Correlation between copy number variations of arm-level result and selected clinical features
Lung Squamous Cell Carcinoma (Primary solid tumor)
22 February 2013  |  analyses__2013_02_22
Maintainer Information
Citation Information
doi:10.7908/C17H1GSW
Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)
Cite as Broad Institute TCGA Genome Data Analysis Center (2013): Correlation between copy number variations of arm-level result and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C17H1GSW
Overview
Introduction

This pipeline computes the correlation between significant arm-level copy number variations (cnvs) and selected clinical features.

Summary

Testing the association between subtypes identified by 79 different clustering approaches and 14 clinical features across 325 patients, 13 significant findings detected with Q value < 0.25.

  • 2 subtypes identified in current cancer cohort by '1p gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '1q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '2p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '2q gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '3p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '3q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11p gain mutation analysis'. These subtypes correlate to 'KARNOFSKY.PERFORMANCE.SCORE'.

  • 2 subtypes identified in current cancer cohort by '11q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '13q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '14q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '15q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '17p gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '17q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '19p gain mutation analysis'. These subtypes correlate to 'KARNOFSKY.PERFORMANCE.SCORE' and 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '19q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20p gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '21q gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '22q gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by 'Xq gain mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '1p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '1q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '2q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '3p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '3q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '4q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '5q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '6q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '7q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '8q loss mutation analysis'. These subtypes correlate to 'STOPPEDSMOKINGYEAR'.

  • 2 subtypes identified in current cancer cohort by '9p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '9q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '10q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '11q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '12p loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '12q loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

  • 2 subtypes identified in current cancer cohort by '13q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '14q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '15q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '16q loss mutation analysis'. These subtypes correlate to 'KARNOFSKY.PERFORMANCE.SCORE'.

  • 2 subtypes identified in current cancer cohort by '17p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '17q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '18q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '19p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '19q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20p loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '20q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '21q loss mutation analysis'. These subtypes do not correlate to any clinical features.

  • 2 subtypes identified in current cancer cohort by '22q loss mutation analysis'. These subtypes correlate to 'PATHOLOGY.N'.

  • 2 subtypes identified in current cancer cohort by 'Xq loss mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.

Results
Overview of the results

Table 1.  Get Full Table Overview of the association between subtypes identified by 79 different clustering approaches and 14 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 13 significant findings detected.

