GSEA analysis on leukemia dataset

In [1]:

    

%load_ext autoreload
%autoreload 2


    

In [2]:

    

from gsea import *
import numpy as np
%pylab


    

    




Using matplotlib backend: agg
Populating the interactive namespace from numpy and matplotlib

In [3]:

    

%matplotlib inline


    

In [4]:

    

genes, D, C = read_expression_file("data/leukemia.txt")
gene_sets, gene_set_names = read_genesets_file("data/pathways.txt", genes)
gene_set_hash = {}
for i in range(len(gene_sets)):
    gene_set_hash[gene_set_names[i][0]] = {'indexes':gene_sets[i],'desc':gene_set_names[i][1]}


    

In [5]:

    

# verify that the dimensions make sense
len(genes),D.shape,len(C)


    

    
Out[5]:





(2672, (2672, 72), 72)

In [173]:

    

L,r = rank_genes(D,C)


    

In [174]:

    

scatter(D[L[1],:],C)


    

    
Out[174]:





<matplotlib.collections.PathCollection at 0x7f6e6dc9eba8>






    

In [175]:

    

scatter(D[L[-1],:],C)


    

    
Out[175]:





<matplotlib.collections.PathCollection at 0x7f6e6dc2c9e8>






    

In [176]:

    

scatter(D[L[1000],:],C)


    

    
Out[176]:





<matplotlib.collections.PathCollection at 0x7f6e6db466d8>






    

In [16]:

    

p_exp = 1
def plot_es_calculations(name, L, r):
    S = gene_set_hash[name]['indexes']
    N = len(L)
    S_mask = np.zeros(N)
    S_mask[S] = 1
    # reorder gene set mask
    S_mask = S_mask[L]
    N_R = sum(abs(r*S_mask)**p_exp)
    P_hit = np.cumsum(abs(r*S_mask)**p_exp)/N_R if N_R!=0 else np.zeros_like(S_mask)
    N_H = len(S)
    P_mis = np.cumsum((1-S_mask))/(N-N_H) if N!=N_H else np.zeros_like(S_mask)
    idx = np.argmax(abs(P_hit - P_mis))
    print("ES =", P_hit[idx]-P_mis[idx])
    f, axarr = plt.subplots(3, sharex=True)
    axarr[0].plot(S_mask)
    axarr[0].set_title('gene set %s' % name)
    axarr[1].plot(r)
    axarr[1].set_title('correlation with phenotype')
    axarr[2].plot(P_hit-P_mis)
    axarr[2].set_title('random walk')


    

In [114]:

    

L,r = rank_genes(D,C)
plot_es_calculations('CBF_LEUKEMIA_DOWNING_AML', L, r)


    

    




ES = 0.214604376897






    

In [19]:

    

N, k = D.shape
pi = np.array([np.random.randint(0,2) for i in range(k)])
L, r = rank_genes(D,pi)
print(pi)
plot_es_calculations('CBF_LEUKEMIA_DOWNING_AML', L, r)


    

    




[0 1 0 1 1 0 1 1 0 1 1 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 1 0 1 1 0 1 1 0 0 1 0
 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 0 1 1 1 0 0 1 0]
ES = -0.446675459428






    

In [27]:

    

%time order, NES, p_values = gsea(D, C, gene_sets)


    

    




/usr/lib64/python3.6/site-packages/numpy/core/fromnumeric.py:2889: RuntimeWarning: Mean of empty slice.
  out=out, **kwargs)
/usr/lib64/python3.6/site-packages/numpy/core/_methods.py:80: RuntimeWarning: invalid value encountered in double_scalars
  ret = ret.dtype.type(ret / rcount)






    




CPU times: user 8min 38s, sys: 100 ms, total: 8min 38s
Wall time: 8min 39s

In [35]:

    

from IPython.display import display, Markdown
s = "| geneset | NES | p-value | number of genes in geneset |\n |-------|---|---|---|\n "
for i in range(len(order)):
    s = s + "| **%s** | %.3f | %.7f | %d |\n" % (gene_set_names[order[i]][0], NES[i], p_values[i], len(gene_sets[order[i]]))
display(Markdown(s))


    

