Correlation between copy number variations of arm-level result and selected clinical features

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 36 different clustering approaches and 15 clinical features across 284 patients, 29 significant findings detected with Q value < 0.25.

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 '4p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '4q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '5p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '5q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '7p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '7q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '11p gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE'.
2 subtypes identified in current cancer cohort by '12p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '12q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '14q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '16p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '16q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '17p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '17q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '19p gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '19q gain mutation analysis'. These subtypes correlate to 'HISTOLOGICAL.TYPE' and 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '20p gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '20q gain mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
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 '2p loss mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '2q loss mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '3q loss mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '9p loss mutation analysis'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
2 subtypes identified in current cancer cohort by '9q loss mutation analysis'. These subtypes correlate to 'NEOPLASM.DISEASESTAGE'.
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 correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '11q loss mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '13q loss mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
2 subtypes identified in current cancer cohort by '15q loss mutation analysis'. These subtypes correlate to 'NUMBER.OF.LYMPH.NODES'.
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 '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 '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 do not correlate to any clinical features.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 36 different clustering approaches and 15 clinical features. Shown in the table are P values (Q values). Thresholded by Q value < 0.25, 29 significant findings detected.


Clinical Features	Time to Death	AGE	GENDER	HISTOLOGICAL TYPE	RADIATIONS RADIATION REGIMENINDICATION	RADIATIONEXPOSURE	DISTANT METASTASIS	EXTRATHYROIDAL EXTENSION	LYMPH NODE METASTASIS	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES	TUMOR STAGECODE	NEOPLASM DISEASESTAGE	MULTIFOCALITY	TUMOR SIZE
Statistical Tests	logrank test	t-test	Fisher's exact test	Chi-square test	Fisher's exact test	Fisher's exact test	Chi-square test	Chi-square test	Chi-square test	Chi-square test	t-test	t-test	Chi-square test	Fisher's exact test	t-test