Clinical
Features 		Time
to
Death 		AGE GENDER KARNOFSKY
PERFORMANCE
SCORE 		HISTOLOGICAL
TYPE 		PATHOLOGY
T 		PATHOLOGY
N 		PATHOLOGICSPREAD(M) TUMOR
STAGE 		RADIATIONS
RADIATION
REGIMENINDICATION 		NUMBERPACKYEARSSMOKED STOPPEDSMOKINGYEAR TOBACCOSMOKINGHISTORYINDICATOR YEAROFTOBACCOSMOKINGONSET
Statistical Tests logrank test t-test Fisher's exact test t-test Chi-square test Chi-square test Chi-square test Chi-square test Chi-square test Fisher's exact test t-test t-test Chi-square test t-test
1p gain 0.767
(1.00) 		0.306
(1.00) 		0.0714
(1.00) 		3.79e-06
(0.0041) 		0.0884
(1.00) 		0.868
(1.00) 		0.326
(1.00) 		0.171
(1.00) 		0.526
(1.00) 		0.449
(1.00) 		0.359
(1.00) 		0.475
(1.00) 		0.245
(1.00)
1q gain 0.624
(1.00) 		0.499
(1.00) 		0.878
(1.00) 		0.358
(1.00) 		0.00453
(1.00) 		0.114
(1.00) 		0.578
(1.00) 		0.309
(1.00) 		0.559
(1.00) 		1
(1.00) 		0.272
(1.00) 		0.909
(1.00) 		0.907
(1.00) 		0.693
(1.00)
2p gain 0.812
(1.00) 		0.898
(1.00) 		0.0706
(1.00) 		0.126
(1.00) 		0.0106
(1.00) 		0.132
(1.00) 		0.254
(1.00) 		0.19
(1.00) 		0.21
(1.00) 		0.291
(1.00) 		0.058
(1.00) 		0.284
(1.00) 		0.674
(1.00) 		0.0243
(1.00)
2q gain 0.511
(1.00) 		0.996
(1.00) 		0.838
(1.00) 		0.611
(1.00) 		5.78e-05
(0.0624) 		0.12
(1.00) 		0.54
(1.00) 		0.266
(1.00) 		0.00617
(1.00) 		0.336
(1.00) 		0.528
(1.00) 		0.185
(1.00) 		0.901
(1.00) 		0.723
(1.00)
3p gain 0.393
(1.00) 		0.137
(1.00) 		0.149
(1.00) 		0.197
(1.00) 		0.978
(1.00) 		0.0503
(1.00) 		0.78
(1.00) 		0.643
(1.00) 		0.723
(1.00) 		1
(1.00) 		0.356
(1.00) 		0.256
(1.00) 		0.119
(1.00) 		0.0561
(1.00)
3q gain 0.633
(1.00) 		0.251
(1.00) 		0.702
(1.00) 		0.334
(1.00) 		0.354
(1.00) 		0.56
(1.00) 		0.882
(1.00) 		0.41
(1.00) 		0.64
(1.00) 		1
(1.00) 		0.797
(1.00) 		0.774
(1.00) 		0.0467
(1.00) 		0.0159
(1.00)
4p gain 0.33
(1.00) 		0.636
(1.00) 		0.0248
(1.00) 		0.712
(1.00) 		0.763
(1.00) 		0.24
(1.00) 		0.969
(1.00) 		0.75
(1.00) 		0.745
(1.00) 		1
(1.00) 		0.354
(1.00) 		0.941
(1.00) 		0.902
(1.00) 		0.404
(1.00)
4q gain 0.888
(1.00) 		0.442
(1.00) 		0.344
(1.00) 		0.188
(1.00) 		0.14
(1.00) 		0.622
(1.00) 		0.694
(1.00) 		0.13
(1.00) 		1
(1.00) 		0.172
(1.00) 		0.286
(1.00) 		0.0741
(1.00)
5p gain 0.464
(1.00) 		0.0652
(1.00) 		0.447
(1.00) 		0.663
(1.00) 		0.314
(1.00) 		0.0614
(1.00) 		0.378
(1.00) 		0.447
(1.00) 		0.604
(1.00) 		1
(1.00) 		0.529
(1.00) 		0.761
(1.00) 		0.0628
(1.00) 		0.74
(1.00)
5q gain 0.642
(1.00) 		0.351
(1.00) 		0.676
(1.00) 		0.453
(1.00) 		0.975
(1.00) 		0.882
(1.00) 		0.43
(1.00) 		0.665
(1.00) 		0.609
(1.00) 		0.296
(1.00) 		0.706
(1.00) 		0.785
(1.00) 		0.717
(1.00) 		0.32
(1.00)
6p gain 0.755
(1.00) 		0.343
(1.00) 		0.83
(1.00) 		0.286
(1.00) 		0.0295
(1.00) 		0.107
(1.00) 		0.872
(1.00) 		0.495
(1.00) 		0.264
(1.00) 		0.269
(1.00) 		0.279
(1.00) 		0.15
(1.00) 		0.74
(1.00) 		0.0131
(1.00)
6q gain 0.779
(1.00) 		0.455
(1.00) 		1
(1.00) 		0.463
(1.00) 		0.000929
(1.00) 		0.663
(1.00) 		0.618
(1.00) 		0.846
(1.00) 		0.966
(1.00) 		0.451
(1.00) 		0.164
(1.00) 		0.733
(1.00) 		0.725
(1.00) 		0.189
(1.00)
7p gain 0.189
(1.00) 		0.115
(1.00) 		0.567
(1.00) 		0.743
(1.00) 		0.736
(1.00) 		0.00865
(1.00) 		0.742
(1.00) 		0.393
(1.00) 		0.638
(1.00) 		0.734
(1.00) 		0.054
(1.00) 		0.294
(1.00) 		0.622
(1.00) 		0.245
(1.00)
7q gain 0.398
(1.00) 		0.284
(1.00) 		0.384
(1.00) 		0.48
(1.00) 		0.463
(1.00) 		0.0119
(1.00) 		0.688
(1.00) 		0.146
(1.00) 		0.339
(1.00) 		0.735
(1.00) 		0.741
(1.00) 		0.0372
(1.00) 		0.818
(1.00) 		0.397
(1.00)
8p gain 0.89
(1.00) 		0.399
(1.00) 		1
(1.00) 		0.00638
(1.00) 		0.105
(1.00) 		0.606
(1.00) 		0.416
(1.00) 		0.0595
(1.00) 		0.188
(1.00) 		0.193
(1.00) 		0.738
(1.00) 		0.843
(1.00) 		0.668
(1.00) 		0.444
(1.00)
8q gain 0.376
(1.00) 		0.745
(1.00) 		0.578
(1.00) 		0.531
(1.00) 		0.869
(1.00) 		0.231
(1.00) 		0.803
(1.00) 		0.102
(1.00) 		0.105
(1.00) 		0.309
(1.00) 		0.309
(1.00) 		0.729
(1.00) 		0.291
(1.00) 		0.635
(1.00)
9p gain 0.214
(1.00) 		0.72
(1.00) 		0.279
(1.00) 		0.00127
(1.00) 		0.538
(1.00) 		0.543
(1.00) 		0.743
(1.00) 		0.8
(1.00) 		1
(1.00) 		0.928
(1.00) 		0.205
(1.00) 		0.954
(1.00) 		0.882
(1.00)
9q gain 0.171
(1.00) 		0.504
(1.00) 		1
(1.00) 		0.537
(1.00) 		0.392
(1.00) 		0.331
(1.00) 		0.699
(1.00) 		0.185
(1.00) 		0.662
(1.00) 		0.609
(1.00) 		0.68
(1.00) 		0.899
(1.00) 		0.564
(1.00) 		0.74
(1.00)
10p gain 0.825
(1.00) 		0.996
(1.00) 		0.768
(1.00) 		0.936
(1.00) 		0.974
(1.00) 		0.361
(1.00) 		0.108
(1.00) 		0.0656
(1.00) 		0.0126
(1.00) 		0.41
(1.00) 		0.248
(1.00) 		0.881
(1.00) 		0.359
(1.00) 		0.372
(1.00)
10q gain 0.354
(1.00) 		0.734
(1.00) 		1
(1.00) 		0.996
(1.00) 		0.458
(1.00) 		0.834
(1.00) 		0.694
(1.00) 		0.673
(1.00) 		0.188
(1.00) 		0.582
(1.00) 		0.459
(1.00) 		0.0837
(1.00) 		0.771
(1.00)
11p gain 0.546
(1.00) 		0.554
(1.00) 		0.578
(1.00) 		2e-06
(0.00217) 		0.963
(1.00) 		0.00313
(1.00) 		0.39
(1.00) 		0.776
(1.00) 		0.633
(1.00) 		0.471
(1.00) 		0.148
(1.00) 		0.873
(1.00) 		0.648
(1.00) 		0.541
(1.00)
11q gain 0.293
(1.00) 		0.735
(1.00) 		0.304
(1.00) 		0.521
(1.00) 		0.921
(1.00) 		0.0217
(1.00) 		0.328
(1.00) 		0.676
(1.00) 		0.0756
(1.00) 		0.526
(1.00) 		0.562
(1.00) 		0.822
(1.00) 		0.549
(1.00) 		0.987
(1.00)
12p gain 0.868
(1.00) 		0.939
(1.00) 		0.00437
(1.00) 		0.0774
(1.00) 		0.884
(1.00) 		0.152
(1.00) 		0.806
(1.00) 		0.853
(1.00) 		0.913
(1.00) 		0.505
(1.00) 		0.965
(1.00) 		0.343
(1.00) 		0.396
(1.00) 		0.404
(1.00)
12q gain 0.722
(1.00) 		0.819
(1.00) 		0.0519
(1.00) 		0.378
(1.00) 		0.978
(1.00) 		0.326
(1.00) 		0.173
(1.00) 		0.458
(1.00) 		0.337
(1.00) 		0.632
(1.00) 		0.63
(1.00) 		0.302
(1.00) 		0.265
(1.00) 		0.839
(1.00)
13q gain 0.275
(1.00) 		0.937
(1.00) 		0.685
(1.00) 		0.992
(1.00) 		0.312
(1.00) 		0.853
(1.00) 		0.299
(1.00) 		0.767
(1.00) 		1
(1.00) 		0.566
(1.00) 		0.843
(1.00) 		0.0667
(1.00) 		0.681