    







geneset
NES
p-value
number of genes in geneset




DNA_DAMAGE_SIGNALLING
1.833
0.0000000
60


MAP00480_Glutathione_metabolism
-1.780
0.0060976
13


MAP00193_ATP_synthesis
-1.756
0.0040323
10


MAP00195_Photosynthesis
-1.756
0.0040323
10


MAP03070_Type_III_secretion_system
-1.756
0.0040323
10


CR_CELL_CYCLE
1.749
0.0118812
56


cell_cycle_checkpointII
1.740
0.0020964
7


Cell_Cycle
1.717
0.0397614
50


g2Pathway
1.710
0.0099602
19


ST_Ga13_Pathway
-1.698
0.0193050
22


compPathway
-1.696
0.0157171
13


stemPathway
-1.690
0.0093633
10


il10Pathway
-1.688
0.0181452
13


ST_Gaq_Pathway
-1.675
0.0305927
18


MAP00600_Sphingoglycolipid_metabolism
-1.671
0.0181818
9


ANTI_CD44_UP
-1.670
0.0125523
23


MAP00530_Aminosugars_metabolism
-1.662
0.0161943
10


neutrophilPathway
-1.660
0.0138889
8


mRNA_splicing
1.660
0.0239521
28


rbPathway
1.658
0.0177165
11


cell2cellPathway
-1.654
0.0116505
8


HOXA9_DOWN
-1.654
0.0197628
26


MAP00531_Glycosaminoglycan_degradation
-1.647
0.0081301
9


eif2Pathway
1.644
0.0119048
6


alternativePathway
-1.635
0.0262172
8


tsp1Pathway
-1.632
0.0163265
7


cell_adhesion
-1.630
0.0040404
103


Matrix_Metalloproteinases
-1.612
0.0181818
16


SA_MMP_CYTOKINE_CONNECTION
-1.611
0.0258964
11


MAP_kinase_kinase_activity
-1.608
0.0100200
4


integrinPathway
-1.608
0.0331384
24


MAP00680_Methane_metabolism
-1.605
0.0286299
6


CR_REPAIR
1.601
0.0382294
25


MAP00910_Nitrogen_metabolism
-1.599
0.0243902
11


GO_ROS
-1.597
0.0413223
19


p53Pathway
1.595
0.0369610
13


MAP00603_Globoside_metabolism
-1.589
0.0281690
9


Proteasome_Degradation
1.584
0.0516899
28


ROS
-1.584
0.0266393
6


KRAS_TOP100_KNOCKDOWN
-1.580
0.0263158
34


cell_adhesion_molecule_activity
-1.576
0.0185185
64


arfPathway
1.560
0.0354906
12


tertPathway
1.556
0.0340000
8


il1rPathway
-1.552
0.0726916
20


DOWNREG_BY_HOXA9
-1.536
0.0346939
23


fbw7Pathway
1.535
0.0459082
8


reckPathway
-1.533
0.0591837
6


tollPathway
-1.532
0.0459082
15


cell_motility
-1.531
0.0364683
63


NFKB_REDUCED
-1.527
0.0579151
12


il6Pathway
-1.522
0.0483559
16


HTERT_DOWN
-1.515
0.0337302
59


pgc1aPathway
1.512
0.0693277
13


extrinsicPathway
-1.512
0.0582329
11


radiation_sensitivity
1.509
0.0542299
18


41bbPathway
-1.505
0.0596421
12


monocytePathway
-1.505
0.0811881
11


ranklPathway
-1.496
0.0540541
10


telPathway
1.491
0.0688935
12


pepiPathway
-1.491
0.0436508
5


Pentose_Phosphate_Pathway
-1.490
0.0401691
5


eosinophilsPathway
-1.489
0.0530452
6


stressPathway
-1.488
0.0504032
16


MAP00230_Purine_metabolism
1.487
0.0452756
55


cell_adhesion_receptor_activity
-1.484
0.0592885
21


S1P_Signaling
-1.481
0.0450098
12


mspPathway
-1.481
0.0458333
4


p27Pathway
1.480
0.0594480
10


il5Pathway
-1.480
0.0796117
9


ldlPathway
-1.479
0.0636183
6


cdc25Pathway
1.475
0.0450980
7


th1th2Pathway
-1.471
0.0730769
13


ADULT_LIVER_vs_FETAL_LIVER_GNF2
-1.471
0.0623742
34


MAP00380_Tryptophan_metabolism
-1.470
0.0655391
30


cell_cycle_checkpoint
1.465
0.1104294
17


tnfr2Pathway
-1.463
0.0982318
10


mRNA_processing
1.461
0.0924370
27


atrbrcaPathway
1.459
0.0861723
8