1q gain	1 (1.00)	0.166 (1.00)	0.428 (1.00)	0.295 (1.00)	0.336 (1.00)	1 (1.00)	0.869 (1.00)	0.00151 (0.701)	0.319 (1.00)	0.798 (1.00)	0.452 (1.00)		0.0796 (1.00)	0.172 (1.00)	0.681 (1.00)
4p gain	0.00468 (1.00)	0.119 (1.00)	1 (1.00)	0.438 (1.00)	0.184 (1.00)	1 (1.00)	0.187 (1.00)	0.962 (1.00)	0.36 (1.00)	0.576 (1.00)	3.02e-14 (1.46e-11)		0.515 (1.00)	0.622 (1.00)	0.479 (1.00)
4q gain	0.00468 (1.00)	0.119 (1.00)	1 (1.00)	0.438 (1.00)	0.184 (1.00)	1 (1.00)	0.187 (1.00)	0.962 (1.00)	0.36 (1.00)	0.576 (1.00)	3.02e-14 (1.46e-11)		0.515 (1.00)	0.622 (1.00)	0.479 (1.00)
5p gain	0.00468 (1.00)	0.0796 (1.00)	1 (1.00)	0.00275 (1.00)	0.336 (1.00)	1 (1.00)	0.0334 (1.00)	0.678 (1.00)	0.195 (1.00)	0.819 (1.00)	2.86e-14 (1.41e-11)		0.0847 (1.00)	0.723 (1.00)	0.0691 (1.00)
5q gain	0.00468 (1.00)	0.0796 (1.00)	1 (1.00)	0.00275 (1.00)	0.336 (1.00)	1 (1.00)	0.0334 (1.00)	0.678 (1.00)	0.195 (1.00)	0.819 (1.00)	2.86e-14 (1.41e-11)		0.0847 (1.00)	0.723 (1.00)	0.0691 (1.00)
7p gain	1 (1.00)	0.0831 (1.00)	1 (1.00)	0.0117 (1.00)	1 (1.00)	1 (1.00)	0.0742 (1.00)	0.509 (1.00)	0.0908 (1.00)	0.399 (1.00)	2.75e-14 (1.37e-11)		0.0372 (1.00)	0.749 (1.00)	0.187 (1.00)
7q gain	1 (1.00)	0.0477 (1.00)	0.737 (1.00)	0.000549 (0.257)	1 (1.00)	1 (1.00)	0.125 (1.00)	0.73 (1.00)	0.0499 (1.00)	0.105 (1.00)	2.75e-14 (1.37e-11)		0.0723 (1.00)	0.769 (1.00)	0.186 (1.00)
11p gain	1 (1.00)	0.725 (1.00)	1 (1.00)	0.0003 (0.141)	1 (1.00)	1 (1.00)	0.286 (1.00)	0.594 (1.00)	0.566 (1.00)	0.905 (1.00)			0.446 (1.00)	0.622 (1.00)	0.0135 (1.00)
12p gain	1 (1.00)	0.286 (1.00)	0.455 (1.00)	0.00462 (1.00)	1 (1.00)	1 (1.00)	0.0215 (1.00)	0.59 (1.00)	0.152 (1.00)	0.821 (1.00)	2.86e-14 (1.41e-11)		0.271 (1.00)	1 (1.00)	0.389 (1.00)
12q gain	1 (1.00)	0.286 (1.00)	0.455 (1.00)	0.00462 (1.00)	1 (1.00)	1 (1.00)	0.0215 (1.00)	0.59 (1.00)	0.152 (1.00)	0.821 (1.00)	2.86e-14 (1.41e-11)		0.271 (1.00)	1 (1.00)	0.389 (1.00)
14q gain	1 (1.00)	0.536 (1.00)	0.333 (1.00)	0.0187 (1.00)	1 (1.00)	1 (1.00)	0.122 (1.00)	0.93 (1.00)	0.464 (1.00)	0.694 (1.00)	3.02e-14 (1.46e-11)		0.346 (1.00)	0.37 (1.00)	0.458 (1.00)
16p gain	1 (1.00)	0.499 (1.00)	0.196 (1.00)	0.0837 (1.00)	1 (1.00)	1 (1.00)	0.655 (1.00)	0.899 (1.00)	0.202 (1.00)	0.181 (1.00)	2.97e-14 (1.44e-11)		0.559 (1.00)	0.723 (1.00)	0.494 (1.00)
16q gain	1 (1.00)	0.339 (1.00)	0.333 (1.00)	0.557 (1.00)	1 (1.00)	1 (1.00)	0.516 (1.00)	0.93 (1.00)	0.464 (1.00)	0.694 (1.00)	3.02e-14 (1.46e-11)		0.346 (1.00)	1 (1.00)	0.412 (1.00)
17p gain	1 (1.00)	0.533 (1.00)	0.196 (1.00)	0.00091 (0.423)	1 (1.00)	1 (1.00)	0.248 (1.00)	0.291 (1.00)	0.273 (1.00)	0.802 (1.00)	2.91e-14 (1.42e-11)		0.113 (1.00)	1 (1.00)	0.504 (1.00)
17q gain	1 (1.00)	0.746 (1.00)	0.118 (1.00)	0.00282 (1.00)	1 (1.00)	1 (1.00)	0.168 (1.00)	0.243 (1.00)	0.195 (1.00)	0.819 (1.00)	2.86e-14 (1.41e-11)		0.184 (1.00)	1 (1.00)	0.796 (1.00)
19p gain	1 (1.00)	0.188 (1.00)	0.572 (1.00)	1.02e-09 (4.81e-07)	1 (1.00)	1 (1.00)	0.286 (1.00)	0.594 (1.00)	0.516 (1.00)	0.392 (1.00)	3.08e-14 (1.47e-11)		0.722 (1.00)	1 (1.00)	0.593 (1.00)
19q gain	0.00468 (1.00)	0.0704 (1.00)	1 (1.00)	4.35e-07 (0.000205)	0.184 (1.00)	1 (1.00)	0.187 (1.00)	0.498 (1.00)	0.36 (1.00)	0.576 (1.00)	3.02e-14 (1.46e-11)		0.515 (1.00)	1 (1.00)	0.344 (1.00)
20p gain	1 (1.00)	0.562 (1.00)	0.575 (1.00)	0.00381 (1.00)	1 (1.00)	1 (1.00)	0.187 (1.00)	0.498 (1.00)	0.545 (1.00)	0.861 (1.00)	3.08e-14 (1.47e-11)		0.164 (1.00)	1 (1.00)	0.124 (1.00)