(1.00)
14q gain 0.0683
(1.00) 		0.0833
(1.00) 		0.518
(1.00) 		0.858
(1.00) 		0.974
(1.00) 		0.253
(1.00) 		0.584
(1.00) 		0.384
(1.00) 		0.876
(1.00) 		1
(1.00) 		0.608
(1.00) 		0.251
(1.00) 		0.373
(1.00) 		0.657
(1.00)
15q gain 0.687
(1.00) 		0.993
(1.00) 		1
(1.00) 		0.389
(1.00) 		0.453
(1.00) 		0.58
(1.00) 		0.266
(1.00) 		0.718
(1.00) 		0.557
(1.00) 		1
(1.00) 		0.511
(1.00) 		0.441
(1.00) 		0.014
(1.00) 		0.463
(1.00)
16p gain 0.779
(1.00) 		0.311
(1.00) 		0.371
(1.00) 		0.0681
(1.00) 		0.799
(1.00) 		0.0158
(1.00) 		0.738
(1.00) 		0.839
(1.00) 		0.705
(1.00) 		1
(1.00) 		0.806
(1.00) 		0.688
(1.00) 		0.609
(1.00) 		0.121
(1.00)
16q gain 0.856
(1.00) 		0.109
(1.00) 		0.175
(1.00) 		0.366
(1.00) 		0.958
(1.00) 		0.0138
(1.00) 		0.181
(1.00) 		0.228
(1.00) 		0.172
(1.00) 		1
(1.00) 		0.94
(1.00) 		0.174
(1.00) 		0.984
(1.00) 		0.0125
(1.00)
17p gain 0.346
(1.00) 		0.839
(1.00) 		0.118
(1.00) 		3.79e-07
(0.000413) 		0.746
(1.00) 		0.499
(1.00) 		0.392
(1.00) 		0.0752
(1.00) 		1
(1.00) 		0.948
(1.00) 		0.581
(1.00) 		0.247
(1.00) 		0.0232
(1.00)
17q gain 0.704
(1.00) 		0.383
(1.00) 		0.0421
(1.00) 		0.12
(1.00) 		0.609
(1.00) 		0.453
(1.00) 		0.0162
(1.00) 		0.259
(1.00) 		0.0639
(1.00) 		0.336
(1.00) 		0.791
(1.00) 		0.401
(1.00) 		0.658
(1.00) 		0.0558
(1.00)
18p gain 0.183
(1.00) 		0.13
(1.00) 		0.22
(1.00) 		0.321
(1.00) 		0.837
(1.00) 		0.151
(1.00) 		0.0175
(1.00) 		0.0955
(1.00) 		0.156
(1.00) 		0.685
(1.00) 		0.344
(1.00) 		0.498
(1.00) 		0.225
(1.00) 		0.636
(1.00)
18q gain 0.42
(1.00) 		0.493
(1.00) 		0.692
(1.00) 		0.567
(1.00) 		0.672
(1.00) 		0.185
(1.00) 		0.0782
(1.00) 		0.244
(1.00) 		0.774
(1.00) 		0.624
(1.00) 		0.0793
(1.00) 		0.691
(1.00) 		0.459
(1.00) 		0.248
(1.00)
19p gain 0.438
(1.00) 		0.227
(1.00) 		0.675
(1.00) 		1.77e-06
(0.00192) 		5.63e-06
(0.00609) 		0.592
(1.00) 		0.411
(1.00) 		0.339
(1.00) 		0.57
(1.00) 		0.309
(1.00) 		0.592
(1.00) 		0.954
(1.00) 		0.632
(1.00) 		0.83
(1.00)
19q gain 0.667
(1.00) 		0.82
(1.00) 		1
(1.00) 		0.139
(1.00) 		0.000565
(0.608) 		0.769
(1.00) 		0.0947
(1.00) 		0.0595
(1.00) 		0.0583
(1.00) 		0.659
(1.00) 		0.879
(1.00) 		0.742
(1.00) 		0.781
(1.00) 		0.162
(1.00)
20p gain 0.765
(1.00) 		0.0637
(1.00) 		0.0231
(1.00) 		0.279
(1.00) 		0.173
(1.00) 		0.959
(1.00) 		0.612
(1.00) 		0.0423
(1.00) 		0.141
(1.00) 		0.19
(1.00) 		0.741
(1.00) 		0.683
(1.00) 		0.758
(1.00) 		0.198
(1.00)
20q gain 0.642
(1.00) 		0.319
(1.00) 		0.188
(1.00) 		0.77
(1.00) 		0.0175
(1.00) 		0.992
(1.00) 		0.466
(1.00) 		0.0652
(1.00) 		0.11
(1.00) 		0.15
(1.00) 		0.47
(1.00) 		0.43
(1.00) 		0.839
(1.00) 		0.658
(1.00)
21q gain 0.268
(1.00) 		0.682
(1.00) 		1
(1.00) 		0.949
(1.00) 		5.96e-05
(0.0643) 		0.364
(1.00) 		0.417
(1.00) 		0.823
(1.00) 		0.202
(1.00) 		0.471
(1.00) 		0.865
(1.00) 		0.515
(1.00) 		0.367
(1.00) 		0.631
(1.00)
22q gain 0.0826
(1.00) 		0.361
(1.00) 		0.458
(1.00) 		0.782
(1.00) 		0.906
(1.00) 		0.215
(1.00) 		0.58
(1.00) 		0.997
(1.00) 		0.133
(1.00) 		0.726
(1.00) 		0.938
(1.00) 		0.0869
(1.00) 		0.306
(1.00) 		0.604
(1.00)
Xq gain 0.881
(1.00) 		0.287
(1.00) 		1
(1.00) 		0.891
(1.00) 		0.99
(1.00) 		0.609
(1.00) 		0.714
(1.00) 		0.93
(1.00) 		0.702
(1.00) 		1
(1.00) 		0.563
(1.00) 		0.753
(1.00) 		0.924
(1.00) 		0.964
(1.00)
1p loss 0.566
(1.00) 		0.764
(1.00) 		0.0021
(1.00) 		0.45
(1.00) 		0.119
(1.00) 		0.589
(1.00) 		0.626
(1.00) 		0.111
(1.00) 		0.218
(1.00) 		0.212
(1.00) 		0.519
(1.00) 		0.76
(1.00) 		0.493
(1.00) 		0.572
(1.00)
1q loss 0.961
(1.00) 		0.0526
(1.00) 		0.685
(1.00) 		0.992
(1.00) 		0.497
(1.00) 		0.854
(1.00) 		0.91
(1.00) 		0.43
(1.00) 		0.243
(1.00) 		0.189
(1.00) 		0.545
(1.00) 		0.252
(1.00) 		0.252
(1.00)
2q loss 0.0514
(1.00) 		0.11
(1.00) 		1
(1.00) 		0.99
(1.00) 		0.675
(1.00) 		0.714
(1.00) 		0.576
(1.00) 		0.809
(1.00) 		1
(1.00) 		0.176
(1.00) 		0.556
(1.00) 		0.148
(1.00) 		0.731
(1.00)
3p loss 0.618
(1.00) 		0.365
(1.00) 		0.702
(1.00) 		0.095
(1.00) 		0.406
(1.00) 		0.954
(1.00) 		0.846
(1.00) 		0.772
(1.00) 		0.912
(1.00) 		0.539
(1.00) 		0.567
(1.00) 		0.253
(1.00) 		0.569
(1.00) 		0.412
(1.00)
3q loss 0.101
(1.00) 		0.24
(1.00) 		0.637
(1.00) 		0.65
(1.00) 		0.0126
(1.00) 		0.338
(1.00) 		0.0774
(1.00) 		0.198
(1.00) 		0.0762
(1.00) 		1
(1.00) 		0.0871
(1.00) 		0.178
(1.00) 		0.911
(1.00) 		0.0536
(1.00)
4p loss 0.113
(1.00) 		0.794
(1.00) 		1
(1.00) 		0.271
(1.00) 		0.406
(1.00) 		0.366
(1.00) 		0.28
(1.00) 		0.221
(1.00) 		0.0527
(1.00) 		1
(1.00) 		0.512
(1.00) 		0.962
(1.00) 		0.92
(1.00) 		0.202
(1.00)
4q loss 0.0797
(1.00) 		0.566
(1.00) 		0.146
(1.00) 		0.138
(1.00) 		0.235
(1.00) 		0.495
(1.00) 		0.857
(1.00) 		0.235
(1.00) 		0.189
(1.00) 		0.536
(1.00) 		0.514
(1.00) 		0.601
(1.00) 		0.655
(1.00) 		0.522
(1.00)
5p loss 0.0514
(1.00) 		0.348
(1.00) 		0.461
(1.00) 		0.491
(1.00) 		0.00789
(1.00) 		0.506
(1.00) 		0.475
(1.00) 		0.304
(1.00) 		0.835
(1.00) 		0.178
(1.00) 		0.851
(1.00) 		0.482
(1.00) 		0.78
(1.00) 		0.283
(1.00)
5q loss 0.154
(1.00) 		0.749
(1.00) 		0.512
(1.00) 		0.684
(1.00) 		0.235
(1.00) 		0.0243
(1.00) 		0.289
(1.00) 		0.205
(1.00) 		0.018
(1.00) 		0.542
(1.00) 		0.472
(1.00) 		0.842
(1.00) 		0.301
(1.00) 		0.0251
(1.00)
6p loss 0.731
(1.00) 		0.457
(1.00) 		0.149
(1.00) 		0.818
(1.00) 		0.0227
(1.00) 		0.0711
(1.00) 		0.177
(1.00) 		0.702
(1.00) 		0.0614
(1.00) 		0.613
(1.00) 		0.7
(1.00) 		0.896
(1.00) 		0.763
(1.00) 		0.976
(1.00)
6q loss 0.794
(1.00) 		0.626
(1.00) 		0.842
(1.00) 		0.789
(1.00) 		0.0372
(1.00) 		0.113
(1.00) 		0.192
(1.00) 		0.736
(1.00) 		0.14
(1.00) 		0.621
(1.00) 		0.264
(1.00) 		0.653
(1.00) 		0.587
(1.00) 		0.144
(1.00)
7p loss 0.739
(1.00) 		0.467
(1.00) 		0.792
(1.00) 		0.796
(1.00) 		0.954
(1.00) 		0.333
(1.00) 		0.365
(1.00) 		0.709
(1.00) 		0.567
(1.00) 		0.508
(1.00) 		0.103
(1.00) 		0.0191
(1.00) 		0.519
(1.00) 		0.394
(1.00)