il18Pathway
-1.456
0.0797546
6


lymphocytePathway
-1.454
0.0962672
9


pentosePathway
-1.453
0.0494071
3


GLYCOL
-1.453
0.0905350
12


CR_CAM
-1.452
0.0449219
71


plk3Pathway
1.449
0.0688935
5


MAP00970_Aminoacyl_tRNA_biosynthesis
1.448
0.0910781
16


classicPathway
-1.444
0.0929752
9


MAP00340_Histidine_metabolism
-1.439
0.0682303
12


Eicosanoid_Synthesis
-1.435
0.0774194
11


lairPathway
-1.435
0.1003937
15


ST_G_alpha_iPathway
-1.433
0.0864440
20


AR_ORTHOS_MAPPED_TO_U133_VIA_NETAFFX
1.431
0.0722892
26


AR_MOUSE
1.431
0.0722892
26


EMT_DOWN
-1.423
0.0745098
29


ifngPathway
-1.420
0.0857700
5


setPathway
1.414
0.0907258
10


MAP00860_Porphyrin_and_chlorophyll_metabolism
-1.409
0.1189979
12


freePathway
-1.405
0.1217039
7


MAP00590_Prostaglandin_and_leukotriene_metabolism
-1.401
0.0944206
10


HOX_LIST_JP
-1.394
0.1295938
18


mitochondriaPathway
1.393
0.1077236
13


cytokinePathway
-1.390
0.1000000
14


krebPathway
1.389
0.0958904
6


inflamPathway
-1.388
0.0888031
22


dcPathway
-1.386
0.1360947
13


MAP00620_Pyruvate_metabolism
1.382
0.1283644
23


ANDROGEN_UP_GENES
-1.378
0.0766284
37


CR_TRANSPORT_OF_VESICLES
-1.376
0.1397206
17


mcalpainPathway
-1.371
0.1138716
19


etcPathway
1.370
0.1019608
6


tcraPathway
1.365
0.1673469
12


GLUT_DOWN
1.357
0.1762376
184


bcl2family_and_reg_network
-1.355
0.1259690
15


blymphocytePathway
-1.354
0.1529412
9


tercPathway
1.351
0.1391650
6


keratinocytePathway
-1.350
0.1385542
30


Glycogen_Metabolism
1.348
0.1162325
25


fosbPathway
-1.347
0.1153082
4


tRNA_Synthetases
1.347
0.1535509
17


mitrPathway
1.346
0.1180258
3


tumor_supressor
1.346
0.1315789
18


il7Pathway
1.344
0.1594203
15


cell_death
-1.341
0.1240310
12


MAP00240_Pyrimidine_metabolism
1.335
0.1690141
31


vitcbPathway
-1.333
0.1273486
5


dnafragmentPathway
1.329
0.1587302
8


erythPathway
-1.328
0.1568627
10


g1Pathway
1.327
0.1304348
23


MAP00500_Starch_and_sucrose_metabolism
-1.326
0.1680162
15


SA_REG_CASCADE_OF_CYCLIN_EXPR
1.324
0.1411992
7


proteasomePathway
1.317
0.1952663
19


electron_transporter_activity
-1.314
0.1081633
60


ST_Tumor_Necrosis_Factor_Pathway
-1.314
0.1533981
22


MAP00030_Pentose_phosphate_pathway
-1.309
0.1940000
13


hdacPathway
1.307
0.1768293
19


ST_Ga12_Pathway
-1.305
0.1591356
15


gabaPathway
-1.303
0.1764706
4


relaPathway
-1.303
0.1630648
12


MAP00252_Alanine_and_aspartate_metabolism
1.302
0.1683992
17


Krebs-TCA_Cycle
1.302
0.1832994
21


il22bpPathway
-1.301
0.1909263
7


ahspPathway
-1.300
0.2459016
9


nktPathway
-1.299
0.1560000
23


NFKB_INDUCED
-1.299
0.1838384
81


eea1Pathway
-1.298
0.1777778
4


Fatty_Acid_Synthesis
1.298
0.1618852
7


longevityPathway
-1.297
0.2007648
10


intrinsicPathway
-1.297
0.1781377
13


Wnt_Signaling
1.295
0.1393939
29


EMT_UP
-1.293
0.1363636
46


ST_STAT3_Pathway
-1.290
0.1775148
5


mta3Pathway
-1.288
0.2027559
7


MAP00602_Blood_group_glycolipid_biosynthesis_neolact_series
-1.287
0.1729622
7


tnf_and_fas_network
-1.286
0.2059406
16


slrp2Pathway
-1.284
0.1513944
3


ST_Dictyostelium_discoideum_cAMP_Receptor_G_Protein_Independent_Pathways
1.284
0.1733871
3