20q gain	1 (1.00)	0.562 (1.00)	0.575 (1.00)	0.00381 (1.00)	1 (1.00)	1 (1.00)	0.187 (1.00)	0.498 (1.00)	0.545 (1.00)	0.861 (1.00)	3.08e-14 (1.47e-11)		0.164 (1.00)	1 (1.00)	0.124 (1.00)
1p loss	1 (1.00)	0.184 (1.00)	0.572 (1.00)	0.378 (1.00)	1 (1.00)	1 (1.00)	0.906 (1.00)	0.594 (1.00)	0.516 (1.00)	0.905 (1.00)			0.0339 (1.00)	0.622 (1.00)
2p loss	1 (1.00)	0.195 (1.00)	0.682 (1.00)	0.0369 (1.00)	1 (1.00)	1 (1.00)	0.248 (1.00)	0.291 (1.00)	0.103 (1.00)	0.802 (1.00)	2.86e-14 (1.41e-11)		0.00421 (1.00)	1 (1.00)	0.948 (1.00)
2q loss	1 (1.00)	0.0385 (1.00)	1 (1.00)	0.0106 (1.00)	1 (1.00)	1 (1.00)	0.362 (1.00)	0.349 (1.00)	0.16 (1.00)	0.764 (1.00)	2.91e-14 (1.42e-11)		0.000772 (0.36)	0.684 (1.00)	0.439 (1.00)
3q loss	1 (1.00)	0.204 (1.00)	1 (1.00)	0.0239 (1.00)	1 (1.00)	1 (1.00)	0.286 (1.00)	0.594 (1.00)	0.516 (1.00)	0.905 (1.00)	3.08e-14 (1.47e-11)		0.0339 (1.00)	1 (1.00)	0.17 (1.00)
9p loss	1 (1.00)	0.909 (1.00)	0.333 (1.00)	0.557 (1.00)	1 (1.00)	1 (1.00)	0.944 (1.00)	0.417 (1.00)	0.54 (1.00)	0.815 (1.00)	0.439 (1.00)		7.71e-05 (0.0362)	0.684 (1.00)	0.863 (1.00)
9q loss	1 (1.00)	0.347 (1.00)	0.682 (1.00)	0.0837 (1.00)	0.301 (1.00)	0.275 (1.00)	0.655 (1.00)	0.291 (1.00)	0.521 (1.00)	0.722 (1.00)	0.76 (1.00)		1.88e-05 (0.00884)	0.723 (1.00)	0.985 (1.00)
10p loss	1 (1.00)	0.0334 (1.00)	1 (1.00)	0.378 (1.00)	1 (1.00)	1 (1.00)	0.906 (1.00)	0.594 (1.00)	0.516 (1.00)	0.905 (1.00)			0.0339 (1.00)	1 (1.00)
10q loss	1 (1.00)	0.0334 (1.00)	1 (1.00)	0.378 (1.00)	1 (1.00)	1 (1.00)	0.906 (1.00)	0.594 (1.00)	0.516 (1.00)	0.905 (1.00)			0.0339 (1.00)	1 (1.00)
11p loss	0.00468 (1.00)	0.031 (1.00)	0.273 (1.00)	0.208 (1.00)	0.184 (1.00)	1 (1.00)	0.187 (1.00)	0.498 (1.00)	0.36 (1.00)	0.576 (1.00)	3.02e-14 (1.46e-11)		0.515 (1.00)	0.622 (1.00)	0.238 (1.00)
11q loss	0.00468 (1.00)	0.0187 (1.00)	0.109 (1.00)	0.111 (1.00)	0.225 (1.00)	1 (1.00)	0.122 (1.00)	0.417 (1.00)	0.243 (1.00)	0.694 (1.00)	2.97e-14 (1.44e-11)		0.186 (1.00)	1 (1.00)	0.238 (1.00)
13q loss	0.00468 (1.00)	0.0339 (1.00)	0.242 (1.00)	0.064 (1.00)	0.37 (1.00)	0.00325 (1.00)	0.364 (1.00)	0.59 (1.00)	0.135 (1.00)	0.943 (1.00)	2.81e-14 (1.39e-11)		0.173 (1.00)	0.501 (1.00)	0.478 (1.00)
15q loss	1 (1.00)	0.412 (1.00)	0.572 (1.00)	0.0497 (1.00)	1 (1.00)	1 (1.00)	0.286 (1.00)	0.594 (1.00)	0.516 (1.00)	0.905 (1.00)	3.08e-14 (1.47e-11)		0.0339 (1.00)	1 (1.00)	0.407 (1.00)
17p loss	1 (1.00)	0.537 (1.00)	0.575 (1.00)	0.606 (1.00)	0.0128 (1.00)	1 (1.00)	0.709 (1.00)	0.00239 (1.00)	0.213 (1.00)	0.861 (1.00)	0.556 (1.00)		0.0835 (1.00)	0.37 (1.00)	0.242 (1.00)
18p loss	1 (1.00)	0.958 (1.00)	0.572 (1.00)	0.886 (1.00)	1 (1.00)	1 (1.00)	0.906 (1.00)	0.594 (1.00)	0.865 (1.00)	0.905 (1.00)	0.0561 (1.00)		0.0502 (1.00)	0.622 (1.00)	0.976 (1.00)
18q loss	1 (1.00)	0.958 (1.00)	0.572 (1.00)	0.886 (1.00)	1 (1.00)	1 (1.00)	0.906 (1.00)	0.594 (1.00)	0.865 (1.00)	0.905 (1.00)	0.0561 (1.00)		0.0502 (1.00)	0.622 (1.00)	0.976 (1.00)
21q loss	1 (1.00)	0.0273 (1.00)	0.606 (1.00)	0.414 (1.00)	1 (1.00)	1 (1.00)	0.516 (1.00)	0.417 (1.00)	0.54 (1.00)	0.694 (1.00)	0.993 (1.00)		0.00626 (1.00)	0.37 (1.00)	0.797 (1.00)
22q loss	1 (1.00)	0.408 (1.00)	1 (1.00)	0.000848 (0.395)	0.229 (1.00)	1 (1.00)	0.648 (1.00)	0.112 (1.00)	0.17 (1.00)	0.784 (1.00)	0.462 (1.00)		0.545 (1.00)	0.21 (1.00)	0.627 (1.00)

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

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

Cluster Labels	1Q GAIN MUTATED	1Q GAIN WILD-TYPE
Number of samples	8	276

Clustering Approach #2: '4p gain mutation analysis'

Table S2. Get Full Table Description of clustering approach #2: '4p gain mutation analysis'

Cluster Labels	4P GAIN MUTATED	4P GAIN WILD-TYPE
Number of samples	4	280

'4p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.02e-14 (t-test), Q value = 1.5e-11

Table S3. Clustering Approach #2: '4p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
4P GAIN MUTATED	4	0.0 (0.0)
4P GAIN WILD-TYPE	221	2.9 (5.3)

Figure S1. Get High-res Image Clustering Approach #2: '4p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: '4q gain mutation analysis'

Table S4. Get Full Table Description of clustering approach #3: '4q gain mutation analysis'

Cluster Labels	4Q GAIN MUTATED	4Q GAIN WILD-TYPE
Number of samples	4	280

'4q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.02e-14 (t-test), Q value = 1.5e-11

Table S5. Clustering Approach #3: '4q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
4Q GAIN MUTATED	4	0.0 (0.0)
4Q GAIN WILD-TYPE	221	2.9 (5.3)

Figure S2. Get High-res Image Clustering Approach #3: '4q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: '5p gain mutation analysis'

Table S6. Get Full Table Description of clustering approach #4: '5p gain mutation analysis'

Cluster Labels	5P GAIN MUTATED	5P GAIN WILD-TYPE
Number of samples	8	276

'5p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.86e-14 (t-test), Q value = 1.4e-11

Table S7. Clustering Approach #4: '5p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
5P GAIN MUTATED	7	0.0 (0.0)
5P GAIN WILD-TYPE	218	2.9 (5.3)

Figure S3. Get High-res Image Clustering Approach #4: '5p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: '5q gain mutation analysis'

Table S8. Get Full Table Description of clustering approach #5: '5q gain mutation analysis'

Cluster Labels	5Q GAIN MUTATED	5Q GAIN WILD-TYPE
Number of samples	8	276

'5q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.86e-14 (t-test), Q value = 1.4e-11

Table S9. Clustering Approach #5: '5q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
5Q GAIN MUTATED	7	0.0 (0.0)
5Q GAIN WILD-TYPE	218	2.9 (5.3)