7q loss 0.485
(1.00) 		0.818
(1.00) 		0.462
(1.00) 		0.988
(1.00) 		0.0789
(1.00) 		0.523
(1.00) 		0.576
(1.00) 		0.915
(1.00) 		0.295
(1.00) 		0.165
(1.00) 		0.893
(1.00) 		0.604
(1.00) 		0.596
(1.00)
8p loss 0.581
(1.00) 		0.181
(1.00) 		0.577
(1.00) 		0.298
(1.00) 		0.866
(1.00) 		0.225
(1.00) 		0.634
(1.00) 		0.387
(1.00) 		0.489
(1.00) 		0.185
(1.00) 		0.0464
(1.00) 		0.0832
(1.00) 		0.069
(1.00) 		0.459
(1.00)
8q loss 0.482
(1.00) 		0.668
(1.00) 		0.301
(1.00) 		0.463
(1.00) 		0.611
(1.00) 		0.469
(1.00) 		0.978
(1.00) 		0.507
(1.00) 		0.917
(1.00) 		1
(1.00) 		0.305
(1.00) 		8.72e-07
(0.000948) 		0.468
(1.00) 		0.171
(1.00)
9p loss 0.804
(1.00) 		0.353
(1.00) 		0.44
(1.00) 		0.3
(1.00) 		0.404
(1.00) 		0.81
(1.00) 		0.492
(1.00) 		0.66
(1.00) 		0.657
(1.00) 		0.766
(1.00) 		0.137
(1.00) 		0.869
(1.00) 		0.725
(1.00) 		0.364
(1.00)
9q loss 0.88
(1.00) 		0.00387
(1.00) 		0.772
(1.00) 		0.768
(1.00) 		0.16
(1.00) 		0.282
(1.00) 		0.737
(1.00) 		0.203
(1.00) 		0.883
(1.00) 		1
(1.00) 		0.753
(1.00) 		0.977
(1.00) 		0.584
(1.00) 		0.0911
(1.00)
10p loss 0.431
(1.00) 		0.592
(1.00) 		0.222
(1.00) 		0.963
(1.00) 		0.363
(1.00) 		0.852
(1.00) 		0.393
(1.00) 		0.279
(1.00) 		0.278
(1.00) 		0.13
(1.00) 		0.929
(1.00) 		0.0484
(1.00) 		0.459
(1.00) 		0.556
(1.00)
10q loss 0.378
(1.00) 		0.998
(1.00) 		0.878
(1.00) 		0.832
(1.00) 		0.353
(1.00) 		0.984
(1.00) 		0.875
(1.00) 		0.164
(1.00) 		0.294
(1.00) 		0.13
(1.00) 		0.186
(1.00) 		0.081
(1.00) 		0.709
(1.00) 		0.0797
(1.00)
11p loss 0.349
(1.00) 		0.769
(1.00) 		0.6
(1.00) 		0.712
(1.00) 		0.209
(1.00) 		0.523
(1.00) 		0.305
(1.00) 		0.253
(1.00) 		0.749
(1.00) 		0.385
(1.00) 		0.453
(1.00) 		0.759
(1.00) 		0.446
(1.00) 		0.723
(1.00)
11q loss 0.663
(1.00) 		0.0338
(1.00) 		0.402
(1.00) 		0.963
(1.00) 		0.89
(1.00) 		0.743
(1.00) 		0.0757
(1.00) 		0.807
(1.00) 		0.503
(1.00) 		0.296
(1.00) 		0.864
(1.00) 		0.639
(1.00) 		0.463
(1.00) 		0.792
(1.00)
12p loss 0.505
(1.00) 		0.00216
(1.00) 		0.605
(1.00) 		3.63e-13
(3.95e-10) 		0.163
(1.00) 		0.863
(1.00) 		0.698
(1.00) 		0.443
(1.00) 		1
(1.00) 		0.23
(1.00) 		0.916
(1.00) 		0.401
(1.00) 		0.273
(1.00)
12q loss 0.864
(1.00) 		0.185
(1.00) 		0.27
(1.00) 		1.32e-16
(1.44e-13) 		0.119
(1.00) 		0.483
(1.00) 		0.551
(1.00) 		0.28
(1.00) 		1
(1.00) 		0.22
(1.00) 		0.327
(1.00)
13q loss 0.0133
(1.00) 		0.56
(1.00) 		0.514
(1.00) 		0.0744
(1.00) 		0.336
(1.00) 		0.944
(1.00) 		0.16
(1.00) 		0.284
(1.00) 		0.0223
(1.00) 		0.753
(1.00) 		0.72
(1.00) 		0.046
(1.00) 		0.229
(1.00) 		0.0149
(1.00)
14q loss 0.195
(1.00) 		0.211
(1.00) 		0.0719
(1.00) 		0.227
(1.00) 		0.167
(1.00) 		0.0146
(1.00) 		0.149
(1.00) 		0.333
(1.00) 		0.334
(1.00) 		0.433
(1.00) 		0.758
(1.00) 		0.899
(1.00) 		0.387
(1.00) 		0.19
(1.00)
15q loss 0.7
(1.00) 		0.313
(1.00) 		0.0469
(1.00) 		0.734
(1.00) 		0.0678
(1.00) 		0.0399
(1.00) 		0.132
(1.00) 		0.69
(1.00) 		0.0279
(1.00) 		1
(1.00) 		0.406
(1.00) 		0.274
(1.00) 		0.199
(1.00) 		0.921
(1.00)
16p loss 0.188
(1.00) 		0.754
(1.00) 		0.86
(1.00) 		0.453
(1.00) 		0.767
(1.00) 		0.77
(1.00) 		0.465
(1.00) 		0.467
(1.00) 		0.814
(1.00) 		1
(1.00) 		0.313
(1.00) 		0.639
(1.00) 		0.8
(1.00) 		0.684
(1.00)
16q loss 0.133
(1.00) 		0.164
(1.00) 		0.141
(1.00) 		1.77e-06
(0.00192) 		0.674
(1.00) 		0.816
(1.00) 		0.483
(1.00) 		0.127
(1.00) 		0.67
(1.00) 		0.433
(1.00) 		0.0755
(1.00) 		0.739
(1.00) 		0.26
(1.00) 		0.59
(1.00)
17p loss 0.201
(1.00) 		0.835
(1.00) 		0.426
(1.00) 		0.0401
(1.00) 		0.546
(1.00) 		0.367
(1.00) 		0.205
(1.00) 		0.343
(1.00) 		0.947
(1.00) 		0.531
(1.00) 		0.387
(1.00) 		0.0314
(1.00) 		0.269
(1.00) 		0.654
(1.00)
17q loss 0.798
(1.00) 		0.793
(1.00) 		0.06
(1.00) 		0.453
(1.00) 		0.954
(1.00) 		0.691
(1.00) 		0.767
(1.00) 		0.191
(1.00) 		0.327
(1.00) 		0.508
(1.00) 		0.967
(1.00) 		0.357
(1.00) 		0.323
(1.00) 		0.185
(1.00)
18p loss 0.434
(1.00) 		0.879
(1.00) 		0.691
(1.00) 		0.997
(1.00) 		0.864
(1.00) 		0.851
(1.00) 		0.431
(1.00) 		0.37
(1.00) 		0.564
(1.00) 		0.618
(1.00) 		0.0756
(1.00) 		0.862
(1.00) 		0.679
(1.00) 		0.426
(1.00)
18q loss 0.499
(1.00) 		0.116
(1.00) 		0.269
(1.00) 		0.768
(1.00) 		0.332
(1.00) 		0.421
(1.00) 		0.971
(1.00) 		0.213
(1.00) 		0.707
(1.00) 		1
(1.00) 		0.134
(1.00) 		0.231
(1.00) 		0.351
(1.00) 		0.294
(1.00)
19p loss 0.0716
(1.00) 		0.669
(1.00) 		0.862
(1.00) 		0.118
(1.00) 		0.137
(1.00) 		0.666
(1.00) 		0.0731
(1.00) 		0.21
(1.00) 		0.168
(1.00) 		0.0188
(1.00) 		0.961
(1.00) 		0.975
(1.00) 		0.105
(1.00) 		0.111
(1.00)
19q loss 0.572
(1.00) 		0.197
(1.00) 		0.839
(1.00) 		0.26
(1.00) 		0.00187
(1.00) 		0.594
(1.00) 		0.217
(1.00) 		0.461
(1.00) 		0.847
(1.00) 		0.11
(1.00) 		0.156
(1.00) 		0.794
(1.00) 		0.321
(1.00) 		0.035
(1.00)
20p loss 0.06
(1.00) 		0.868
(1.00) 		1
(1.00) 		0.97
(1.00) 		0.35
(1.00) 		0.535
(1.00) 		0.823
(1.00) 		0.0636
(1.00) 		1
(1.00) 		0.909
(1.00) 		0.0366
(1.00) 		0.958
(1.00) 		0.705
(1.00)
20q loss 0.376
(1.00) 		0.907
(1.00) 		0.575
(1.00) 		0.966
(1.00) 		0.777
(1.00) 		0.487
(1.00) 		0.846
(1.00) 		0.884
(1.00) 		1
(1.00) 		0.12
(1.00) 		0.906
(1.00) 		0.758
(1.00) 		0.178
(1.00)
21q loss 0.454
(1.00) 		0.314
(1.00) 		0.0125
(1.00) 		0.564
(1.00) 		0.53
(1.00) 		0.53
(1.00) 		0.696
(1.00) 		0.318
(1.00) 		0.126
(1.00) 		0.741
(1.00) 		0.253
(1.00) 		0.102
(1.00) 		0.935
(1.00) 		0.374
(1.00)
22q loss 0.648
(1.00) 		0.126
(1.00) 		0.0605
(1.00) 		0.327
(1.00) 		0.00119
(1.00) 		0.24
(1.00) 		2.82e-05
(0.0305) 		0.522
(1.00) 		0.00232
(1.00) 		1
(1.00) 		0.715
(1.00) 		0.0676
(1.00) 		0.474
(1.00) 		0.0836
(1.00)
Xq loss 0.884
(1.00) 		0.284
(1.00) 		0.329
(1.00) 		0.949
(1.00) 		6.63e-05
(0.0714) 		0.69
(1.00) 		0.224
(1.00) 		0.893
(1.00) 		0.512
(1.00) 		1
(1.00) 		0.681
(1.00) 		0.841
(1.00) 		0.886
(1.00) 		0.788
(1.00)
Clustering Approach #1: '1p gain mutation analysis'