ST_MONOCYTE_AD_PATHWAY
-1.283
0.1766990
17


Complement_Activation_Classical
-1.281
0.1850312
10


MAP00360_Phenylalanine_metabolism
-1.281
0.1818182
9


TCA
1.279
0.1988189
10


eponfkbPathway
-1.275
0.2246964
8


egfr_smrtePathway
-1.274
0.1878788
7


ANTI_CD44_DOWN
-1.273
0.1771894
12


XINACT_MERGED
1.272
0.2204724
12


lechtinPathway
-1.272
0.1745380
8


MAP00561_Glycerolipid_metabolism
-1.265
0.1680328
27


GPCRs_Class_A_Rhodopsin-like
-1.264
0.1988304
63


SIG_CD40PATHWAYMAP
-1.264
0.1965649
19


CR_CYTOSKELETON
-1.263
0.1941176
19


akap13Pathway
-1.260
0.2016293
5


ST_ERK1_ERK2_MAPK_Pathway
-1.259
0.1968811
23


MAP00310_Lysine_degradation
-1.259
0.2085020
11


ST_IL_13_PATHWAY
-1.258
0.1797980
4


ST_Interleukin_13_Pathway
-1.258
0.1797980
4


il3Pathway
-1.257
0.2152642
11


d4gdiPathway
1.256
0.1733871
11


ST_Type_I_Interferon_Pathway
-1.256
0.1923077
8


CR_TRANSCRIPTION_FACTORS
-1.249
0.2118321
49


fasPathway
1.248
0.2217484
21


drug_resistance_and_metabolism
1.244
0.1390041
71


cellcyclePathway
1.243
0.2107438
19


epoPathway
-1.241
0.2246521
16


ifnaPathway
-1.239
0.2220000
8


tidPathway
-1.238
0.2558594
13


TESTIS_GENES_FROM_XHX_AND_NETAFFX
-1.235
0.2115768
20


fibrinolysisPathway
-1.232
0.2220149
9


anthraxPathway
-1.232
0.1712474
2


LEU_DOWN
1.229
0.2555332
120


cd40Pathway
-1.228
0.2648752
7


pyk2Pathway
-1.227
0.2416503
23


FRASOR_ER_DOWN
-1.224
0.1964286
41


SA_CASPASE_CASCADE
1.224
0.2475442
13


neurotransmittersPathway
-1.223
0.2378049
6


fatty_acid_metabolism
-1.222
0.2331288
10


TGF_Beta_Signaling_Pathway
-1.222
0.2069632
26


rarPathway
-1.221
0.2460938
3


gata3Pathway
-1.210
0.2124248
12


mrpPathway
-1.206
0.2397661
3


ST_Interferon_gamma_Pathway
-1.205
0.2558594
5


insulin_signalling
-1.205
0.2186312
69


RAP_UP
1.204
0.2301255
104


aifPathway
1.203
0.2328244
2


srcRPTPPathway
1.203
0.2714286
10


GATA1_WEISS
-1.198
0.2751540
13


pkcPathway
-1.197
0.2600382
6


MAP00630_Glyoxylate_and_dicarboxylate_metabolism
1.196
0.2632613
5


plcePathway
-1.191
0.2455796
7


tcrMoleculePathway
1.190
0.3367347
5


carm-erPathway
1.186
0.3066132
15


tnfr1Pathway
1.185
0.2643443
23


tall1Pathway
-1.184
0.2828685
8


INSULIN_2F_UP
1.183
0.2809224
111


amiPathway
1.178
0.2689938
21


cskPathway
1.178
0.2689938
21


skp2e2fPathway
1.176
0.2725451
10


ST_Integrin_Signaling_Pathway
-1.175
0.2725490
42


biopeptidesPathway
-1.169
0.2669246
32


UPREG_BY_HOXA9
-1.167
0.2785571
24


ptc1Pathway
1.165
0.2917466
9


MAP00670_One_carbon_pool_by_folate
1.162
0.2715105
10


rabPathway
-1.161
0.2978723
10


botulinPathway
1.156
0.3048017
4


ngfPathway
-1.155
0.3168317
15


calcineurinPathway
1.153
0.2889344
16


MAP00650_Butanoate_metabolism
-1.152
0.2769857
16


MAP00330_Arginine_and_proline_metabolism
-1.151
0.2568093
24


MAP00010_Glycolysis_Gluconeogenesis
-1.147
0.3002028
36


mapkPathway
-1.147
0.2617188
53


MAP00533_Keratan_sulfate_biosynthesis
1.145
0.2989691
2


acetaminophenPathway
-1.142
0.3253968
5


SA_TRKA_RECEPTOR
-1.141
0.3214286
12


nfkbPathway
-1.140
0.3192771
16


ST_B_Cell_Antigen_Receptor
-1.137
0.3219048
28


SA_DAG1
-1.132
0.3109756
8


p35alzheimersPathway
-1.131
0.3064833
9


glycolysisPathway
-1.129
0.3286290
6


nthiPathway
-1.129
0.3630952
14


carm1Pathway
-1.129
0.3155819
