.499022
LOC389906\1558045_a_at
1.219038
4.182897
0.000086
0.432140
GFRA2\205722_s_at
1.246157
4.171039
0.000090
0.404202
IL1F8\224230_at
-1.228913
-4.110874
0.000111
0.263023
KLF8\230986_at
1.056070
4.102448
0.000114
0.243327
RGS8\234297_at
1.093250
4.083148
0.000122
0.198289
STRBP\223246_s_at
1.181890
4.008383
0.000157
0.024786
KIAA1377\236325_at
1.056305
3.958482
0.000186
-0.090131
NAPEPLD\242635_s_at
1.170465
3.953391
0.000190
-0.101814
EGFEM1P\1558411_at
1.012719
3.919803
0.000212
-0.178704
ATF7IP2\219870_at
1.012826
3.880784
0.000242
-0.267603
LOC644192\232878_at
1.116624
3.875276
0.000247
-0.280114
LOC100508546\244037_at
1.070067
3.866795
0.000254
-0.299360
RUNX1T1\216831_s_at
1.200380
3.863911
0.000256
-0.305900
ANGEL1\244685_at
-1.170453
-3.844109
0.000274
-0.350735
SEMA6D\226492_at
0.998893
3.835239
0.000282
-0.370778
C9orf85\235866_at
1.025476
3.811178
0.000305
-0.425026
HHEX\215933_s_at
0.876758
3.788431
0.000329
-0.476140
CLDN15\1552845_at
1.070289
3.787280
0.000330
-0.478723
KLHL24\206551_x_at
0.886946
3.767771
0.000352
-0.522422
NHLRC3\236953_s_at
0.998617
3.766816
0.000353
-0.524558
ELMOD1\231930_at
1.035745
3.735181
0.000392
-0.595148
ARHGAP29\1558280_s_at
0.960541
3.717077
0.000416
-0.635398
MGC12488\211386_at
0.925050
3.713993
0.000420
-0.642242
GOLM1\1558248_at
1.133981
3.710102
0.000426
-0.650875
MTHFSD\230442_at
1.004106
3.708857
0.000427
-0.653636
LOC400590\1560992_at
1.137758
3.707311
0.000430
-0.657063
LOC729799\217667_at
1.135771
3.706478
0.000431
-0.658910
ZNF334\220022_at
1.084147
3.684612
0.000463
-0.707301
ZFP82\235176_at
0.920436
3.675480
0.000477
-0.727463
AK2\212172_at
1.019343
3.665957
0.000492
-0.748457
FAM173B\225668_at
1.061109
3.661258
0.000499
-0.758805
ZFP14\232911_at
0.892159
3.647028
0.000523
-0.790097
GTF2H2B\215470_at
0.959996
3.640101
0.000534
-0.805306
LOC339047\1558075_at
0.882082
3.629213
0.000554
-0.829174
CREBZF\225595_at
0.935700
3.626421
0.000559
-0.835290
LOC286272\232182_at
0.930666
3.622087
0.000566
-0.844775
LOC100507100\230187_s_at
1.060942
3.620916
0.000569
-0.847338
GATAD2B\238076_at
0.738321
3.617144
0.000576
-0.855586
ABCA8\1565778_at
1.045979
3.615368
0.000579
-0.859468
PTPRB\1560105_at
1.022702
3.613720
0.000582
-0.863069
ZNRF1\225962_at
0.900862
3.611676
0.000586
-0.867536
GPATCH8\212487_at
0.830247
3.561935
0.000687
-0.975750
TSC2\215624_at
1.116368
3.557750
0.000696
-0.984815
EDA\211127_x_at
1.070483
3.555175
0.000702
-0.990390
LOC441259\1552622_s_at
0.897353
3.551402
0.000711
-0.998555
CASP8AP2\1570001_at
1.032101
3.547817
0.000719
-1.006305
CTBP2\215377_at
1.018589
3.534816
0.000749
-1.034381
CACNA1D\243334_at
1.026184
3.529423
0.000762
-1.046007
FBXL13\1556770_a_at
0.945425
3.525738
0.000771
-1.053947
RRP9\204133_at
0.998497
3.518901
0.000788
-1.068664
ZNF521\226676_at
0.793170
3.518051
0.000790
-1.070491
ITGB1BP2\219829_at
1.042978
3.514850
0.000798
-1.077375
MAGIX\219965_s_at
1.088856
3.492431
0.000857
-1.125480
FOXJ1\205906_at
1.039986
3.492429
0.000857
-1.125482
LOC644613\1563426_a_at
1.035299
3.487102
0.000871
-1.136885
EFCAB7\226800_at
0.884007
3.483128
0.000882
-1.145385
LOC100294362\1569078_at
0.984446
3.475146
0.000905
-1.162441
GTPBP10\230684_at
1.062098
3.469254
0.000922
-1.175013
ZNF440\215892_at
0.901321
3.466112
0.000931
-1.181715
WDR52\221103_s_at
0.991380
3.461480
0.000945
-1.191585
NCRNA00182\1558515_at
0.838394
3.460203
0.000948
-1.194305
CXorf56\235442_at
0.995492
3.457467
0.000956
-1.200130
TOP3B\215781_s_at
0.946690
3.447509
0.000987
-1.221308
In [48]:
def get_gene_name(probe):
return probe.split('\\')[0]
top_probes['gene_name'] = top_probes.index.map(get_gene_name)
top_probes
Out[48]:
log_fc
t
p_value
b
gene_name
probe
DNM3\1558502_s_at
1.375778
4.806308
0.000009
1.945957
DNM3
CASC4\1552719_at
1.130824
4.687018
0.000014
1.650206
CASC4
ZNF30\232014_at
1.224315
4.522312
0.000026
1.246204
ZNF30
LOC389634\1560119_at
1.204393
4.320760
0.000053
0.759543
LOC389634
RP9\227852_at
0.921346
4.312707
0.000055
0.740289
RP9
SLITRK6\232481_s_at
1.113788
4.223494
0.000075
0.528059
SLITRK6
AGTPBP1\204499_at
1.030825
4.211223
0.000078
0.499022
AGTPBP1
LOC389906\1558045_a_at
1.219038
4.182897
0.000086
0.432140
LOC389906
GFRA2\205722_s_at
1.246157
4.171039
0.000090
0.404202
GFRA2
IL1F8\224230_at
-1.228913
-4.110874
0.000111
0.263023
IL1F8
KLF8\230986_at
1.056070
4.102448
0.000114
0.243327
KLF8
RGS8\234297_at
1.093250
4.083148
0.000122
0.198289
RGS8
STRBP\223246_s_at
1.181890
4.008383
0.000157
0.024786
STRBP
KIAA1377\236325_at
1.056305
3.958482
0.000186
-0.090131
KIAA1377
NAPEPLD\242635_s_at
1.170465
3.953391
0.000190
-0.101814
NAPEPLD
EGFEM1P\1558411_at
1.012719
3.919803
0.000212
-0.178704
EGFEM1P
ATF7IP2\219870_at
1.012826
3.880784
0.000242
-0.267603
ATF7IP2
LOC644192\232878_at
1.116624
3.875276
0.000247
-0.280114
LOC644192
LOC100508546\244037_at
1.070067
3.866795
0.000254
-0.299360
LOC100508546
RUNX1T1\216831_s_at
1.200380
3.863911
0.000256
-0.305900
RUNX1T1
ANGEL1\244685_at
-1.170453
-3.844109
0.000274
-0.350735
ANGEL1
SEMA6D\226492_at
0.998893
3.835239
0.000282
-0.370778
SEMA6D
C9orf85\235866_at
1.025476
3.811178
0.000305
-0.425026
C9orf85
HHEX\215933_s_at
0.876758
3.788431
0.000329
-0.476140
HHEX
CLDN15\1552845_at
1.070289
3.787280
0.000330
-0.478723
CLDN15
KLHL24\206551_x_at
0.886946
3.767771
0.000352
-0.522422
KLHL24
NHLRC3\236953_s_at
0.998617
3.766816
0.000353
-0.524558
NHLRC3
ELMOD1\231930_at
1.035745
3.735181
0.000392
-0.595148
ELMOD1
ARHGAP29\1558280_s_at
0.960541
3.717077
0.000416
-0.635398
ARHGAP29
MGC12488\211386_at
0.925050
3.713993
0.000420
-0.642242
MGC12488
GOLM1\1558248_at
1.133981
3.710102
0.000426
-0.650875
GOLM1
MTHFSD\230442_at
1.004106
3.708857
0.000427
-0.653636
MTHFSD
LOC400590\1560992_at
1.137758
3.707311
0.000430
-0.657063
LOC400590
LOC729799\217667_at
1.135771
3.706478
0.000431
-0.658910
LOC729799
ZNF334\220022_at
1.084147
3.684612
0.000463
-0.707301
ZNF334
ZFP82\235176_at
0.920436
3.675480
0.000477
-0.727463
ZFP82
AK2\212172_at
1.019343
3.665957
0.000492
-0.748457
AK2
FAM173B\225668_at
1.061109
3.661258
0.000499
-0.758805
FAM173B
ZFP14\232911_at
0.892159
3.647028
0.000523
-0.790097
ZFP14
GTF2H2B\215470_at
0.959996
3.640101
0.000534
-0.805306
GTF2H2B
LOC339047\1558075_at
0.882082
3.629213
0.000554
-0.829174
LOC339047
CREBZF\225595_at
0.935700
3.626421
0.000559
-0.835290
CREBZF
LOC286272\232182_at
0.930666
3.622087
0.000566
-0.844775
LOC286272
LOC100507100\230187_s_at
1.060942
3.620916
0.000569
-0.847338
LOC100507100
GATAD2B\238076_at
0.738321
3.617144
0.000576
-0.855586
GATAD2B
ABCA8\1565778_at
1.045979
3.615368
0.000579
-0.859468
ABCA8
PTPRB\1560105_at
1.022702
3.613720
0.000582
-0.863069
PTPRB
ZNRF1\225962_at
0.900862
3.611676
0.000586
-0.867536
ZNRF1
GPATCH8\212487_at
0.830247
3.561935
0.000687
-0.975750
GPATCH8
TSC2\215624_at
1.116368
3.557750
0.000696
-0.984815
TSC2
EDA\211127_x_at
1.070483
3.555175
0.000702
-0.990390
EDA
LOC441259\1552622_s_at
0.897353
3.551402
0.000711
-0.998555
LOC441259
CASP8AP2\1570001_at
1.032101
3.547817
0.000719
-1.006305
CASP8AP2
CTBP2\215377_at
1.018589
3.534816
0.000749
-1.034381
CTBP2
CACNA1D\243334_at
1.026184
3.529423
0.000762
-1.046007
CACNA1D
FBXL13\1556770_a_at
0.945425
3.525738
0.000771
-1.053947
FBXL13
RRP9\204133_at
0.998497
3.518901
0.000788
-1.068664
RRP9
ZNF521\226676_at
0.793170
3.518051
0.000790
-1.070491
ZNF521
ITGB1BP2\219829_at
1.042978
3.514850
0.000798
-1.077375
ITGB1BP2
MAGIX\219965_s_at
1.088856
3.492431
0.000857
-1.125480
MAGIX
FOXJ1\205906_at
1.039986
3.492429
0.000857
-1.125482
FOXJ1
LOC644613\1563426_a_at
1.035299
3.487102
0.000871
-1.136885
LOC644613
EFCAB7\226800_at
0.884007
3.483128
0.000882
-1.145385
EFCAB7
LOC100294362\1569078_at
0.984446
3.475146
0.000905
-1.162441
LOC100294362
GTPBP10\230684_at
1.062098
3.469254
0.000922
-1.175013
GTPBP10
ZNF440\215892_at
0.901321
3.466112
0.000931
-1.181715
ZNF440
WDR52\221103_s_at
0.991380
3.461480
0.000945
-1.191585
WDR52
NCRNA00182\1558515_at
0.838394
3.460203
0.000948
-1.194305
NCRNA00182
CXorf56\235442_at
0.995492
3.457467
0.000956
-1.200130
CXorf56
TOP3B\215781_s_at
0.946690
3.447509
0.000987
-1.221308
TOP3B
In [49]:
from utils import Entrez
entrez = Entrez()
top_probes['gene_id'] = top_probes.gene_name.map(entrez.get_gene_id)
top_probes
Out[49]:
log_fc
t
p_value
b
gene_name
gene_id
probe
DNM3\1558502_s_at
1.375778
4.806308
0.000009
1.945957
DNM3
26052
CASC4\1552719_at
1.130824
4.687018
0.000014
1.650206
CASC4
113201
ZNF30\232014_at
1.224315
4.522312
0.000026
1.246204
ZNF30
90075
LOC389634\1560119_at
1.204393
4.320760
0.000053
0.759543
LOC389634
389634
RP9\227852_at
0.921346
4.312707
0.000055
0.740289
RP9
6100
SLITRK6\232481_s_at
1.113788
4.223494
0.000075
0.528059
SLITRK6
84189
AGTPBP1\204499_at
1.030825
4.211223
0.000078
0.499022
AGTPBP1
23287
LOC389906\1558045_a_at
1.219038
4.182897
0.000086
0.432140
LOC389906
389906
GFRA2\205722_s_at
1.246157
4.171039
0.000090
0.404202
GFRA2
2675
IL1F8\224230_at
-1.228913
-4.110874
0.000111
0.263023
IL1F8
None
KLF8\230986_at
1.056070
4.102448
0.000114
0.243327
KLF8
11279
RGS8\234297_at
1.093250
4.083148
0.000122
0.198289
RGS8
85397
STRBP\223246_s_at
1.181890
4.008383
0.000157
0.024786
STRBP
55342
KIAA1377\236325_at
1.056305
3.958482
0.000186
-0.090131
KIAA1377
57562
NAPEPLD\242635_s_at
1.170465
3.953391
0.000190
-0.101814
NAPEPLD
222236
EGFEM1P\1558411_at
1.012719
3.919803
0.000212
-0.178704
EGFEM1P
93556
ATF7IP2\219870_at
1.012826
3.880784
0.000242
-0.267603
ATF7IP2
80063
LOC644192\232878_at
1.116624
3.875276
0.000247
-0.280114
LOC644192
644192
LOC100508546\244037_at
1.070067
3.866795
0.000254
-0.299360
LOC100508546
None
RUNX1T1\216831_s_at
1.200380
3.863911
0.000256
-0.305900
RUNX1T1
862
ANGEL1\244685_at
-1.170453
-3.844109
0.000274
-0.350735
ANGEL1
23357
SEMA6D\226492_at
0.998893
3.835239
0.000282
-0.370778
SEMA6D
80031
C9orf85\235866_at
1.025476
3.811178
0.000305
-0.425026
C9orf85
138241
HHEX\215933_s_at
0.876758
3.788431
0.000329
-0.476140
HHEX
3087
CLDN15\1552845_at
1.070289
3.787280
0.000330
-0.478723
CLDN15
24146
KLHL24\206551_x_at
0.886946
3.767771
0.000352
-0.522422
KLHL24
54800
NHLRC3\236953_s_at
0.998617
3.766816
0.000353
-0.524558
NHLRC3
387921
ELMOD1\231930_at
1.035745
3.735181
0.000392
-0.595148
ELMOD1
55531
ARHGAP29\1558280_s_at
0.960541
3.717077
0.000416
-0.635398
ARHGAP29
9411
MGC12488\211386_at
0.925050
3.713993
0.000420
-0.642242
MGC12488
84786
GOLM1\1558248_at
1.133981
3.710102
0.000426
-0.650875
GOLM1
51280
MTHFSD\230442_at
1.004106
3.708857
0.000427
-0.653636
MTHFSD
64779
LOC400590\1560992_at
1.137758
3.707311
0.000430
-0.657063
LOC400590
400590
LOC729799\217667_at
1.135771
3.706478
0.000431
-0.658910
LOC729799
729799
ZNF334\220022_at
1.084147
3.684612
0.000463
-0.707301
ZNF334
55713
ZFP82\235176_at
0.920436
3.675480
0.000477
-0.727463
ZFP82
284406
AK2\212172_at
1.019343
3.665957
0.000492
-0.748457
AK2
204
FAM173B\225668_at
1.061109
3.661258
0.000499
-0.758805
FAM173B
134145
ZFP14\232911_at
0.892159
3.647028
0.000523
-0.790097
ZFP14
57677
GTF2H2B\215470_at
0.959996
3.640101
0.000534
-0.805306
GTF2H2B
653238
LOC339047\1558075_at
0.882082
3.629213
0.000554
-0.829174
LOC339047
None
CREBZF\225595_at
0.935700
3.626421
0.000559
-0.835290
CREBZF
58487
LOC286272\232182_at
0.930666
3.622087
0.000566
-0.844775
LOC286272
286272
LOC100507100\230187_s_at
1.060942
3.620916
0.000569
-0.847338
LOC100507100
100507100
GATAD2B\238076_at
0.738321
3.617144
0.000576
-0.855586
GATAD2B
57459
ABCA8\1565778_at
1.045979
3.615368
0.000579
-0.859468
ABCA8
10351
PTPRB\1560105_at
1.022702
3.613720
0.000582
-0.863069
PTPRB
5787
ZNRF1\225962_at
0.900862
3.611676
0.000586
-0.867536
ZNRF1
84937
GPATCH8\212487_at
0.830247
3.561935
0.000687
-0.975750
GPATCH8
23131
TSC2\215624_at
1.116368
3.557750
0.000696
-0.984815
TSC2
7249
EDA\211127_x_at
1.070483
3.555175
0.000702
-0.990390
EDA
1896
LOC441259\1552622_s_at
0.897353
3.551402
0.000711
-0.998555
LOC441259
441259
CASP8AP2\1570001_at
1.032101
3.547817
0.000719
-1.006305
CASP8AP2
9994
CTBP2\215377_at
1.018589
3.534816
0.000749
-1.034381
CTBP2
1488
CACNA1D\243334_at
1.026184
3.529423
0.000762
-1.046007
CACNA1D
776
FBXL13\1556770_a_at
0.945425
3.525738
0.000771
-1.053947
FBXL13
222235
RRP9\204133_at
0.998497
3.518901
0.000788
-1.068664
RRP9
9136
ZNF521\226676_at
0.793170
3.518051
0.000790
-1.070491
ZNF521
25925
ITGB1BP2\219829_at
1.042978
3.514850
0.000798
-1.077375
ITGB1BP2
26548
MAGIX\219965_s_at
1.088856
3.492431
0.000857
-1.125480
MAGIX
79917
FOXJ1\205906_at
1.039986
3.492429
0.000857
-1.125482
FOXJ1
2302
LOC644613\1563426_a_at
1.035299
3.487102
0.000871
-1.136885
LOC644613
None
EFCAB7\226800_at
0.884007
3.483128
0.000882
-1.145385
EFCAB7
84455
LOC100294362\1569078_at
0.984446
3.475146
0.000905
-1.162441
LOC100294362
100294362
GTPBP10\230684_at
1.062098
3.469254
0.000922
-1.175013
GTPBP10
85865
ZNF440\215892_at
0.901321
3.466112
0.000931
-1.181715
ZNF440
126070
WDR52\221103_s_at
0.991380
3.461480
0.000945
-1.191585
WDR52
55779
NCRNA00182\1558515_at
0.838394
3.460203
0.000948
-1.194305
NCRNA00182
None
CXorf56\235442_at
0.995492
3.457467
0.000956
-1.200130
CXorf56
63932
TOP3B\215781_s_at
0.946690
3.447509
0.000987
-1.221308
TOP3B
8940
In [50]:
from utils import Kegg
kegg = Kegg(gene_ids = top_probes.gene_id)
top_probes['pathways_ids'] = top_probes.gene_id.map(kegg.get_pathways_image_links_by_gene)
from IPython.display import HTML
HTML(top_probes.to_html(escape=False))
Out[50]:
log_fc
t
p_value
b
gene_name
gene_id
pathways_ids
probe
DNM3\1558502_s_at
1.375778
4.806308
0.000009
1.945957
DNM3
26052
[hsa04144, hsa04721, hsa04961, hsa05100]
CASC4\1552719_at
1.130824
4.687018
0.000014
1.650206
CASC4
113201
[]
ZNF30\232014_at
1.224315
4.522312
0.000026
1.246204
ZNF30
90075
[]
LOC389634\1560119_at
1.204393
4.320760
0.000053
0.759543
LOC389634
389634
[]
RP9\227852_at
0.921346
4.312707
0.000055
0.740289
RP9
6100
[]
SLITRK6\232481_s_at
1.113788
4.223494
0.000075
0.528059
SLITRK6
84189
[]
AGTPBP1\204499_at
1.030825
4.211223
0.000078
0.499022
AGTPBP1
23287
[]
LOC389906\1558045_a_at
1.219038
4.182897
0.000086
0.432140
LOC389906
389906
[]
GFRA2\205722_s_at
1.246157
4.171039
0.000090
0.404202
GFRA2
2675
[]
IL1F8\224230_at
-1.228913
-4.110874
0.000111
0.263023
IL1F8
None
[]
KLF8\230986_at
1.056070
4.102448
0.000114
0.243327
KLF8
11279
[]
RGS8\234297_at
1.093250
4.083148
0.000122
0.198289
RGS8
85397
[]
STRBP\223246_s_at
1.181890
4.008383
0.000157
0.024786
STRBP
55342
[]
KIAA1377\236325_at
1.056305
3.958482
0.000186
-0.090131
KIAA1377
57562
[]
NAPEPLD\242635_s_at
1.170465
3.953391
0.000190
-0.101814
NAPEPLD
222236
[hsa04723]
EGFEM1P\1558411_at
1.012719
3.919803
0.000212
-0.178704
EGFEM1P
93556
[]
ATF7IP2\219870_at
1.012826
3.880784
0.000242
-0.267603
ATF7IP2
80063
[]
LOC644192\232878_at
1.116624
3.875276
0.000247
-0.280114
LOC644192
644192
[]
LOC100508546\244037_at
1.070067
3.866795
0.000254
-0.299360
LOC100508546
None
[]
RUNX1T1\216831_s_at
1.200380
3.863911
0.000256
-0.305900
RUNX1T1
862
[hsa05200, hsa05202, hsa05221]
ANGEL1\244685_at
-1.170453
-3.844109
0.000274
-0.350735
ANGEL1
23357
[]
SEMA6D\226492_at
0.998893
3.835239
0.000282
-0.370778
SEMA6D
80031
[hsa04360]
C9orf85\235866_at
1.025476
3.811178
0.000305
-0.425026
C9orf85
138241
[]
HHEX\215933_s_at
0.876758
3.788431
0.000329
-0.476140
HHEX
3087
[hsa04950, hsa05202]
CLDN15\1552845_at
1.070289
3.787280
0.000330
-0.478723
CLDN15
24146
[hsa04514, hsa04530, hsa04670, hsa05160]
KLHL24\206551_x_at
0.886946
3.767771
0.000352
-0.522422
KLHL24
54800
[]
NHLRC3\236953_s_at
0.998617
3.766816
0.000353
-0.524558
NHLRC3
387921
[]
ELMOD1\231930_at
1.035745
3.735181
0.000392
-0.595148
ELMOD1
55531
[]
ARHGAP29\1558280_s_at
0.960541
3.717077
0.000416
-0.635398
ARHGAP29
9411
[]
MGC12488\211386_at
0.925050
3.713993
0.000420
-0.642242
MGC12488
84786
[]
GOLM1\1558248_at
1.133981
3.710102
0.000426
-0.650875
GOLM1
51280
[]
MTHFSD\230442_at
1.004106
3.708857
0.000427
-0.653636
MTHFSD
64779
[]
LOC400590\1560992_at
1.137758
3.707311
0.000430
-0.657063
LOC400590
400590
[]
LOC729799\217667_at
1.135771
3.706478
0.000431
-0.658910
LOC729799
729799
[]
ZNF334\220022_at
1.084147
3.684612
0.000463
-0.707301
ZNF334
55713
[]
ZFP82\235176_at
0.920436
3.675480
0.000477
-0.727463
ZFP82
284406
[]
AK2\212172_at
1.019343
3.665957
0.000492
-0.748457
AK2
204
[hsa00230, hsa01100, hsa01130]
FAM173B\225668_at
1.061109
3.661258
0.000499
-0.758805
FAM173B
134145
[]
ZFP14\232911_at
0.892159
3.647028
0.000523
-0.790097
ZFP14
57677
[]
GTF2H2B\215470_at
0.959996
3.640101
0.000534
-0.805306
GTF2H2B
653238
[]
LOC339047\1558075_at
0.882082
3.629213
0.000554
-0.829174
LOC339047
None
[]
CREBZF\225595_at
0.935700
3.626421
0.000559
-0.835290
CREBZF
58487
[]
LOC286272\232182_at
0.930666
3.622087
0.000566
-0.844775
LOC286272
286272
[]
LOC100507100\230187_s_at
1.060942
3.620916
0.000569
-0.847338
LOC100507100
100507100
[]
GATAD2B\238076_at
0.738321
3.617144
0.000576
-0.855586
GATAD2B
57459
[]
ABCA8\1565778_at
1.045979
3.615368
0.000579
-0.859468
ABCA8
10351
[hsa02010]
PTPRB\1560105_at
1.022702
3.613720
0.000582
-0.863069
PTPRB
5787
[hsa04520]
ZNRF1\225962_at
0.900862
3.611676
0.000586
-0.867536
ZNRF1
84937
[]
GPATCH8\212487_at
0.830247
3.561935
0.000687
-0.975750
GPATCH8
23131
[]
TSC2\215624_at
1.116368
3.557750
0.000696
-0.984815
TSC2
7249
[hsa04115, hsa04150, hsa04151, hsa04152, hsa04910, hsa04919, hsa05231]
EDA\211127_x_at
1.070483
3.555175
0.000702
-0.990390
EDA
1896
[hsa04060]
LOC441259\1552622_s_at
0.897353
3.551402
0.000711
-0.998555
LOC441259
441259
[]
CASP8AP2\1570001_at
1.032101
3.547817
0.000719
-1.006305
CASP8AP2
9994
[]
CTBP2\215377_at
1.018589
3.534816
0.000749
-1.034381
CTBP2
1488
[hsa04310, hsa04330, hsa05200, hsa05220]
CACNA1D\243334_at
1.026184
3.529423
0.000762
-1.046007
CACNA1D
776
[hsa04010, hsa04020, hsa04022, hsa04024, hsa04260, hsa04261, hsa04270, hsa04713, hsa04723, hsa04724, hsa04725, hsa04726, hsa03440, hsa03460]
Content source: thehyve/transmart-api-training
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