Table S1.  Get Full Table Description of clustering approach #1: '1p gain mutation analysis'

Cluster Labels 1P GAIN MUTATED 1P GAIN WILD-TYPE
Number of samples 21 304
'1p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 3.79e-06 (Chi-square test), Q value = 0.0041

Table S2.  Clustering Approach #1: '1p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
1P GAIN MUTATED 4 0 0 0 17
1P GAIN WILD-TYPE 3 1 1 1 298

Figure S1.  Get High-res Image Clustering Approach #1: '1p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #2: '1q gain mutation analysis'

Table S3.  Get Full Table Description of clustering approach #2: '1q gain mutation analysis'

Cluster Labels 1Q GAIN MUTATED 1Q GAIN WILD-TYPE
Number of samples 69 256
Clustering Approach #3: '2p gain mutation analysis'

Table S4.  Get Full Table Description of clustering approach #3: '2p gain mutation analysis'

Cluster Labels 2P GAIN MUTATED 2P GAIN WILD-TYPE
Number of samples 76 249
Clustering Approach #4: '2q gain mutation analysis'

Table S5.  Get Full Table Description of clustering approach #4: '2q gain mutation analysis'

Cluster Labels 2Q GAIN MUTATED 2Q GAIN WILD-TYPE
Number of samples 35 290
'2q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 5.78e-05 (Chi-square test), Q value = 0.062

Table S6.  Clustering Approach #4: '2q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
2Q GAIN MUTATED 4 1 0 0 30
2Q GAIN WILD-TYPE 3 0 1 1 285

Figure S2.  Get High-res Image Clustering Approach #4: '2q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #5: '3p gain mutation analysis'

Table S7.  Get Full Table Description of clustering approach #5: '3p gain mutation analysis'

Cluster Labels 3P GAIN MUTATED 3P GAIN WILD-TYPE
Number of samples 35 290
Clustering Approach #6: '3q gain mutation analysis'

Table S8.  Get Full Table Description of clustering approach #6: '3q gain mutation analysis'

Cluster Labels 3Q GAIN MUTATED 3Q GAIN WILD-TYPE
Number of samples 148 177
Clustering Approach #7: '4p gain mutation analysis'

Table S9.  Get Full Table Description of clustering approach #7: '4p gain mutation analysis'

Cluster Labels 4P GAIN MUTATED 4P GAIN WILD-TYPE
Number of samples 14 311
Clustering Approach #8: '4q gain mutation analysis'

Table S10.  Get Full Table Description of clustering approach #8: '4q gain mutation analysis'

Cluster Labels 4Q GAIN MUTATED 4Q GAIN WILD-TYPE
Number of samples 6 319
Clustering Approach #9: '5p gain mutation analysis'

Table S11.  Get Full Table Description of clustering approach #9: '5p gain mutation analysis'

Cluster Labels 5P GAIN MUTATED 5P GAIN WILD-TYPE
Number of samples 149 176
Clustering Approach #10: '5q gain mutation analysis'

Table S12.  Get Full Table Description of clustering approach #10: '5q gain mutation analysis'

Cluster Labels 5Q GAIN MUTATED 5Q GAIN WILD-TYPE
Number of samples 32 293
Clustering Approach #11: '6p gain mutation analysis'

Table S13.  Get Full Table Description of clustering approach #11: '6p gain mutation analysis'

Cluster Labels 6P GAIN MUTATED 6P GAIN WILD-TYPE
Number of samples 30 295
Clustering Approach #12: '6q gain mutation analysis'

Table S14.  Get Full Table Description of clustering approach #12: '6q gain mutation analysis'

Cluster Labels 6Q GAIN MUTATED 6Q GAIN WILD-TYPE
Number of samples 17 308
Clustering Approach #13: '7p gain mutation analysis'

Table S15.  Get Full Table Description of clustering approach #13: '7p gain mutation analysis'

Cluster Labels 7P GAIN MUTATED 7P GAIN WILD-TYPE
Number of samples 88 237
Clustering Approach #14: '7q gain mutation analysis'

Table S16.  Get Full Table Description of clustering approach #14: '7q gain mutation analysis'

Cluster Labels 7Q GAIN MUTATED 7Q GAIN WILD-TYPE
Number of samples 84 241
Clustering Approach #15: '8p gain mutation analysis'

Table S17.  Get Full Table Description of clustering approach #15: '8p gain mutation analysis'