10


hcmvPathway
-1.128
0.3412229
14


GLUCO
-1.125
0.2983539
19


stat3Pathway
-1.125
0.3675889
6


ST_Differentiation_Pathway_in_PC12_Cells
-1.122
0.3165736
28


ephA4Pathway
-1.118
0.3424658
9


CR_HORMONAL_FUNCTIONS
-1.118
0.3217054
6


ST_ADRENERGIC
-1.118
0.3268482
27


GLUT_UP
1.114
0.3155819
134


ace2Pathway
-1.114
0.3529412
4


MAP00020_Citrate_cycle_TCA_cycle
1.112
0.4047151
15


SA_G2_AND_M_PHASES
1.111
0.3551587
6


MAPK_Cascade
-1.111
0.3339695
13


FRASOR_ER_UP
1.111
0.2960000
17


cell_cycle_regulator
1.110
0.3547718
17


igf1mtorPathway
1.103
0.3562992
17


CR_SIGNALLING
-1.101
0.3086172
131


chemicalPathway
1.099
0.3272358
16


betaoxidationPathway
1.098
0.3869048
5


AR_MOUSE_PLUS_TESTO_FROM_NETAFFX
1.096
0.3244898
33


GLUCOSE_DOWN
1.095
0.3831967
87


HTERT_UP
1.093
0.3625255
100


MAPKKK_cascade
-1.091
0.3604888
7


tcapoptosisPathway
1.089
0.3842975
7


ucalpainPathway
-1.088
0.3584158
11


deathPathway
-1.088
0.3413174
22


MAP00720_Reductive_carboxylate_cycle_CO2_fixation
1.087
0.3885602
6


caspasePathway
1.085
0.3727455
19


MAP00450_Selenoamino_acid_metabolism
-1.084
0.3800425
4


ST_G_alpha_5_Pathway
-1.077
0.3710317
8


gsk3Pathway
-1.076
0.3925049
16


ranPathway
1.076
0.3923541
2


cell_surface_receptor_linked_signal_transduction
-1.070
0.3627451
79


ptdinsPathway
-1.070
0.3964497
17


bcrPathway
1.069
0.3853955
29


mRNA_binding_activity
1.068
0.4376278
8


SIG_BCR_Signaling_Pathway
1.066
0.3946281
37


feederPathway
-1.062
0.4392713
4


wntPathway
1.058
0.4000000
13


cdk5Pathway
-1.058
0.4152893
9


GPCRs_Class_B_Secretin-like
-1.058
0.3653061
11


VOXPHOS
1.057
0.4567404
38


HEMO_TF_LIST_JP
-1.056
0.3858268
58


tpoPathway
-1.055
0.4114286
21


soddPathway
-1.044
0.4367589
8


CR_IMMUNE_FUNCTION
1.042
0.3929961
38


MAP00350_Tyrosine_metabolism
-1.041
0.4078675
17


spryPathway
-1.036
0.4399185
9


ST_Interleukin_4_Pathway
-1.034
0.4296875
14


rhoPathway
-1.032
0.4308943
18


P53_DOWN
1.030
0.4223602
15


ST_PAC1_RECEPTOR_PATHWAY
-1.026
0.4459725
6


MAP00601_Blood_group_glycolipid_biosynthesis_lact_series
-1.021
0.4109312
5


ndkDynaminPathway
1.018
0.4372549
12


GLUCOSE_UP
-1.015
0.4427646
25


MAP03020_RNA_polymerase
1.015
0.4861660
10


Electron_Transport_Chain
1.014
0.5100402
38


vifPathway
1.012
0.4549098
3


fmlppathway
-1.012
0.4394251
27


gsPathway
1.007
0.4702259
5


MAP00052_Galactose_metabolism
-1.006
0.4301310
16


vobesityPathway
1.005
0.4549098
6


malatexPathway
1.002
0.4766537
6


gcrPathway
-1.002
0.4621514
15


dbpbPathway
-1.002
0.4850895
2


CR_ANGIOG
-1.002
0.4500000
5


ketonebodiesPathway
1.002
0.4851485
3


KET
1.002
0.4851485
3


MAP00532_Chondroitin_Heparan_sulfate_biosynthesis
-1.000
0.0000000
0


INSULIN_2F_DOWN
-0.996
0.4840637
7


circadianPathway
0.995
0.4821429
4


BRCA_DOWN
0.993
0.4780952
8


salmonellaPathway
-0.989
0.4869215
7


sppaPathway
-0.984
0.4787879
18


metPathway
-0.982
0.4848485
29


FA
0.981
0.4864341
17


cell_proliferation
0.980
0.5072464
123


SIG_PIP3_signaling_in_B_lymphocytes
0.980
0.4873950
22


HUMAN_CD34_ENRICHED_TF_JP
0.978
0.5042017
112


no1Pathway
-0.974
0.5010352
21


MAP00410_beta_Alanine_metabolism
-0.973
0.4969325
12


cell_growth_and_or_maintenance
-0.972
0.4765625
31


parkinPathway
0.971
0.5527344
3


g1pPathway
0.971