Figure S4. Get High-res Image Clustering Approach #5: '5q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: '7p gain mutation analysis'

Table S10. Get Full Table Description of clustering approach #6: '7p gain mutation analysis'

Cluster Labels	7P GAIN MUTATED	7P GAIN WILD-TYPE
Number of samples	10	274

'7p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.75e-14 (t-test), Q value = 1.4e-11

Table S11. Clustering Approach #6: '7p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
7P GAIN MUTATED	9	0.0 (0.0)
7P GAIN WILD-TYPE	216	2.9 (5.3)

Figure S5. Get High-res Image Clustering Approach #6: '7p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #7: '7q gain mutation analysis'

Table S12. Get Full Table Description of clustering approach #7: '7q gain mutation analysis'

Cluster Labels	7Q GAIN MUTATED	7Q GAIN WILD-TYPE
Number of samples	12	272

'7q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.75e-14 (t-test), Q value = 1.4e-11

Table S13. Clustering Approach #7: '7q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
7Q GAIN MUTATED	9	0.0 (0.0)
7Q GAIN WILD-TYPE	216	2.9 (5.3)

Figure S6. Get High-res Image Clustering Approach #7: '7q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #8: '11p gain mutation analysis'

Table S14. Get Full Table Description of clustering approach #8: '11p gain mutation analysis'

Cluster Labels	11P GAIN MUTATED	11P GAIN WILD-TYPE
Number of samples	3	281

'11p gain mutation analysis' versus 'HISTOLOGICAL.TYPE'

P value = 3e-04 (Chi-square test), Q value = 0.14

Table S15. Clustering Approach #8: '11p gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	167	69	30
11P GAIN MUTATED	2	1	0	0
11P GAIN WILD-TYPE	16	166	69	30

Figure S7. Get High-res Image Clustering Approach #8: '11p gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

Clustering Approach #9: '12p gain mutation analysis'

Table S16. Get Full Table Description of clustering approach #9: '12p gain mutation analysis'

Cluster Labels	12P GAIN MUTATED	12P GAIN WILD-TYPE
Number of samples	9	275

'12p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.86e-14 (t-test), Q value = 1.4e-11

Table S17. Clustering Approach #9: '12p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
12P GAIN MUTATED	7	0.0 (0.0)
12P GAIN WILD-TYPE	218	2.9 (5.3)

Figure S8. Get High-res Image Clustering Approach #9: '12p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #10: '12q gain mutation analysis'

Table S18. Get Full Table Description of clustering approach #10: '12q gain mutation analysis'

Cluster Labels	12Q GAIN MUTATED	12Q GAIN WILD-TYPE
Number of samples	9	275

'12q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.86e-14 (t-test), Q value = 1.4e-11

Table S19. Clustering Approach #10: '12q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
12Q GAIN MUTATED	7	0.0 (0.0)
12Q GAIN WILD-TYPE	218	2.9 (5.3)

Figure S9. Get High-res Image Clustering Approach #10: '12q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #11: '14q gain mutation analysis'

Table S20. Get Full Table Description of clustering approach #11: '14q gain mutation analysis'

Cluster Labels	14Q GAIN MUTATED	14Q GAIN WILD-TYPE
Number of samples	5	279

'14q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.02e-14 (t-test), Q value = 1.5e-11

Table S21. Clustering Approach #11: '14q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
14Q GAIN MUTATED	4	0.0 (0.0)
14Q GAIN WILD-TYPE	221	2.9 (5.3)

Figure S10. Get High-res Image Clustering Approach #11: '14q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #12: '16p gain mutation analysis'

Table S22. Get Full Table Description of clustering approach #12: '16p gain mutation analysis'

Cluster Labels	16P GAIN MUTATED	16P GAIN WILD-TYPE
Number of samples	7	277

'16p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.97e-14 (t-test), Q value = 1.4e-11

Table S23. Clustering Approach #12: '16p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
16P GAIN MUTATED	5	0.0 (0.0)
16P GAIN WILD-TYPE	220	2.9 (5.3)

Figure S11. Get High-res Image Clustering Approach #12: '16p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #13: '16q gain mutation analysis'

Table S24. Get Full Table Description of clustering approach #13: '16q gain mutation analysis'

Cluster Labels	16Q GAIN MUTATED	16Q GAIN WILD-TYPE
Number of samples	5	279

'16q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.02e-14 (t-test), Q value = 1.5e-11

Table S25. Clustering Approach #13: '16q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
16Q GAIN MUTATED	4	0.0 (0.0)
16Q GAIN WILD-TYPE	221	2.9 (5.3)

Figure S12. Get High-res Image Clustering Approach #13: '16q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #14: '17p gain mutation analysis'

Table S26. Get Full Table Description of clustering approach #14: '17p gain mutation analysis'

Cluster Labels	17P GAIN MUTATED	17P GAIN WILD-TYPE
Number of samples	7	277

'17p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.91e-14 (t-test), Q value = 1.4e-11

Table S27. Clustering Approach #14: '17p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
17P GAIN MUTATED	6	0.0 (0.0)
17P GAIN WILD-TYPE	219	2.9 (5.3)

Figure S13. Get High-res Image Clustering Approach #14: '17p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #15: '17q gain mutation analysis'

Table S28. Get Full Table Description of clustering approach #15: '17q gain mutation analysis'

Cluster Labels	17Q GAIN MUTATED	17Q GAIN WILD-TYPE
Number of samples	8	276

'17q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.86e-14 (t-test), Q value = 1.4e-11

Table S29. Clustering Approach #15: '17q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
17Q GAIN MUTATED	7	0.0 (0.0)
17Q GAIN WILD-TYPE	218	2.9 (5.3)

Figure S14. Get High-res Image Clustering Approach #15: '17q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #16: '19p gain mutation analysis'

Table S30. Get Full Table Description of clustering approach #16: '19p gain mutation analysis'

Cluster Labels	19P GAIN MUTATED	19P GAIN WILD-TYPE
Number of samples	3	281

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

P value = 1.02e-09 (Chi-square test), Q value = 4.8e-07

Table S31. Clustering Approach #16: '19p gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	167	69	30
19P GAIN MUTATED	3	0	0	0
19P GAIN WILD-TYPE	15	167	69	30

Figure S15. Get High-res Image Clustering Approach #16: '19p gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'19p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.08e-14 (t-test), Q value = 1.5e-11

Table S32. Clustering Approach #16: '19p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
19P GAIN MUTATED	3	0.0 (0.0)
19P GAIN WILD-TYPE	222	2.9 (5.3)

Figure S16. Get High-res Image Clustering Approach #16: '19p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #17: '19q gain mutation analysis'

Table S33. Get Full Table Description of clustering approach #17: '19q gain mutation analysis'

Cluster Labels	19Q GAIN MUTATED	19Q GAIN WILD-TYPE
Number of samples	4	280

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

P value = 4.35e-07 (Chi-square test), Q value = 0.00021

Table S34. Clustering Approach #17: '19q gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	OTHER	THYROID PAPILLARY CARCINOMA - CLASSICAL/USUAL	THYROID PAPILLARY CARCINOMA - FOLLICULAR (>= 99% FOLLICULAR PATTERNED)	THYROID PAPILLARY CARCINOMA - TALL CELL (>= 50% TALL CELL FEATURES)
ALL	18	167	69	30
19Q GAIN MUTATED	3	1	0	0
19Q GAIN WILD-TYPE	15	166	69	30

Figure S17. Get High-res Image Clustering Approach #17: '19q gain mutation analysis' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'19q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.02e-14 (t-test), Q value = 1.5e-11

Table S35. Clustering Approach #17: '19q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
19Q GAIN MUTATED	4	0.0 (0.0)
19Q GAIN WILD-TYPE	221	2.9 (5.3)

Figure S18. Get High-res Image Clustering Approach #17: '19q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #18: '20p gain mutation analysis'

Table S36. Get Full Table Description of clustering approach #18: '20p gain mutation analysis'

Cluster Labels	20P GAIN MUTATED	20P GAIN WILD-TYPE
Number of samples	4	280

'20p gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.08e-14 (t-test), Q value = 1.5e-11

Table S37. Clustering Approach #18: '20p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
20P GAIN MUTATED	3	0.0 (0.0)
20P GAIN WILD-TYPE	222	2.9 (5.3)

Figure S19. Get High-res Image Clustering Approach #18: '20p gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #19: '20q gain mutation analysis'

Table S38. Get Full Table Description of clustering approach #19: '20q gain mutation analysis'

Cluster Labels	20Q GAIN MUTATED	20Q GAIN WILD-TYPE
Number of samples	4	280

'20q gain mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.08e-14 (t-test), Q value = 1.5e-11

Table S39. Clustering Approach #19: '20q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
20Q GAIN MUTATED	3	0.0 (0.0)
20Q GAIN WILD-TYPE	222	2.9 (5.3)

Figure S20. Get High-res Image Clustering Approach #19: '20q gain mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #20: '1p loss mutation analysis'

Table S40. Get Full Table Description of clustering approach #20: '1p loss mutation analysis'

Cluster Labels	1P LOSS MUTATED	1P LOSS WILD-TYPE
Number of samples	3	281

Clustering Approach #21: '2p loss mutation analysis'

Table S41. Get Full Table Description of clustering approach #21: '2p loss mutation analysis'

Cluster Labels	2P LOSS MUTATED	2P LOSS WILD-TYPE
Number of samples	7	277

'2p loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.86e-14 (t-test), Q value = 1.4e-11

Table S42. Clustering Approach #21: '2p loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
2P LOSS MUTATED	7	0.0 (0.0)
2P LOSS WILD-TYPE	218	2.9 (5.3)

Figure S21. Get High-res Image Clustering Approach #21: '2p loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #22: '2q loss mutation analysis'

Table S43. Get Full Table Description of clustering approach #22: '2q loss mutation analysis'

Cluster Labels	2Q LOSS MUTATED	2Q LOSS WILD-TYPE
Number of samples	6	278

'2q loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.91e-14 (t-test), Q value = 1.4e-11

Table S44. Clustering Approach #22: '2q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
2Q LOSS MUTATED	6	0.0 (0.0)
2Q LOSS WILD-TYPE	219	2.9 (5.3)

Figure S22. Get High-res Image Clustering Approach #22: '2q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #23: '3q loss mutation analysis'

Table S45. Get Full Table Description of clustering approach #23: '3q loss mutation analysis'

Cluster Labels	3Q LOSS MUTATED	3Q LOSS WILD-TYPE
Number of samples	3	281

'3q loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.08e-14 (t-test), Q value = 1.5e-11

Table S46. Clustering Approach #23: '3q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
3Q LOSS MUTATED	3	0.0 (0.0)
3Q LOSS WILD-TYPE	222	2.9 (5.3)

Figure S23. Get High-res Image Clustering Approach #23: '3q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #24: '9p loss mutation analysis'

Table S47. Get Full Table Description of clustering approach #24: '9p loss mutation analysis'

Cluster Labels	9P LOSS MUTATED	9P LOSS WILD-TYPE
Number of samples	5	279

'9p loss mutation analysis' versus 'NEOPLASM.DISEASESTAGE'

P value = 7.71e-05 (Chi-square test), Q value = 0.036

Table S48. Clustering Approach #24: '9p loss mutation analysis' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	161	32	62	25	3
9P LOSS MUTATED	1	4	0	0	0
9P LOSS WILD-TYPE	160	28	62	25	3

Figure S24. Get High-res Image Clustering Approach #24: '9p loss mutation analysis' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #25: '9q loss mutation analysis'

Table S49. Get Full Table Description of clustering approach #25: '9q loss mutation analysis'

Cluster Labels	9Q LOSS MUTATED	9Q LOSS WILD-TYPE
Number of samples	7	277

'9q loss mutation analysis' versus 'NEOPLASM.DISEASESTAGE'

P value = 1.88e-05 (Chi-square test), Q value = 0.0088

Table S50. Clustering Approach #25: '9q loss mutation analysis' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE II	STAGE III	STAGE IVA	STAGE IVC
ALL	161	32	62	25	3
9Q LOSS MUTATED	1	5	0	1	0
9Q LOSS WILD-TYPE	160	27	62	24	3

Figure S25. Get High-res Image Clustering Approach #25: '9q loss mutation analysis' versus Clinical Feature #13: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #26: '10p loss mutation analysis'

Table S51. Get Full Table Description of clustering approach #26: '10p loss mutation analysis'

Cluster Labels	10P LOSS MUTATED	10P LOSS WILD-TYPE
Number of samples	3	281

Clustering Approach #27: '10q loss mutation analysis'

Table S52. Get Full Table Description of clustering approach #27: '10q loss mutation analysis'

Cluster Labels	10Q LOSS MUTATED	10Q LOSS WILD-TYPE
Number of samples	3	281

Clustering Approach #28: '11p loss mutation analysis'

Table S53. Get Full Table Description of clustering approach #28: '11p loss mutation analysis'

Cluster Labels	11P LOSS MUTATED	11P LOSS WILD-TYPE
Number of samples	4	280

'11p loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.02e-14 (t-test), Q value = 1.5e-11

Table S54. Clustering Approach #28: '11p loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
11P LOSS MUTATED	4	0.0 (0.0)
11P LOSS WILD-TYPE	221	2.9 (5.3)

Figure S26. Get High-res Image Clustering Approach #28: '11p loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #29: '11q loss mutation analysis'

Table S55. Get Full Table Description of clustering approach #29: '11q loss mutation analysis'

Cluster Labels	11Q LOSS MUTATED	11Q LOSS WILD-TYPE
Number of samples	5	279

'11q loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.97e-14 (t-test), Q value = 1.4e-11

Table S56. Clustering Approach #29: '11q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
11Q LOSS MUTATED	5	0.0 (0.0)
11Q LOSS WILD-TYPE	220	2.9 (5.3)

Figure S27. Get High-res Image Clustering Approach #29: '11q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #30: '13q loss mutation analysis'

Table S57. Get Full Table Description of clustering approach #30: '13q loss mutation analysis'

Cluster Labels	13Q LOSS MUTATED	13Q LOSS WILD-TYPE
Number of samples	9	275

'13q loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 2.81e-14 (t-test), Q value = 1.4e-11

Table S58. Clustering Approach #30: '13q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
13Q LOSS MUTATED	8	0.0 (0.0)
13Q LOSS WILD-TYPE	217	2.9 (5.3)

Figure S28. Get High-res Image Clustering Approach #30: '13q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #31: '15q loss mutation analysis'

Table S59. Get Full Table Description of clustering approach #31: '15q loss mutation analysis'

Cluster Labels	15Q LOSS MUTATED	15Q LOSS WILD-TYPE
Number of samples	3	281

'15q loss mutation analysis' versus 'NUMBER.OF.LYMPH.NODES'

P value = 3.08e-14 (t-test), Q value = 1.5e-11

Table S60. Clustering Approach #31: '15q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	225	2.8 (5.2)
15Q LOSS MUTATED	3	0.0 (0.0)
15Q LOSS WILD-TYPE	222	2.9 (5.3)

Figure S29. Get High-res Image Clustering Approach #31: '15q loss mutation analysis' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #32: '17p loss mutation analysis'

Table S61. Get Full Table Description of clustering approach #32: '17p loss mutation analysis'

Cluster Labels	17P LOSS MUTATED	17P LOSS WILD-TYPE
Number of samples	4	280

Clustering Approach #33: '18p loss mutation analysis'

Table S62. Get Full Table Description of clustering approach #33: '18p loss mutation analysis'

Cluster Labels	18P LOSS MUTATED	18P LOSS WILD-TYPE
Number of samples	3	281

Clustering Approach #34: '18q loss mutation analysis'

Table S63. Get Full Table Description of clustering approach #34: '18q loss mutation analysis'

Cluster Labels	18Q LOSS MUTATED	18Q LOSS WILD-TYPE
Number of samples	3	281

Clustering Approach #35: '21q loss mutation analysis'

Table S64. Get Full Table Description of clustering approach #35: '21q loss mutation analysis'

Cluster Labels	21Q LOSS MUTATED	21Q LOSS WILD-TYPE
Number of samples	5	279

Clustering Approach #36: '22q loss mutation analysis'

Table S65. Get Full Table Description of clustering approach #36: '22q loss mutation analysis'

Cluster Labels	22Q LOSS MUTATED	22Q LOSS WILD-TYPE
Number of samples	36	248

Methods & Data

Input

Cluster data file = broad_values_by_arm.mutsig.cluster.txt
Clinical data file = THCA-TP.clin.merged.picked.txt
Number of patients = 284
Number of clustering approaches = 36
Number of selected clinical features = 15
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.

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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