Cluster Labels 8P GAIN MUTATED 8P GAIN WILD-TYPE
Number of samples 46 279
Clustering Approach #16: '8q gain mutation analysis'

Table S18.  Get Full Table Description of clustering approach #16: '8q gain mutation analysis'

Cluster Labels 8Q GAIN MUTATED 8Q GAIN WILD-TYPE
Number of samples 95 230
Clustering Approach #17: '9p gain mutation analysis'

Table S19.  Get Full Table Description of clustering approach #17: '9p gain mutation analysis'

Cluster Labels 9P GAIN MUTATED 9P GAIN WILD-TYPE
Number of samples 19 306
Clustering Approach #18: '9q gain mutation analysis'

Table S20.  Get Full Table Description of clustering approach #18: '9q gain mutation analysis'

Cluster Labels 9Q GAIN MUTATED 9Q GAIN WILD-TYPE
Number of samples 27 298
Clustering Approach #19: '10p gain mutation analysis'

Table S21.  Get Full Table Description of clustering approach #19: '10p gain mutation analysis'

Cluster Labels 10P GAIN MUTATED 10P GAIN WILD-TYPE
Number of samples 15 310
Clustering Approach #20: '10q gain mutation analysis'

Table S22.  Get Full Table Description of clustering approach #20: '10q gain mutation analysis'

Cluster Labels 10Q GAIN MUTATED 10Q GAIN WILD-TYPE
Number of samples 6 319
Clustering Approach #21: '11p gain mutation analysis'

Table S23.  Get Full Table Description of clustering approach #21: '11p gain mutation analysis'

Cluster Labels 11P GAIN MUTATED 11P GAIN WILD-TYPE
Number of samples 18 307
'11p gain mutation analysis' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 2e-06 (t-test), Q value = 0.0022

Table S24.  Clustering Approach #21: '11p gain mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 28.5 (39.7)
11P GAIN MUTATED 4 0.0 (0.0)
11P GAIN WILD-TYPE 50 30.8 (40.4)

Figure S3.  Get High-res Image Clustering Approach #21: '11p gain mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #22: '11q gain mutation analysis'

Table S25.  Get Full Table Description of clustering approach #22: '11q gain mutation analysis'

Cluster Labels 11Q GAIN MUTATED 11Q GAIN WILD-TYPE
Number of samples 21 304
Clustering Approach #23: '12p gain mutation analysis'

Table S26.  Get Full Table Description of clustering approach #23: '12p gain mutation analysis'

Cluster Labels 12P GAIN MUTATED 12P GAIN WILD-TYPE
Number of samples 90 235
Clustering Approach #24: '12q gain mutation analysis'

Table S27.  Get Full Table Description of clustering approach #24: '12q gain mutation analysis'

Cluster Labels 12Q GAIN MUTATED 12Q GAIN WILD-TYPE
Number of samples 40 285
Clustering Approach #25: '13q gain mutation analysis'

Table S28.  Get Full Table Description of clustering approach #25: '13q gain mutation analysis'

Cluster Labels 13Q GAIN MUTATED 13Q GAIN WILD-TYPE
Number of samples 8 317
Clustering Approach #26: '14q gain mutation analysis'

Table S29.  Get Full Table Description of clustering approach #26: '14q gain mutation analysis'

Cluster Labels 14Q GAIN MUTATED 14Q GAIN WILD-TYPE
Number of samples 31 294
Clustering Approach #27: '15q gain mutation analysis'

Table S30.  Get Full Table Description of clustering approach #27: '15q gain mutation analysis'

Cluster Labels 15Q GAIN MUTATED 15Q GAIN WILD-TYPE
Number of samples 29 296
Clustering Approach #28: '16p gain mutation analysis'

Table S31.  Get Full Table Description of clustering approach #28: '16p gain mutation analysis'

Cluster Labels 16P GAIN MUTATED 16P GAIN WILD-TYPE
Number of samples 15 310
Clustering Approach #29: '16q gain mutation analysis'

Table S32.  Get Full Table Description of clustering approach #29: '16q gain mutation analysis'

Cluster Labels 16Q GAIN MUTATED 16Q GAIN WILD-TYPE
Number of samples 19 306
Clustering Approach #30: '17p gain mutation analysis'

Table S33.  Get Full Table Description of clustering approach #30: '17p gain mutation analysis'

Cluster Labels 17P GAIN MUTATED 17P GAIN WILD-TYPE
Number of samples 9 316
'17p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 3.79e-07 (Chi-square test), Q value = 0.00041

Table S34.  Clustering Approach #30: '17p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
17P GAIN MUTATED 0 1 0 0 8
17P GAIN WILD-TYPE 7 0 1 1 307

Figure S4.  Get High-res Image Clustering Approach #30: '17p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #31: '17q gain mutation analysis'

Table S35.  Get Full Table Description of clustering approach #31: '17q gain mutation analysis'

Cluster Labels 17Q GAIN MUTATED 17Q GAIN WILD-TYPE
Number of samples 35 290
Clustering Approach #32: '18p gain mutation analysis'

Table S36.  Get Full Table Description of clustering approach #32: '18p gain mutation analysis'

Cluster Labels 18P GAIN MUTATED 18P GAIN WILD-TYPE
Number of samples 51 274
Clustering Approach #33: '18q gain mutation analysis'

Table S37.  Get Full Table Description of clustering approach #33: '18q gain mutation analysis'

Cluster Labels 18Q GAIN MUTATED 18Q GAIN WILD-TYPE
Number of samples 38 287
Clustering Approach #34: '19p gain mutation analysis'

Table S38.  Get Full Table Description of clustering approach #34: '19p gain mutation analysis'

Cluster Labels 19P GAIN MUTATED 19P GAIN WILD-TYPE
Number of samples 33 292
'19p gain mutation analysis' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 1.77e-06 (t-test), Q value = 0.0019

Table S39.  Clustering Approach #34: '19p gain mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 28.5 (39.7)
19P GAIN MUTATED 6 0.0 (0.0)
19P GAIN WILD-TYPE 48 32.1 (40.7)

Figure S5.  Get High-res Image Clustering Approach #34: '19p gain mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

'19p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 5.63e-06 (Chi-square test), Q value = 0.0061

Table S40.  Clustering Approach #34: '19p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
19P GAIN MUTATED 5 0 0 0 28
19P GAIN WILD-TYPE 2 1 1 1 287

Figure S6.  Get High-res Image Clustering Approach #34: '19p gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #35: '19q gain mutation analysis'

Table S41.  Get Full Table Description of clustering approach #35: '19q gain mutation analysis'

Cluster Labels 19Q GAIN MUTATED 19Q GAIN WILD-TYPE
Number of samples 46 279
Clustering Approach #36: '20p gain mutation analysis'

Table S42.  Get Full Table Description of clustering approach #36: '20p gain mutation analysis'

Cluster Labels 20P GAIN MUTATED 20P GAIN WILD-TYPE
Number of samples 91 234
Clustering Approach #37: '20q gain mutation analysis'

Table S43.  Get Full Table Description of clustering approach #37: '20q gain mutation analysis'

Cluster Labels 20Q GAIN MUTATED 20Q GAIN WILD-TYPE
Number of samples 81 244
Clustering Approach #38: '21q gain mutation analysis'

Table S44.  Get Full Table Description of clustering approach #38: '21q gain mutation analysis'

Cluster Labels 21Q GAIN MUTATED 21Q GAIN WILD-TYPE
Number of samples 18 307
'21q gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 5.96e-05 (Chi-square test), Q value = 0.064

Table S45.  Clustering Approach #38: '21q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
21Q GAIN MUTATED 2 0 0 1 15
21Q GAIN WILD-TYPE 5 1 1 0 300

Figure S7.  Get High-res Image Clustering Approach #38: '21q gain mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #39: '22q gain mutation analysis'

Table S46.  Get Full Table Description of clustering approach #39: '22q gain mutation analysis'

Cluster Labels 22Q GAIN MUTATED 22Q GAIN WILD-TYPE
Number of samples 77 248
Clustering Approach #40: 'Xq gain mutation analysis'

Table S47.  Get Full Table Description of clustering approach #40: 'Xq gain mutation analysis'

Cluster Labels XQ GAIN MUTATED XQ GAIN WILD-TYPE
Number of samples 9 316
Clustering Approach #41: '1p loss mutation analysis'

Table S48.  Get Full Table Description of clustering approach #41: '1p loss mutation analysis'

Cluster Labels 1P LOSS MUTATED 1P LOSS WILD-TYPE
Number of samples 48 277
Clustering Approach #42: '1q loss mutation analysis'

Table S49.  Get Full Table Description of clustering approach #42: '1q loss mutation analysis'

Cluster Labels 1Q LOSS MUTATED 1Q LOSS WILD-TYPE
Number of samples 8 317
Clustering Approach #43: '2q loss mutation analysis'

Table S50.  Get Full Table Description of clustering approach #43: '2q loss mutation analysis'

Cluster Labels 2Q LOSS MUTATED 2Q LOSS WILD-TYPE
Number of samples 9 316
Clustering Approach #44: '3p loss mutation analysis'

Table S51.  Get Full Table Description of clustering approach #44: '3p loss mutation analysis'

Cluster Labels 3P LOSS MUTATED 3P LOSS WILD-TYPE
Number of samples 139 186
Clustering Approach #45: '3q loss mutation analysis'

Table S52.  Get Full Table Description of clustering approach #45: '3q loss mutation analysis'

Cluster Labels 3Q LOSS MUTATED 3Q LOSS WILD-TYPE
Number of samples 25 300
Clustering Approach #46: '4p loss mutation analysis'

Table S53.  Get Full Table Description of clustering approach #46: '4p loss mutation analysis'

Cluster Labels 4P LOSS MUTATED 4P LOSS WILD-TYPE
Number of samples 137 188
Clustering Approach #47: '4q loss mutation analysis'

Table S54.  Get Full Table Description of clustering approach #47: '4q loss mutation analysis'

Cluster Labels 4Q LOSS MUTATED 4Q LOSS WILD-TYPE
Number of samples 118 207
Clustering Approach #48: '5p loss mutation analysis'

Table S55.  Get Full Table Description of clustering approach #48: '5p loss mutation analysis'

Cluster Labels 5P LOSS MUTATED 5P LOSS WILD-TYPE
Number of samples 23 302
Clustering Approach #49: '5q loss mutation analysis'

Table S56.  Get Full Table Description of clustering approach #49: '5q loss mutation analysis'

Cluster Labels 5Q LOSS MUTATED 5Q LOSS WILD-TYPE
Number of samples 120 205
Clustering Approach #50: '6p loss mutation analysis'

Table S57.  Get Full Table Description of clustering approach #50: '6p loss mutation analysis'

Cluster Labels 6P LOSS MUTATED 6P LOSS WILD-TYPE
Number of samples 34 291
Clustering Approach #51: '6q loss mutation analysis'

Table S58.  Get Full Table Description of clustering approach #51: '6q loss mutation analysis'

Cluster Labels 6Q LOSS MUTATED 6Q LOSS WILD-TYPE
Number of samples 37 288
Clustering Approach #52: '7p loss mutation analysis'

Table S59.  Get Full Table Description of clustering approach #52: '7p loss mutation analysis'

Cluster Labels 7P LOSS MUTATED 7P LOSS WILD-TYPE
Number of samples 20 305
Clustering Approach #53: '7q loss mutation analysis'

Table S60.  Get Full Table Description of clustering approach #53: '7q loss mutation analysis'

Cluster Labels 7Q LOSS MUTATED 7Q LOSS WILD-TYPE
Number of samples 10 315
Clustering Approach #54: '8p loss mutation analysis'

Table S61.  Get Full Table Description of clustering approach #54: '8p loss mutation analysis'

Cluster Labels 8P LOSS MUTATED 8P LOSS WILD-TYPE
Number of samples 96 229
Clustering Approach #55: '8q loss mutation analysis'

Table S62.  Get Full Table Description of clustering approach #55: '8q loss mutation analysis'

Cluster Labels 8Q LOSS MUTATED 8Q LOSS WILD-TYPE
Number of samples 11 314
'8q loss mutation analysis' versus 'STOPPEDSMOKINGYEAR'

P value = 8.72e-07 (t-test), Q value = 0.00095

Table S63.  Clustering Approach #55: '8q loss mutation analysis' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

nPatients Mean (Std.Dev)
ALL 214 1997.4 (11.5)
8Q LOSS MUTATED 6 2007.3 (2.3)
8Q LOSS WILD-TYPE 208 1997.1 (11.6)

Figure S8.  Get High-res Image Clustering Approach #55: '8q loss mutation analysis' versus Clinical Feature #12: 'STOPPEDSMOKINGYEAR'

Clustering Approach #56: '9p loss mutation analysis'

Table S64.  Get Full Table Description of clustering approach #56: '9p loss mutation analysis'

Cluster Labels 9P LOSS MUTATED 9P LOSS WILD-TYPE
Number of samples 134 191
Clustering Approach #57: '9q loss mutation analysis'

Table S65.  Get Full Table Description of clustering approach #57: '9q loss mutation analysis'

Cluster Labels 9Q LOSS MUTATED 9Q LOSS WILD-TYPE
Number of samples 84 241
Clustering Approach #58: '10p loss mutation analysis'

Table S66.  Get Full Table Description of clustering approach #58: '10p loss mutation analysis'

Cluster Labels 10P LOSS MUTATED 10P LOSS WILD-TYPE
Number of samples 71 254
Clustering Approach #59: '10q loss mutation analysis'

Table S67.  Get Full Table Description of clustering approach #59: '10q loss mutation analysis'

Cluster Labels 10Q LOSS MUTATED 10Q LOSS WILD-TYPE
Number of samples 70 255
Clustering Approach #60: '11p loss mutation analysis'

Table S68.  Get Full Table Description of clustering approach #60: '11p loss mutation analysis'

Cluster Labels 11P LOSS MUTATED 11P LOSS WILD-TYPE
Number of samples 50 275
Clustering Approach #61: '11q loss mutation analysis'

Table S69.  Get Full Table Description of clustering approach #61: '11q loss mutation analysis'

Cluster Labels 11Q LOSS MUTATED 11Q LOSS WILD-TYPE
Number of samples 32 293
Clustering Approach #62: '12p loss mutation analysis'

Table S70.  Get Full Table Description of clustering approach #62: '12p loss mutation analysis'

Cluster Labels 12P LOSS MUTATED 12P LOSS WILD-TYPE
Number of samples 5 320
'12p loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 3.63e-13 (Chi-square test), Q value = 4e-10

Table S71.  Clustering Approach #62: '12p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
12P LOSS MUTATED 0 0 0 1 4
12P LOSS WILD-TYPE 7 1 1 0 311

Figure S9.  Get High-res Image Clustering Approach #62: '12p loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #63: '12q loss mutation analysis'

Table S72.  Get Full Table Description of clustering approach #63: '12q loss mutation analysis'

Cluster Labels 12Q LOSS MUTATED 12Q LOSS WILD-TYPE
Number of samples 4 321
'12q loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 1.32e-16 (Chi-square test), Q value = 1.4e-13