0.5620155
2


egfPathway
-0.971
0.5087719
23


cdmacPathway
-0.970
0.4969450
14


flumazenilPathway
-0.970
0.5231660
5


etsPathway
-0.970
0.4900398
12


cell_cycle_arrest
0.969
0.5146444
18


MAP00632_Benzoate_degradation
0.968
0.5286885
7


cblPathway
-0.966
0.5009862
6


cdc42racPathway
-0.966
0.5073069
9


ptenPathway
-0.964
0.5207101
11


FETAL_LIVER_HS_ENRICHED_TF_JP
0.960
0.5073684
46


tob1Pathway
0.954
0.4989691
14


LEU_UP
0.949
0.5468750
70


SIG_PIP3SIGINCARDIACMYOCTES
0.949
0.5524194
41


ecmPathway
-0.946
0.5472837
17


bbcellPathway
0.942
0.5452586
3


leptinPathway
-0.940
0.5638507
5


ST_Wnt_beta_catenin_Pathway
-0.936
0.5620000
15


vegfPathway
0.933
0.5546720
21


notchPathway
-0.929
0.5480000
4


nkcellsPathway
-0.929
0.5637860
16


cptPathway
0.924
0.6224490
2


GO_0005739
0.923
0.5627451
90


shhPathway
-0.921
0.5773196
8


MAP00790_Folate_biosynthesis
0.918
0.5542857
10


MAP00400_Phenylalanine_tyrosine_and_tryptophan_biosynthesis
-0.918
0.5469062
6


raccycdPathway
-0.917
0.5575397
20


dreamPathway
-0.917
0.5810277
11


MAP00512_O_Glycans_biosynthesis
-0.916
0.6011450
3


mprPathway
0.915
0.5622568
19


INS
-0.914
0.5873016
18


GLYCOGEN
-0.912
0.5684647
10


MAP00062_Fatty_acid_biosynthesis_path_2
0.912
0.5914397
7


SA_PROGRAMMED_CELL_DEATH
0.912
0.5866142
6


tubbyPathway
-0.912
0.5901328
6


cremPathway
0.910
0.5894737
5


Fatty_Acid_Degradation
0.908
0.5885827
13


cxcr4Pathway
-0.908
0.5960000
21


slrpPathway
-0.907
0.5928854
3


mef2dPathway
0.906
0.5592516
15


MAP00071_Fatty_acid_metabolism
0.904
0.5979798
32


arenrf2Pathway
0.901
0.6044625
11


trkaPathway
-0.899
0.5859375
9


ST_Wnt_Ca2_cyclic_GMP_Pathway
-0.897
0.6026365
13


GPCRs_Class_A_Rhodopsin-like_2
-0.894
0.6464844
1


MAP00100_Sterol_biosynthesis
0.891
0.6300000
9


MAP00051_Fructose_and_mannose_metabolism
-0.891
0.5814978
14


aktPathway
-0.891
0.5968379
13


pmlPathway
-0.889
0.5950920
11


MAP00640_Propanoate_metabolism
-0.888
0.5991984
15


tcrPathway
0.886
0.6214575
39


SA_B_CELL_RECEPTOR_COMPLEXES
-0.885
0.6140684
16


p53_signalling
0.884
0.6537678
65


akap96Pathway
0.884
0.6141414
9


GNF_FEMALE_GENES
-0.883
0.5802469
56


SA_G1_AND_S_PHASES
0.881
0.6149194
10


MAP00190_Oxidative_phosphorylation
-0.880
0.6047431
30


P53_UP
-0.879
0.6311637
29


ST_GRANULE_CELL_SURVIVAL_PATHWAY
-0.871
0.6461233
18


reelinPathway
-0.871
0.6334661
5


ck1Pathway
-0.870
0.6509240
12


ccr3Pathway
-0.868
0.6325301
16


ST_T_Cell_Signal_Transduction
-0.867
0.6423077
26


SIG_Regulation_of_the_actin_cytoskeleton_by_Rho_GTPases
-0.863
0.6410749
16


ccr5Pathway
-0.863
0.6480769
17


rnaPathway
0.863
0.6526946
6


nos1Pathway
0.863
0.6222664
17


cftrPathway
-0.863
0.6706827
9


SIG_CHEMOTAXIS
0.860
0.6444444
23


pparPathway
-0.858
0.6918605
2


hivnefPathway
0.857
0.6469388
40


shh_lisa
0.854
0.6811024
14


rasPathway
-0.854
0.6335878
18


RAP_DOWN
0.853
0.6374745
148


asbcellPathway
-0.853
0.6604128
6


appPathway
0.853
0.6845361
3


actinYPathway
-0.850
0.6463415
8


badPathway
-0.849
0.6597077
15


il17Pathway
0.842
0.6536082
14


pparaPathway
0.842
0.7263780
44


MAP00260_Glycine_serine_and_threonine_metabolism
-0.841
0.6768293
13


sarsPathway
0.835
0.6359833
9


akapCentrosomePathway
-0.835
0.7001934
8


pdgfPathway
-0.833
0.7041420
25


PGC
0.833
0.7140187
185


ctlPathway
-0.831
0.6546906
12


ca_nf_at_signalling
0.830
0.7397541
60


chrebpPathway
0.830
0.6899384
11


alkPathway
-0.823
0.6920078
17


ox40Pathway
0.819
0.7002012
3


ST_p38_MAPK_Pathway
-0.817
0.7142857
23


ANDROGEN_GENES_FROM_NETAFFX
0.815
0.7030928
21


SA_PTEN_PATHWAY
-0.814
0.6949807
13


SA_BONE_MORPHOGENETIC
-0.809
0.7783985
1


nfatPathway
0.808
0.7358871
39


gpcrPathway
0.808
0.7090164
30


breast_cancer_estrogen_signalling
-0.808
0.8333333
56


il4Pathway
-0.808
0.7140115
9


GPCRs_Class_C_Metabotropic_glutamate_pheromone
-0.799
0.7164751
6


PROLIF_GENES
-0.798
0.9096267
219


MAP00430_Taurine_and_hypotaurine_metabolism
-0.797
0.7170923
3


MAP00562_Inositol_phosphate_metabolism
-0.795
0.7051546
12


myosinPathway
-0.794
0.7334630
10


SIG_InsulinReceptorPathwayInCardiacMyocytes
-0.790
0.7357002
33


MAP00251_Glutamate_metabolism
0.788
0.7431579
16


fcer1Pathway
-0.788
0.7323944
32


parkinsonsPathway
-0.788
0.8228106
1


Il12Pathway
-0.786
0.7206478
19


achPathway
-0.776
0.7331976
7


CR_PROTEIN_MOD
0.775
0.8388430
114


agpcrPathway
-0.775
0.7901961
10


Inflammatory_Response_Pathway
0.773
0.7629938
15


human_mitoDB_6_2002
0.772
0.7698259
198


ST_Jak_STAT_Pathway
-0.772
0.7624750
4


MAP00120_Bile_acid_biosynthesis
-0.767
0.7425150
14


ST_Fas_Signaling_Pathway
0.766
0.8095238
34


cardiacegfPathway
-0.764
0.7788845
12


malatePathway
0.764
0.7753906
4


igf1rPathway
-0.763
0.7672584
13


vipPathway
-0.763
0.8189300
22


RAR_UP
-0.761
0.7972167
18


MAP00460_Cyanoamino_acid_metabolism
-0.758
0.8263598
2


MAP00140_C21_Steroid_hormone_metabolism
-0.757
0.7648184
4


thelperPathway
-0.753
0.7254902
11


pelp1Pathway
0.751
0.7693878
4


igf1Pathway
-0.751
0.7762646
18


MAP00710_Carbon_fixation
-0.751
0.7689162
10


MAP00272_Cysteine_metabolism
0.750
0.7947686
8


erkPathway
-0.749
0.7922330
21


hbxPathway
-0.748
0.8268839
5


tcytotoxicPathway
-0.747
0.7227723
11


p38mapkPathway
0.747
0.7844311
25


CR_TRANSPORT
0.745
0.7583497
5


hifPathway
0.744
0.7974684
11


ST_Phosphoinositide_3_Kinase_Pathway
0.743
0.7864693
24


erbb4Pathway
0.739
0.7944664
4


erk5Pathway
0.736
0.7807377
14


MAP00740_Riboflavin_metabolism
0.734
0.8390152
3


MAP00150_Androgen_and_estrogen_metabolism
-0.732
0.8146067
9


insulinPathway
-0.731
0.7879377
20


arapPathway
-0.728
0.8016032
8


cacamPathway
0.727
0.8029046
7


il2Pathway
-0.727
0.8076190
19


atmPathway
-0.723
0.8248031
15


ppargPathway
0.723
0.7995910
6


huntingtonPathway
0.722
0.8257426
4


plcdPathway
-0.721
0.8565737
3


MYC_WT
-0.718
0.8827038
2


CR_DNA_MET_AND_MOD
-0.716
0.8121212
15


hsp27Pathway
0.714
0.8443114
13


p53hypoxiaPathway
-0.713
0.8362573
17


ghPathway
-0.711
0.8249027
22


CR_DEATH
-0.709
0.8696498
48


CBF_LEUKEMIA_DOWNING_AML
0.708
0.8512397
37


rarrxrPathway
-0.707
0.8440000
10


EGF_receptor_signaling_pathway
0.707
0.8488121
4


mitochondr
0.705
0.8326923
197


PYR
0.703
0.8459959
6


gleevecPathway
0.691
0.8374486
18


ST_Dictyostelium_discoideum_cAMP_Chemotaxis_Pathway
0.689
0.8767396
22


electron_transport
-0.687
0.9125249
32


muscle_myosin
-0.682
0.8677043
8


at1rPathway
-0.679
0.8818898
25


tgfbPathway
-0.669
0.8624230
10


MYC_MUT
-0.668
0.9025341
2


erbb3Pathway
-0.667
1.0000000
1


rac1Pathway
0.667
0.9018789