Table S73.  Clustering Approach #63: '12q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
12Q LOSS MUTATED 0 0 0 1 3
12Q LOSS WILD-TYPE 7 1 1 0 312

Figure S10.  Get High-res Image Clustering Approach #63: '12q loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Clustering Approach #64: '13q loss mutation analysis'

Table S74.  Get Full Table Description of clustering approach #64: '13q loss mutation analysis'

Cluster Labels 13Q LOSS MUTATED 13Q LOSS WILD-TYPE
Number of samples 123 202
Clustering Approach #65: '14q loss mutation analysis'

Table S75.  Get Full Table Description of clustering approach #65: '14q loss mutation analysis'

Cluster Labels 14Q LOSS MUTATED 14Q LOSS WILD-TYPE
Number of samples 60 265
Clustering Approach #66: '15q loss mutation analysis'

Table S76.  Get Full Table Description of clustering approach #66: '15q loss mutation analysis'

Cluster Labels 15Q LOSS MUTATED 15Q LOSS WILD-TYPE
Number of samples 38 287
Clustering Approach #67: '16p loss mutation analysis'

Table S77.  Get Full Table Description of clustering approach #67: '16p loss mutation analysis'

Cluster Labels 16P LOSS MUTATED 16P LOSS WILD-TYPE
Number of samples 49 276
Clustering Approach #68: '16q loss mutation analysis'

Table S78.  Get Full Table Description of clustering approach #68: '16q loss mutation analysis'

Cluster Labels 16Q LOSS MUTATED 16Q LOSS WILD-TYPE
Number of samples 60 265
'16q loss mutation analysis' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 1.77e-06 (t-test), Q value = 0.0019

Table S79.  Clustering Approach #68: '16q loss mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

nPatients Mean (Std.Dev)
ALL 54 28.5 (39.7)
16Q LOSS MUTATED 6 0.0 (0.0)
16Q LOSS WILD-TYPE 48 32.1 (40.7)

Figure S11.  Get High-res Image Clustering Approach #68: '16q loss mutation analysis' versus Clinical Feature #4: 'KARNOFSKY.PERFORMANCE.SCORE'

Clustering Approach #69: '17p loss mutation analysis'

Table S80.  Get Full Table Description of clustering approach #69: '17p loss mutation analysis'

Cluster Labels 17P LOSS MUTATED 17P LOSS WILD-TYPE
Number of samples 116 209
Clustering Approach #70: '17q loss mutation analysis'

Table S81.  Get Full Table Description of clustering approach #70: '17q loss mutation analysis'

Cluster Labels 17Q LOSS MUTATED 17Q LOSS WILD-TYPE
Number of samples 20 305
Clustering Approach #71: '18p loss mutation analysis'

Table S82.  Get Full Table Description of clustering approach #71: '18p loss mutation analysis'

Cluster Labels 18P LOSS MUTATED 18P LOSS WILD-TYPE
Number of samples 36 289
Clustering Approach #72: '18q loss mutation analysis'

Table S83.  Get Full Table Description of clustering approach #72: '18q loss mutation analysis'

Cluster Labels 18Q LOSS MUTATED 18Q LOSS WILD-TYPE
Number of samples 66 259
Clustering Approach #73: '19p loss mutation analysis'

Table S84.  Get Full Table Description of clustering approach #73: '19p loss mutation analysis'

Cluster Labels 19P LOSS MUTATED 19P LOSS WILD-TYPE
Number of samples 52 273
Clustering Approach #74: '19q loss mutation analysis'

Table S85.  Get Full Table Description of clustering approach #74: '19q loss mutation analysis'

Cluster Labels 19Q LOSS MUTATED 19Q LOSS WILD-TYPE
Number of samples 36 289
Clustering Approach #75: '20p loss mutation analysis'

Table S86.  Get Full Table Description of clustering approach #75: '20p loss mutation analysis'

Cluster Labels 20P LOSS MUTATED 20P LOSS WILD-TYPE
Number of samples 16 309
Clustering Approach #76: '20q loss mutation analysis'

Table S87.  Get Full Table Description of clustering approach #76: '20q loss mutation analysis'

Cluster Labels 20Q LOSS MUTATED 20Q LOSS WILD-TYPE
Number of samples 17 308
Clustering Approach #77: '21q loss mutation analysis'

Table S88.  Get Full Table Description of clustering approach #77: '21q loss mutation analysis'

Cluster Labels 21Q LOSS MUTATED 21Q LOSS WILD-TYPE
Number of samples 99 226
Clustering Approach #78: '22q loss mutation analysis'

Table S89.  Get Full Table Description of clustering approach #78: '22q loss mutation analysis'

Cluster Labels 22Q LOSS MUTATED 22Q LOSS WILD-TYPE
Number of samples 33 292
'22q loss mutation analysis' versus 'PATHOLOGY.N'

P value = 2.82e-05 (Chi-square test), Q value = 0.03

Table S90.  Clustering Approach #78: '22q loss mutation analysis' versus Clinical Feature #7: 'PATHOLOGY.N'

nPatients N0 N1 N2 N3
ALL 201 91 26 5
22Q LOSS MUTATED 14 9 7 3
22Q LOSS WILD-TYPE 187 82 19 2

Figure S12.  Get High-res Image Clustering Approach #78: '22q loss mutation analysis' versus Clinical Feature #7: 'PATHOLOGY.N'

Clustering Approach #79: 'Xq loss mutation analysis'

Table S91.  Get Full Table Description of clustering approach #79: 'Xq loss mutation analysis'

Cluster Labels XQ LOSS MUTATED XQ LOSS WILD-TYPE
Number of samples 13 312
'Xq loss mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 6.63e-05 (Chi-square test), Q value = 0.071

Table S92.  Clustering Approach #79: 'Xq loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

nPatients LUNG BASALOID SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARCINOMA LUNG PAPILLARY SQUAMOUS CELL CARICNOMA LUNG SMALL CELL SQUAMOUS CELL CARCINOMA LUNG SQUAMOUS CELL CARCINOMA- NOT OTHERWISE SPECIFIED (NOS)
ALL 7 1 1 1 315
XQ LOSS MUTATED 0 0 0 1 12
XQ LOSS WILD-TYPE 7 1 1 0 303

Figure S13.  Get High-res Image Clustering Approach #79: 'Xq loss mutation analysis' versus Clinical Feature #5: 'HISTOLOGICAL.TYPE'

Methods & Data
Input

  • Cluster data file = broad_values_by_arm.mutsig.cluster.txt

  • Clinical data file = LUSC-TP.clin.merged.picked.txt

  • Number of patients = 325

  • Number of clustering approaches = 79

  • Number of selected clinical features = 14

  • Exclude small clusters that include fewer than K patients, K = 3

Survival analysis

For survival clinical features, the Kaplan-Meier survival curves of tumors with and without gene mutations were plotted and the statistical significance P values were estimated by logrank test (Bland and Altman 2004) using the 'survdiff' function in R

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.test' function in R

Fisher's exact test

For binary clinical features, two-tailed Fisher's exact tests (Fisher 1922) were used to estimate the P values using the 'fisher.test' function in R

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

Q value calculation

For multiple hypothesis correction, Q value is the False Discovery Rate (FDR) analogue of the P value (Benjamini and Hochberg 1995), defined as the minimum FDR at which the test may be called significant. We used the 'Benjamini and Hochberg' method of 'p.adjust' function in R to convert P values into Q values.

Download Results

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References
[1] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[2] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)
[3] Fisher, R.A., On the interpretation of chi-square from contingency tables, and the calculation of P, Journal of the Royal Statistical Society 85(1):87-94 (1922)
[4] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)
[5] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)
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