18


il2rbPathway
0.666
0.8790323
28


s1pPathway
-0.665
0.8568548
6


SIG_IL4RECEPTOR_IN_B_LYPHOCYTES
-0.656
0.8693069
20


ionPathway
-0.654
0.8980392
5


plcPathway
-0.653
0.8610568
7


ctla4Pathway
0.650
0.8832599
17


edg1Pathway
-0.645
0.9092742
18


ceramidePathway
-0.644
0.8793456
18


ST_JNK_MAPK_Pathway
-0.642
0.9420290
24


par1Pathway
-0.636
0.8657588
13


MAP00510_N_Glycans_biosynthesis
-0.632
0.8974359
8


ureacyclePathway
-0.631
0.9282787
5


ps1Pathway
-0.629
0.8997955
7


mtorPathway
0.622
0.8947368
20


MAP_kinase_activity
0.622
0.9111969
4


no2il12Pathway
0.620
0.9000000
15


MAP00220_Urea_cycle_and_metabolism_of_amino_groups
-0.619
0.9259259
11


KRAS_TOP100_CONTROL
0.613
0.9790795
31


crebPathway
-0.610
0.9325397
21


MAP00280_Valine_leucine_and_isoleucine_degradation
0.587
0.9481766
17


eif4Pathway
0.577
0.9504950
21


SA_FAS_SIGNALLING
-0.566
0.9519231
5


G13_Signaling_Pathway
0.561
0.9426230
18


pitx2Pathway
-0.559
0.9642147
8


argininecPathway
-0.559
0.9532520
4


MAP00361_gamma_Hexachlorocyclohexane_degradation
0.557
0.9755102
18


torPathway
-0.553
0.9665354
3


ST_G_Protein_Independent_7_Transmembrane
0.540
0.9664694
3


ST_G_Protein_Independent_7_Transmembrane_Receptor_Signaling
0.540
0.9664694
3


CR_CANCER_TESTIS
0.524
0.9760956
8


MAP00271_Methionine_metabolism
0.513
0.9544592
6


BRCA_UP
0.501
0.9978587
36


fxrPathway
-0.499
0.9873684
4


caspase_activity
-0.496
0.9875776
6


methioninePathway
-0.491
0.9834025
3


MAP00760_Nicotinate_and_nicotinamide_metabolism
0.408
1.0000000
2

In [29]:

    

name = 'DNA_DAMAGE_SIGNALLING'
L,r = rank_genes(D,C)
plot_es_calculations(name, L, r)


    

    




ES = 0.534899988502






    

In [30]:

    

n = 1000
S =  gene_set_hash[name]['indexes']
L, r = rank_genes(D,C)
ES = enrichment_score(L,r,S)
ES_pi = np.zeros(n)
for i in range(n):
    pi = np.array([np.random.randint(0,2) for i in range(k)])
    L, r = rank_genes(D,pi)
    ES_pi[i] = enrichment_score(L,r,S)


    

In [31]:

    

hist(ES_pi,bins=100)
plot([ES,ES],[0,20],'r-',label="ES(S)")
title("Histogram of ES vlues for random phenotype labels.\nRed line is ES for the selected gene set.")


    

    
Out[31]:





<matplotlib.text.Text at 0x7f5f8bbf2898>






    

In [32]:

    

name = 'tcrPathway'
L,r = rank_genes(D,C)
plot_es_calculations(name, L, r)


    

    




ES = 0.268404322933






    

In [33]:

    

n = 1000
S =  gene_set_hash[name]['indexes']
L, r = rank_genes(D,C)
ES = enrichment_score(L,r,S)
ES_pi = np.zeros(n)
for i in range(n):
    pi = np.array([np.random.randint(0,2) for i in range(k)])
    L, r = rank_genes(D,pi)
    ES_pi[i] = enrichment_score(L,r,S)


    

In [34]:

    

hist(ES_pi,bins=100)
plot([ES,ES],[0,20],'r-',label="ES(S)")
title("Histogram of ES vlues for random phenotype labels.\nRed line is ES for the selected gene set.")


    

    
Out[34]:





<matplotlib.text.Text at 0x7f5f8c1319e8>






    

In [24]:

    

%timeit L,R = rank_genes(D,C)


    

    




3.56 ms ± 125 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)

In [25]:

    

%timeit ES = enrichment_score(L,r,S)


    

    




926 µs ± 18.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)

In [26]:

    

%prun order, NES, p_values = gsea(D, C, gene_sets)


    

In [ ]: