Correlation between aggregated molecular cancer subtypes and selected clinical features

Overview

Introduction

This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.

Summary

Testing the association between subtypes identified by 12 different clustering approaches and 13 clinical features across 459 patients, 8 significant findings detected with P value < 0.05 and Q value < 0.25.

CNMF clustering analysis on array-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on array-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER'.
CNMF clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
CNMF clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'Time to Death', 'AGE', and 'GENDER'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'AGE', 'GENDER', and 'HISTOLOGICAL.TYPE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. 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 12 different clustering approaches and 13 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 8 significant findings detected.


Clinical Features	Statistical Tests	mRNA CNMF subtypes	mRNA cHierClus subtypes	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RPPA CNMF subtypes	RPPA cHierClus subtypes	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	0.798 (1.00)	0.952 (1.00)	0.605 (1.00)	0.597 (1.00)	0.0527 (1.00)	0.247 (1.00)	1.44e-05 (0.00222)	0.0104 (1.00)	0.473 (1.00)	0.628 (1.00)	0.66 (1.00)	0.806 (1.00)
AGE	ANOVA	0.477 (1.00)	0.557 (1.00)	0.00462 (0.656)	0.385 (1.00)	0.0281 (1.00)	0.0158 (1.00)	0.000839 (0.126)	0.000865 (0.129)	0.0482 (1.00)	0.0412 (1.00)	0.00508 (0.716)	0.00367 (0.532)
NEOPLASM DISEASESTAGE	Chi-square test	0.127 (1.00)	0.127 (1.00)	0.668 (1.00)	0.0912 (1.00)	0.734 (1.00)	0.788 (1.00)	0.0167 (1.00)	0.0201 (1.00)	0.111 (1.00)	0.13 (1.00)	0.497 (1.00)	0.307 (1.00)
PATHOLOGY T STAGE	Chi-square test	0.489 (1.00)	0.479 (1.00)	0.499 (1.00)	0.0125 (1.00)	0.288 (1.00)	0.855 (1.00)	0.00608 (0.851)	0.176 (1.00)	0.0967 (1.00)	0.2 (1.00)	0.033 (1.00)	0.072 (1.00)
PATHOLOGY N STAGE	Chi-square test	0.572 (1.00)	0.651 (1.00)	0.563 (1.00)	0.635 (1.00)	0.287 (1.00)	0.368 (1.00)	0.00619 (0.86)	0.0107 (1.00)	0.18 (1.00)	0.233 (1.00)	0.287 (1.00)	0.622 (1.00)
PATHOLOGY M STAGE	Chi-square test	0.504 (1.00)	1 (1.00)	0.838 (1.00)	0.427 (1.00)	0.751 (1.00)	0.73 (1.00)	0.511 (1.00)	0.426 (1.00)	0.633 (1.00)	0.931 (1.00)	0.684 (1.00)	0.965 (1.00)
GENDER	Fisher's exact test	0.272 (1.00)	0.373 (1.00)	0.00409 (0.585)	0.000799 (0.121)	0.198 (1.00)	0.0344 (1.00)	0.000472 (0.0718)	8.48e-05 (0.013)	0.737 (1.00)	0.00127 (0.187)	0.207 (1.00)	0.0237 (1.00)
KARNOFSKY PERFORMANCE SCORE	ANOVA			0.843 (1.00)	0.661 (1.00)	0.0224 (1.00)	0.21 (1.00)	0.0786 (1.00)	0.666 (1.00)	0.766 (1.00)	0.444 (1.00)	0.832 (1.00)	0.401 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.3 (1.00)	0.274 (1.00)	0.396 (1.00)	0.0504 (1.00)	0.13 (1.00)	0.216 (1.00)	0.0103 (1.00)	0.00123 (0.181)	0.0253 (1.00)	0.0108 (1.00)	0.032 (1.00)	0.0743 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	1 (1.00)	1 (1.00)	0.254 (1.00)	0.22 (1.00)	0.11 (1.00)	0.257 (1.00)	0.528 (1.00)	0.592 (1.00)	0.827 (1.00)	1 (1.00)	0.242 (1.00)	0.869 (1.00)
NUMBERPACKYEARSSMOKED	ANOVA	0.448 (1.00)	0.357 (1.00)	0.0852 (1.00)	0.0115 (1.00)	0.329 (1.00)	0.451 (1.00)	0.136 (1.00)	0.0751 (1.00)	0.134 (1.00)	0.0841 (1.00)	0.0643 (1.00)	0.0767 (1.00)
YEAROFTOBACCOSMOKINGONSET	ANOVA	0.616 (1.00)	0.68 (1.00)	0.377 (1.00)	0.576 (1.00)	0.102 (1.00)	0.00375 (0.54)	0.411 (1.00)	0.0856 (1.00)	0.0267 (1.00)	0.0477 (1.00)	0.00221 (0.323)	0.00755 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.453 (1.00)	0.723 (1.00)	0.96 (1.00)	0.534 (1.00)	0.258 (1.00)	0.558 (1.00)	0.438 (1.00)	0.962 (1.00)	0.568 (1.00)	0.92 (1.00)	0.404 (1.00)	0.716 (1.00)

Clustering Approach #1: 'mRNA CNMF subtypes'

Table S1. Description of clustering approach #1: 'mRNA CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	5	9	12	6

'mRNA CNMF subtypes' versus 'Time to Death'

P value = 0.798 (logrank test), Q value = 1

Table S2. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	31	6	0.5 - 56.8 (23.7)
subtype1	4	0	6.0 - 48.6 (24.5)
subtype2	9	2	4.0 - 56.8 (38.2)
subtype3	12	2	0.5 - 44.9 (16.4)
subtype4	6	2	20.0 - 45.2 (30.9)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA CNMF subtypes' versus 'AGE'

P value = 0.477 (ANOVA), Q value = 1

Table S3. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	30	65.7 (10.8)
subtype1	4	58.5 (15.5)
subtype2	9	65.0 (9.1)
subtype3	12	67.1 (11.1)
subtype4	5	69.4 (9.0)

Figure S2. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'mRNA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.127 (Chi-square test), Q value = 1

Table S4. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IV
ALL	12	11	1	3	3	2
subtype1	3	0	0	1	0	1
subtype2	4	4	0	0	1	0
subtype3	3	7	0	1	0	1
subtype4	2	0	1	1	2	0

Figure S3. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.489 (Chi-square test), Q value = 1

Table S5. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3
ALL	12	19	1
subtype1	3	2	0
subtype2	4	4	1
subtype3	4	8	0
subtype4	1	5	0

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.572 (Chi-square test), Q value = 1

Table S6. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	23	4	4
subtype1	3	1	1
subtype2	8	1	0
subtype3	9	1	1
subtype4	3	1	2

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'mRNA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.504 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1
ALL	30	2
subtype1	4	1
subtype2	9	0
subtype3	11	1
subtype4	6	0

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA CNMF subtypes' versus 'GENDER'

P value = 0.272 (Fisher's exact test), Q value = 1

Table S8. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	14
subtype1	3	2
subtype2	3	6
subtype3	9	3
subtype4	3	3

Figure S7. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.3 (Chi-square test), Q value = 1

Table S9. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG CLEAR CELL ADENOCARCINOMA
ALL	1	30	1
subtype1	0	4	1
subtype2	0	9	0
subtype3	1	11	0
subtype4	0	6	0

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'mRNA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	31
subtype1	0	5
subtype2	0	9
subtype3	1	11
subtype4	0	6

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.448 (ANOVA), Q value = 1

Table S11. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	20	41.1 (15.0)
subtype1	2	29.0 (12.7)
subtype2	9	47.0 (15.5)
subtype3	6	37.0 (13.1)
subtype4	3	40.0 (17.3)

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.616 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1968.5 (11.4)
subtype1	2	1977.0 (18.4)
subtype2	6	1971.2 (14.2)
subtype3	6	1965.8 (8.2)
subtype4	5	1965.2 (9.8)

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'mRNA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.453 (Chi-square test), Q value = 1

Table S13. Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	26	1	2
subtype1	3	0	1
subtype2	7	1	0
subtype3	10	0	1
subtype4	6	0	0

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #2: 'mRNA cHierClus subtypes'

Table S14. Description of clustering approach #2: 'mRNA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	7	13	12

'mRNA cHierClus subtypes' versus 'Time to Death'

P value = 0.952 (logrank test), Q value = 1

Table S15. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	31	6	0.5 - 56.8 (23.7)
subtype1	7	2	20.0 - 48.6 (38.7)
subtype2	13	2	0.5 - 47.0 (18.8)
subtype3	11	2	4.0 - 56.8 (34.1)

Figure S13. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'mRNA cHierClus subtypes' versus 'AGE'

P value = 0.557 (ANOVA), Q value = 1

Table S16. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	30	65.7 (10.8)
subtype1	5	69.4 (9.0)
subtype2	13	66.5 (10.9)
subtype3	12	63.3 (11.6)

Figure S14. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'mRNA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.127 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IV
ALL	12	11	1	3	3	2
subtype1	3	0	1	1	2	0
subtype2	3	8	0	1	0	1
subtype3	6	3	0	1	1	1

Figure S15. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.479 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3
ALL	12	19	1
subtype1	2	5	0
subtype2	4	9	0
subtype3	6	5	1

Figure S16. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.651 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	23	4	4
subtype1	4	1	2
subtype2	10	1	1
subtype3	9	2	1

Figure S17. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 1 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1
ALL	30	2
subtype1	7	0
subtype2	12	1
subtype3	11	1

Figure S18. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'mRNA cHierClus subtypes' versus 'GENDER'

P value = 0.373 (Fisher's exact test), Q value = 1

Table S21. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	18	14
subtype1	4	3
subtype2	9	4
subtype3	5	7

Figure S19. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.274 (Chi-square test), Q value = 1

Table S22. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG CLEAR CELL ADENOCARCINOMA
ALL	1	30	1
subtype1	0	6	1
subtype2	1	12	0
subtype3	0	12	0

Figure S20. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'mRNA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test), Q value = 1

Table S23. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	1	31
subtype1	0	7
subtype2	1	12
subtype3	0	12

Figure S21. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'mRNA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.357 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	20	41.1 (15.0)
subtype1	3	40.0 (17.3)
subtype2	7	35.0 (13.1)
subtype3	10	45.8 (15.4)

Figure S22. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'mRNA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.68 (ANOVA), Q value = 1

Table S25. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	19	1968.5 (11.4)
subtype1	6	1965.0 (8.8)
subtype2	7	1969.9 (13.0)
subtype3	6	1970.5 (12.9)

Figure S23. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'mRNA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.723 (Chi-square test), Q value = 1

Table S26. Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R2	RX
ALL	26	1	2
subtype1	6	0	0
subtype2	10	0	1
subtype3	10	1	1

Figure S24. Get High-res Image Clustering Approach #2: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #3: 'Copy Number Ratio CNMF subtypes'

Table S27. Description of clustering approach #3: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	180	189	86

'Copy Number Ratio CNMF subtypes' versus 'Time to Death'

P value = 0.605 (logrank test), Q value = 1

Table S28. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	419	118	0.0 - 224.0 (12.7)
subtype1	167	47	0.1 - 224.0 (13.9)
subtype2	173	48	0.0 - 120.8 (12.0)
subtype3	79	23	0.1 - 97.7 (13.6)

Figure S25. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.00462 (ANOVA), Q value = 0.66

Table S29. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	424	65.5 (9.7)
subtype1	168	64.5 (9.9)
subtype2	175	67.3 (9.1)
subtype3	81	63.7 (9.9)

Figure S26. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #2: 'AGE'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.668 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	113	131	44	65	65	11	22
subtype1	2	36	53	21	29	25	5	9
subtype2	0	54	56	17	26	23	3	9
subtype3	1	23	22	6	10	17	3	4

Figure S27. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.499 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	141	255	39	18
subtype1	49	107	19	5
subtype2	66	102	12	9
subtype3	26	46	8	4

Figure S28. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.563 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	290	85	70
subtype1	111	36	31
subtype2	123	37	24
subtype3	56	12	15

Figure S29. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.838 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	314	18	1	3	115
subtype1	122	8	1	0	47
subtype2	133	6	0	2	46
subtype3	59	4	0	1	22

Figure S30. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.00409 (Fisher's exact test), Q value = 0.59

Table S34. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	244	211
subtype1	108	72
subtype2	103	86
subtype3	33	53

Figure S31. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.843 (ANOVA), Q value = 1

Table S35. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	78	83.8 (23.0)
subtype1	39	83.8 (22.8)
subtype2	26	85.4 (20.4)
subtype3	13	80.8 (29.3)

Figure S32. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'Copy Number Ratio CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.396 (Chi-square test), Q value = 1

Table S36. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	92	292	4	17	2	3	2	18	1	4	7
subtype1	6	34	120	2	9	1	2	0	5	0	1	0
subtype2	3	42	113	2	7	1	1	2	10	1	1	6
subtype3	4	16	59	0	1	0	0	0	3	0	2	1

Figure S33. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.254 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	21	434
subtype1	5	175
subtype2	10	179
subtype3	6	80

Figure S34. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'Copy Number Ratio CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0852 (ANOVA), Q value = 1

Table S38. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	315	42.2 (27.3)
subtype1	117	45.0 (26.0)
subtype2	129	38.1 (26.8)
subtype3	69	45.1 (29.7)

Figure S35. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'Copy Number Ratio CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.377 (ANOVA), Q value = 1

Table S39. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	250	1964.9 (12.4)
subtype1	96	1964.9 (13.1)
subtype2	99	1963.8 (12.2)
subtype3	55	1966.7 (11.6)

Figure S36. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'Copy Number Ratio CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.96 (Chi-square test), Q value = 1

Table S40. Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	312	10	4	18
subtype1	127	5	1	8
subtype2	129	4	2	6
subtype3	56	1	1	4

Figure S37. Get High-res Image Clustering Approach #3: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #4: 'METHLYATION CNMF'

Table S41. Description of clustering approach #4: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	133	129	134

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.597 (logrank test), Q value = 1

Table S42. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	363	98	0.0 - 224.0 (10.9)
subtype1	121	38	0.0 - 208.6 (12.4)
subtype2	120	30	0.1 - 163.1 (11.2)
subtype3	122	30	0.1 - 224.0 (8.0)

Figure S38. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.385 (ANOVA), Q value = 1

Table S43. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	368	65.2 (10.0)
subtype1	124	64.7 (9.8)
subtype2	122	64.7 (10.3)
subtype3	122	66.2 (9.8)

Figure S39. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0912 (Chi-square test), Q value = 1

Table S44. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	98	115	41	55	57	9	17
subtype1	2	33	34	12	19	21	5	7
subtype2	1	20	47	13	23	17	3	5
subtype3	0	45	34	16	13	19	1	5

Figure S40. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0125 (Chi-square test), Q value = 1

Table S45. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	125	219	34	15
subtype1	39	75	13	5
subtype2	29	78	15	7
subtype3	57	66	6	3

Figure S41. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.635 (Chi-square test), Q value = 1

Table S46. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	255	72	60
subtype1	85	22	25
subtype2	85	27	16
subtype3	85	23	19

Figure S42. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'METHLYATION CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.427 (Chi-square test), Q value = 1

Table S47. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	259	12	1	3	117
subtype1	93	5	1	2	32
subtype2	85	4	0	0	38
subtype3	81	3	0	1	47

Figure S43. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.000799 (Fisher's exact test), Q value = 0.12

Table S48. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	212	184
subtype1	57	76
subtype2	67	62
subtype3	88	46

Figure S44. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #7: 'GENDER'

'METHLYATION CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.661 (ANOVA), Q value = 1

Table S49. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	71	84.2 (21.6)
subtype1	30	81.7 (26.3)
subtype2	24	87.1 (12.0)
subtype3	17	84.7 (23.7)

Figure S45. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0504 (Chi-square test), Q value = 1

Table S50. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	77	251	4	17	1	2	2	17	1	4	7
subtype1	8	32	76	0	4	0	0	0	9	0	1	3
subtype2	2	30	86	1	5	0	1	0	3	0	1	0
subtype3	3	15	89	3	8	1	1	2	5	1	2	4

Figure S46. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.22 (Fisher's exact test), Q value = 1

Table S51. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	18	378
subtype1	6	127
subtype2	3	126
subtype3	9	125

Figure S47. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'METHLYATION CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0115 (ANOVA), Q value = 1

Table S52. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	273	41.2 (27.3)
subtype1	97	47.8 (31.0)
subtype2	92	36.8 (26.0)
subtype3	84	38.5 (22.4)

Figure S48. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'METHLYATION CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.576 (ANOVA), Q value = 1

Table S53. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	226	1965.3 (12.4)
subtype1	82	1964.5 (13.8)
subtype2	79	1966.4 (12.6)
subtype3	65	1964.8 (10.2)

Figure S49. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'METHLYATION CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.534 (Chi-square test), Q value = 1

Table S54. Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	261	8	1	15
subtype1	88	1	0	5
subtype2	90	5	0	5
subtype3	83	2	1	5

Figure S50. Get High-res Image Clustering Approach #4: 'METHLYATION CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #5: 'RPPA CNMF subtypes'

Table S55. Description of clustering approach #5: 'RPPA CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	95	68	74

'RPPA CNMF subtypes' versus 'Time to Death'

P value = 0.0527 (logrank test), Q value = 1

Table S56. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	217	74	0.1 - 224.0 (18.9)
subtype1	82	29	0.1 - 71.5 (17.9)
subtype2	64	17	0.1 - 224.0 (16.9)
subtype3	71	28	0.4 - 97.7 (19.5)

Figure S51. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA CNMF subtypes' versus 'AGE'

P value = 0.0281 (ANOVA), Q value = 1

Table S57. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	216	64.8 (9.8)
subtype1	81	64.3 (10.0)
subtype2	65	67.4 (8.4)
subtype3	70	63.0 (10.6)

Figure S52. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RPPA CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.734 (Chi-square test), Q value = 1

Table S58. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	55	69	20	30	42	7	13
subtype1	0	26	26	7	13	16	3	4
subtype2	1	18	17	7	8	10	3	4
subtype3	0	11	26	6	9	16	1	5

Figure S53. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.288 (Chi-square test), Q value = 1

Table S59. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	63	143	17	13
subtype1	30	51	9	5
subtype2	19	41	2	5
subtype3	14	51	6	3

Figure S54. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.287 (Chi-square test), Q value = 1

Table S60. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	141	46	44
subtype1	51	24	16
subtype2	45	11	11
subtype3	45	11	17

Figure S55. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.751 (Chi-square test), Q value = 1

Table S61. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	175	12	47
subtype1	73	4	16
subtype2	47	4	17
subtype3	55	4	14

Figure S56. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA CNMF subtypes' versus 'GENDER'

P value = 0.198 (Fisher's exact test), Q value = 1

Table S62. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	106
subtype1	59	36
subtype2	36	32
subtype3	36	38

Figure S57. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0224 (ANOVA), Q value = 1

Table S63. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	23	70.4 (34.2)
subtype1	6	40.0 (45.2)
subtype2	8	87.5 (10.4)
subtype3	9	75.6 (28.8)

Figure S58. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.13 (Chi-square test), Q value = 1

Table S64. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	6	52	153	3	9	1	2	2	5	1	3
subtype1	2	19	65	1	2	1	1	1	1	1	1
subtype2	3	18	33	1	7	0	1	0	3	0	2
subtype3	1	15	55	1	0	0	0	1	1	0	0

Figure S59. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RPPA CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.11 (Fisher's exact test), Q value = 1

Table S65. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	16	221
subtype1	4	91
subtype2	3	65
subtype3	9	65

Figure S60. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.329 (ANOVA), Q value = 1

Table S66. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	170	40.8 (26.6)
subtype1	55	43.9 (28.1)
subtype2	60	36.8 (25.8)
subtype3	55	42.2 (25.9)

Figure S61. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.102 (ANOVA), Q value = 1

Table S67. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	126	1964.4 (13.5)
subtype1	36	1965.4 (14.3)
subtype2	47	1961.2 (11.7)
subtype3	43	1967.2 (14.3)

Figure S62. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'RPPA CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.258 (Chi-square test), Q value = 1

Table S68. Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	147	7	3	10
subtype1	57	3	3	6
subtype2	46	3	0	1
subtype3	44	1	0	3

Figure S63. Get High-res Image Clustering Approach #5: 'RPPA CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #6: 'RPPA cHierClus subtypes'

Table S69. Description of clustering approach #6: 'RPPA cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	107	70	60

'RPPA cHierClus subtypes' versus 'Time to Death'

P value = 0.247 (logrank test), Q value = 1

Table S70. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	217	74	0.1 - 224.0 (18.9)
subtype1	94	36	0.1 - 97.7 (19.3)
subtype2	66	20	0.1 - 224.0 (17.3)
subtype3	57	18	0.4 - 88.0 (19.4)

Figure S64. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RPPA cHierClus subtypes' versus 'AGE'

P value = 0.0158 (ANOVA), Q value = 1

Table S71. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	216	64.8 (9.8)
subtype1	95	64.5 (9.8)
subtype2	65	67.4 (8.1)
subtype3	56	62.4 (11.0)

Figure S65. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RPPA cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.788 (Chi-square test), Q value = 1

Table S72. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	1	55	69	20	30	42	7	13
subtype1	0	27	32	9	14	16	3	6
subtype2	0	17	20	5	11	12	3	2
subtype3	1	11	17	6	5	14	1	5

Figure S66. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.855 (Chi-square test), Q value = 1

Table S73. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	63	143	17	13
subtype1	30	66	5	5
subtype2	17	41	7	5
subtype3	16	36	5	3

Figure S67. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.368 (Chi-square test), Q value = 1

Table S74. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	141	46	44
subtype1	60	25	18
subtype2	45	13	11
subtype3	36	8	15

Figure S68. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RPPA cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.73 (Chi-square test), Q value = 1

Table S75. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	MX
ALL	175	12	47
subtype1	80	6	19
subtype2	51	2	17
subtype3	44	4	11

Figure S69. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RPPA cHierClus subtypes' versus 'GENDER'

P value = 0.0344 (Fisher's exact test), Q value = 1

Table S76. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	131	106
subtype1	69	38
subtype2	33	37
subtype3	29	31

Figure S70. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RPPA cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.21 (ANOVA), Q value = 1

Table S77. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	23	70.4 (34.2)
subtype1	9	56.7 (43.9)
subtype2	8	86.2 (9.2)
subtype3	6	70.0 (34.6)

Figure S71. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RPPA cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.216 (Chi-square test), Q value = 1

Table S78. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	6	52	153	3	9	1	2	2	5	1	3
subtype1	2	22	74	0	3	1	1	1	1	1	1
subtype2	2	19	34	3	5	0	1	1	3	0	2
subtype3	2	11	45	0	1	0	0	0	1	0	0

Figure S72. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RPPA cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.257 (Fisher's exact test), Q value = 1

Table S79. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	16	221
subtype1	8	99
subtype2	2	68
subtype3	6	54

Figure S73. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RPPA cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.451 (ANOVA), Q value = 1

Table S80. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	170	40.8 (26.6)
subtype1	63	40.0 (24.2)
subtype2	59	38.5 (28.4)
subtype3	48	44.8 (27.4)

Figure S74. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RPPA cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.00375 (ANOVA), Q value = 0.54

Table S81. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	126	1964.4 (13.5)
subtype1	42	1966.3 (13.4)
subtype2	44	1959.1 (13.1)
subtype3	40	1968.3 (12.6)

Figure S75. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'RPPA cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.558 (Chi-square test), Q value = 1

Table S82. Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	147	7	3	10
subtype1	65	3	3	5
subtype2	47	3	0	2
subtype3	35	1	0	3

Figure S76. Get High-res Image Clustering Approach #6: 'RPPA cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #7: 'RNAseq CNMF subtypes'

Table S83. Description of clustering approach #7: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4	5
Number of samples	132	101	70	101	46

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 1.44e-05 (logrank test), Q value = 0.0022

Table S84. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	414	116	0.0 - 224.0 (12.7)
subtype1	123	42	0.2 - 163.1 (14.7)
subtype2	93	17	0.0 - 120.8 (9.8)
subtype3	65	30	0.5 - 97.7 (10.6)
subtype4	92	21	0.1 - 224.0 (15.5)
subtype5	41	6	0.1 - 63.5 (11.1)

Figure S77. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.000839 (ANOVA), Q value = 0.13

Table S85. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	419	65.5 (9.7)
subtype1	123	63.5 (10.1)
subtype2	95	68.3 (7.8)
subtype3	65	67.4 (9.8)
subtype4	94	63.8 (9.5)
subtype5	42	65.6 (11.0)

Figure S78. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0167 (Chi-square test), Q value = 1

Table S86. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	111	131	41	64	66	11	22
subtype1	2	21	39	16	24	20	4	6
subtype2	0	39	29	9	9	12	1	2
subtype3	0	11	17	2	15	17	3	5
subtype4	1	23	35	10	11	12	3	6
subtype5	0	17	11	4	5	5	0	3

Figure S79. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.00608 (Chi-square test), Q value = 0.85

Table S87. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	139	251	40	18
subtype1	31	83	12	6
subtype2	46	45	6	4
subtype3	14	42	9	4
subtype4	26	63	8	3
subtype5	22	18	5	1

Figure S80. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.00619 (Chi-square test), Q value = 0.86

Table S88. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	285	85	70
subtype1	72	34	24
subtype2	74	14	10
subtype3	36	15	18
subtype4	74	13	13
subtype5	29	9	5

Figure S81. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.511 (Chi-square test), Q value = 1

Table S89. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	310	18	1	3	114
subtype1	91	5	0	0	35
subtype2	70	2	0	0	29
subtype3	52	5	0	0	13
subtype4	69	4	1	2	24
subtype5	28	2	0	1	13

Figure S82. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.000472 (Chi-square test), Q value = 0.072

Table S90. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	244	206
subtype1	76	56
subtype2	67	34
subtype3	34	36
subtype4	38	63
subtype5	29	17

Figure S83. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.0786 (ANOVA), Q value = 1

Table S91. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	77	83.9 (23.1)
subtype1	27	76.3 (29.9)
subtype2	16	94.4 (7.3)
subtype3	6	93.3 (10.3)
subtype4	20	86.0 (15.7)
subtype5	8	76.2 (31.6)

Figure S84. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0103 (Chi-square test), Q value = 1

Table S92. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	92	287	4	17	2	3	2	18	1	4	7
subtype1	2	23	97	0	5	1	2	0	2	0	0	0
subtype2	5	22	58	2	8	0	1	0	5	0	0	0
subtype3	1	15	43	1	0	1	0	1	2	0	2	4
subtype4	4	19	66	0	2	0	0	0	7	0	2	1
subtype5	1	13	23	1	2	0	0	1	2	1	0	2

Figure S85. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.528 (Chi-square test), Q value = 1

Table S93. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	21	429
subtype1	6	126
subtype2	4	97
subtype3	6	64
subtype4	3	98
subtype5	2	44

Figure S86. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.136 (ANOVA), Q value = 1

Table S94. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	309	42.1 (27.4)
subtype1	89	45.7 (28.0)
subtype2	63	34.5 (23.7)
subtype3	51	41.5 (24.0)
subtype4	77	44.6 (31.7)
subtype5	29	41.8 (24.6)

Figure S87. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.411 (ANOVA), Q value = 1

Table S95. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	245	1965.0 (12.5)
subtype1	69	1966.2 (14.0)
subtype2	53	1962.4 (10.3)
subtype3	37	1963.9 (13.4)
subtype4	62	1965.6 (12.3)
subtype5	24	1967.0 (11.0)

Figure S88. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'RNAseq CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.438 (Chi-square test), Q value = 1

Table S96. Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	309	10	4	16
subtype1	89	3	1	5
subtype2	72	3	0	3
subtype3	47	1	1	1
subtype4	71	3	0	4
subtype5	30	0	2	3

Figure S89. Get High-res Image Clustering Approach #7: 'RNAseq CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #8: 'RNAseq cHierClus subtypes'

Table S97. Description of clustering approach #8: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	115	187	148

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.0104 (logrank test), Q value = 1

Table S98. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	414	116	0.0 - 224.0 (12.7)
subtype1	108	34	0.2 - 97.7 (12.7)
subtype2	171	38	0.0 - 224.0 (12.7)
subtype3	135	44	0.1 - 208.6 (12.6)

Figure S90. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.000865 (ANOVA), Q value = 0.13

Table S99. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	419	65.5 (9.7)
subtype1	107	63.6 (10.4)
subtype2	173	67.6 (9.1)
subtype3	139	64.3 (9.5)

Figure S91. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.0201 (Chi-square test), Q value = 1

Table S100. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	111	131	41	64	66	11	22
subtype1	1	20	35	11	21	18	5	4
subtype2	0	61	58	16	24	17	3	7
subtype3	2	30	38	14	19	31	3	11

Figure S92. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.176 (Chi-square test), Q value = 1

Table S101. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	139	251	40	18
subtype1	30	66	13	6
subtype2	71	94	16	6
subtype3	38	91	11	6

Figure S93. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.0107 (Chi-square test), Q value = 1

Table S102. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	285	85	70
subtype1	66	27	21
subtype2	131	33	17
subtype3	88	25	32

Figure S94. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.426 (Chi-square test), Q value = 1

Table S103. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	310	18	1	3	114
subtype1	79	3	0	0	32
subtype2	130	5	1	1	48
subtype3	101	10	0	2	34

Figure S95. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 8.48e-05 (Fisher's exact test), Q value = 0.013

Table S104. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	244	206
subtype1	64	51
subtype2	120	67
subtype3	60	88

Figure S96. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'RNAseq cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.666 (ANOVA), Q value = 1

Table S105. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	77	83.9 (23.1)
subtype1	20	80.0 (22.2)
subtype2	30	86.0 (24.9)
subtype3	27	84.4 (22.2)

Figure S97. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.00123 (Chi-square test), Q value = 0.18

Table S106. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	92	287	4	17	2	3	2	18	1	4	7
subtype1	1	16	90	0	2	1	2	0	2	0	1	0
subtype2	9	45	97	4	12	0	1	2	12	1	0	4
subtype3	3	31	100	0	3	1	0	0	4	0	3	3

Figure S98. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.592 (Fisher's exact test), Q value = 1

Table S107. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	21	429
subtype1	4	111
subtype2	8	179
subtype3	9	139

Figure S99. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'RNAseq cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0751 (ANOVA), Q value = 1

Table S108. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	309	42.1 (27.4)
subtype1	81	42.5 (26.8)
subtype2	116	37.9 (26.0)
subtype3	112	46.1 (28.9)

Figure S100. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'RNAseq cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0856 (ANOVA), Q value = 1

Table S109. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	245	1965.0 (12.5)
subtype1	63	1966.1 (13.5)
subtype2	93	1962.7 (11.8)
subtype3	89	1966.5 (12.2)

Figure S101. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'RNAseq cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.962 (Chi-square test), Q value = 1

Table S110. Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	309	10	4	16
subtype1	80	2	1	3
subtype2	131	4	1	7
subtype3	98	4	2	6

Figure S102. Get High-res Image Clustering Approach #8: 'RNAseq cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #9: 'MIRSEQ CNMF'

Table S111. Description of clustering approach #9: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	159	221	72

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.473 (logrank test), Q value = 1

Table S112. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	416	116	0.0 - 224.0 (12.7)
subtype1	147	37	0.0 - 163.1 (12.9)
subtype2	204	55	0.1 - 208.6 (12.6)
subtype3	65	24	0.1 - 224.0 (12.2)

Figure S103. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0482 (ANOVA), Q value = 1

Table S113. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	422	65.5 (9.7)
subtype1	150	66.9 (9.4)
subtype2	210	64.4 (10.1)
subtype3	62	65.8 (9.1)

Figure S104. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.111 (Chi-square test), Q value = 1

Table S114. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	112	131	44	63	66	11	21
subtype1	0	54	36	14	23	20	2	9
subtype2	2	45	74	21	30	31	8	10
subtype3	1	13	21	9	10	15	1	2

Figure S105. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T.STAGE'

P value = 0.0967 (Chi-square test), Q value = 1

Table S115. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	140	251	40	18
subtype1	62	75	15	7
subtype2	55	135	19	10
subtype3	23	41	6	1

Figure S106. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.N.STAGE'

P value = 0.18 (Chi-square test), Q value = 1

Table S116. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	287	84	70
subtype1	98	36	19
subtype2	147	36	35
subtype3	42	12	16

Figure S107. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CNMF' versus 'PATHOLOGY.M.STAGE'

P value = 0.633 (Chi-square test), Q value = 1

Table S117. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	311	16	1	3	117
subtype1	110	7	0	1	39
subtype2	156	8	1	2	53
subtype3	45	1	0	0	25

Figure S108. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.737 (Fisher's exact test), Q value = 1

Table S118. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	242	210
subtype1	89	70
subtype2	115	106
subtype3	38	34

Figure S109. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CNMF' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.766 (ANOVA), Q value = 1

Table S119. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	76	83.4 (23.1)
subtype1	24	81.7 (26.6)
subtype2	47	83.6 (22.4)
subtype3	5	90.0 (10.0)

Figure S110. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0253 (Chi-square test), Q value = 1

Table S120. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	91	291	4	17	2	2	2	18	1	4	7
subtype1	5	35	90	3	11	0	1	2	7	1	1	3
subtype2	8	50	143	0	4	2	0	0	9	0	3	2
subtype3	0	6	58	1	2	0	1	0	2	0	0	2

Figure S111. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.827 (Fisher's exact test), Q value = 1

Table S121. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	20	432
subtype1	7	152
subtype2	9	212
subtype3	4	68

Figure S112. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CNMF' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.134 (ANOVA), Q value = 1

Table S122. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	312	42.3 (27.3)
subtype1	104	37.9 (24.8)
subtype2	162	44.5 (28.4)
subtype3	46	44.4 (28.2)

Figure S113. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CNMF' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0267 (ANOVA), Q value = 1

Table S123. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	248	1964.7 (12.4)
subtype1	86	1962.3 (12.3)
subtype2	130	1966.7 (12.5)
subtype3	32	1963.0 (11.4)

Figure S114. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'MIRSEQ CNMF' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.568 (Chi-square test), Q value = 1

Table S124. Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	308	10	4	18
subtype1	106	1	2	7
subtype2	159	6	2	8
subtype3	43	3	0	3

Figure S115. Get High-res Image Clustering Approach #9: 'MIRSEQ CNMF' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #10: 'MIRSEQ CHIERARCHICAL'

Table S125. Description of clustering approach #10: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	49	239	164

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.628 (logrank test), Q value = 1

Table S126. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	416	116	0.0 - 224.0 (12.7)
subtype1	46	13	0.1 - 224.0 (12.2)
subtype2	222	62	0.1 - 208.6 (13.1)
subtype3	148	41	0.0 - 163.1 (12.2)

Figure S116. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.0412 (ANOVA), Q value = 1

Table S127. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	422	65.5 (9.7)
subtype1	45	64.8 (8.4)
subtype2	226	64.5 (10.0)
subtype3	151	67.1 (9.5)

Figure S117. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.13 (Chi-square test), Q value = 1

Table S128. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	112	131	44	63	66	11	21
subtype1	0	12	12	9	6	9	0	1
subtype2	3	50	76	22	33	37	9	9
subtype3	0	50	43	13	24	20	2	11

Figure S118. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T.STAGE'

P value = 0.2 (Chi-square test), Q value = 1

Table S129. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	140	251	40	18
subtype1	15	31	3	0
subtype2	64	140	23	10
subtype3	61	80	14	8

Figure S119. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N.STAGE'

P value = 0.233 (Chi-square test), Q value = 1

Table S130. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	287	84	70
subtype1	29	12	8
subtype2	156	37	43
subtype3	102	35	19

Figure S120. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.M.STAGE'

P value = 0.931 (Chi-square test), Q value = 1

Table S131. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	311	16	1	3	117
subtype1	35	1	0	0	12
subtype2	167	7	1	2	61
subtype3	109	8	0	1	44

Figure S121. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.00127 (Fisher's exact test), Q value = 0.19

Table S132. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	242	210
subtype1	37	12
subtype2	114	125
subtype3	91	73

Figure S122. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.444 (ANOVA), Q value = 1

Table S133. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	76	83.4 (23.1)
subtype1	10	91.0 (13.7)
subtype2	46	83.5 (22.1)
subtype3	20	79.5 (28.6)

Figure S123. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.0108 (Chi-square test), Q value = 1

Table S134. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	91	291	4	17	2	2	2	18	1	4	7
subtype1	0	6	37	2	0	0	1	0	3	0	0	0
subtype2	8	47	164	0	5	1	0	0	9	0	3	2
subtype3	5	38	90	2	12	1	1	2	6	1	1	5

Figure S124. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 1 (Fisher's exact test), Q value = 1

Table S135. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	20	432
subtype1	2	47
subtype2	11	228
subtype3	7	157

Figure S125. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRSEQ CHIERARCHICAL' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0841 (ANOVA), Q value = 1

Table S136. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	312	42.3 (27.3)
subtype1	29	40.3 (18.0)
subtype2	174	45.3 (29.3)
subtype3	109	38.0 (25.8)

Figure S126. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRSEQ CHIERARCHICAL' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.0477 (ANOVA), Q value = 1

Table S137. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	248	1964.7 (12.4)
subtype1	24	1962.7 (9.0)
subtype2	132	1966.5 (12.9)
subtype3	92	1962.6 (12.1)

Figure S127. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'MIRSEQ CHIERARCHICAL' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.92 (Chi-square test), Q value = 1

Table S138. Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	308	10	4	18
subtype1	36	2	0	3
subtype2	164	5	2	10
subtype3	108	3	2	5

Figure S128. Get High-res Image Clustering Approach #10: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #11: 'MIRseq Mature CNMF subtypes'

Table S139. Description of clustering approach #11: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	139	177	73

'MIRseq Mature CNMF subtypes' versus 'Time to Death'

P value = 0.66 (logrank test), Q value = 1

Table S140. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	355	94	0.0 - 224.0 (10.8)
subtype1	130	35	0.0 - 163.1 (12.2)
subtype2	159	36	0.1 - 208.6 (9.4)
subtype3	66	23	0.1 - 224.0 (11.6)

Figure S129. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.00508 (ANOVA), Q value = 0.72

Table S141. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	361	65.5 (9.8)
subtype1	132	67.3 (9.6)
subtype2	165	63.7 (9.9)
subtype3	64	66.1 (9.2)

Figure S130. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.497 (Chi-square test), Q value = 1

Table S142. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	97	111	41	53	57	9	17
subtype1	0	43	36	12	16	20	4	7
subtype2	2	39	58	21	23	22	4	8
subtype3	1	15	17	8	14	15	1	2

Figure S131. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.033 (Chi-square test), Q value = 1

Table S143. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	124	213	34	15
subtype1	53	62	14	10
subtype2	49	107	15	4
subtype3	22	44	5	1

Figure S132. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.287 (Chi-square test), Q value = 1

Table S144. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	250	70	60
subtype1	89	26	19
subtype2	121	28	25
subtype3	40	16	16

Figure S133. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature CNMF subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.684 (Chi-square test), Q value = 1

Table S145. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	252	12	1	3	117
subtype1	90	5	0	1	41
subtype2	119	6	1	2	48
subtype3	43	1	0	0	28

Figure S134. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.207 (Fisher's exact test), Q value = 1

Table S146. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	208	181
subtype1	81	58
subtype2	86	91
subtype3	41	32

Figure S135. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.832 (ANOVA), Q value = 1

Table S147. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	71	84.2 (21.6)
subtype1	17	82.9 (23.1)
subtype2	44	83.9 (22.8)
subtype3	10	88.0 (13.2)

Figure S136. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.032 (Chi-square test), Q value = 1

Table S148. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	76	245	4	17	1	2	2	17	1	4	7
subtype1	6	38	71	2	9	0	1	1	8	0	0	3
subtype2	6	31	118	0	7	1	0	0	7	1	4	2
subtype3	1	7	56	2	1	0	1	1	2	0	0	2

Figure S137. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.242 (Fisher's exact test), Q value = 1

Table S149. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	18	371
subtype1	6	133
subtype2	6	171
subtype3	6	67

Figure S138. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature CNMF subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0643 (ANOVA), Q value = 1

Table S150. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	268	41.4 (27.4)
subtype1	94	36.1 (25.5)
subtype2	129	43.9 (28.4)
subtype3	45	45.3 (27.4)

Figure S139. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature CNMF subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.00221 (ANOVA), Q value = 0.32

Table S151. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	220	1964.8 (12.2)
subtype1	79	1962.4 (12.1)
subtype2	110	1967.6 (12.1)
subtype3	31	1960.9 (10.7)

Figure S140. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.404 (Chi-square test), Q value = 1

Table S152. Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	256	8	1	15
subtype1	84	4	1	4
subtype2	127	2	0	6
subtype3	45	2	0	5

Figure S141. Get High-res Image Clustering Approach #11: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Clustering Approach #12: 'MIRseq Mature cHierClus subtypes'

Table S153. Description of clustering approach #12: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	45	180	164

'MIRseq Mature cHierClus subtypes' versus 'Time to Death'

P value = 0.806 (logrank test), Q value = 1

Table S154. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	355	94	0.0 - 224.0 (10.8)
subtype1	42	13	0.1 - 224.0 (10.6)
subtype2	163	38	0.1 - 208.6 (8.8)
subtype3	150	43	0.0 - 163.1 (13.2)

Figure S142. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.00367 (ANOVA), Q value = 0.53

Table S155. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	361	65.5 (9.8)
subtype1	41	65.5 (8.4)
subtype2	165	63.7 (9.9)
subtype3	155	67.3 (9.7)

Figure S143. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.307 (Chi-square test), Q value = 1

Table S156. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE I	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE IIIA	STAGE IIIB	STAGE IV
ALL	3	97	111	41	53	57	9	17
subtype1	0	10	11	8	5	10	0	1
subtype2	3	39	57	21	23	26	4	7
subtype3	0	48	43	12	25	21	5	9

Figure S144. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'NEOPLASM.DISEASESTAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.T.STAGE'

P value = 0.072 (Chi-square test), Q value = 1

Table S157. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

nPatients	T1	T2	T3	T4
ALL	124	213	34	15
subtype1	15	28	2	0
subtype2	52	107	15	4
subtype3	57	78	17	11

Figure S145. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'PATHOLOGY.T.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.N.STAGE'

P value = 0.622 (Chi-square test), Q value = 1

Table S158. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

nPatients	N0	N1	N2+N3
ALL	250	70	60
subtype1	27	8	10
subtype2	119	30	29
subtype3	104	32	21

Figure S146. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.N.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'PATHOLOGY.M.STAGE'

P value = 0.965 (Chi-square test), Q value = 1

Table S159. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

nPatients	M0	M1	M1A	M1B	MX
ALL	252	12	1	3	117
subtype1	29	1	0	0	14
subtype2	119	5	1	2	52
subtype3	104	6	0	1	51

Figure S147. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.M.STAGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.0237 (Fisher's exact test), Q value = 1

Table S160. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

nPatients	FEMALE	MALE
ALL	208	181
subtype1	30	15
subtype2	84	96
subtype3	94	70

Figure S148. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'KARNOFSKY.PERFORMANCE.SCORE'

P value = 0.401 (ANOVA), Q value = 1

Table S161. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

	nPatients	Mean (Std.Dev)
ALL	71	84.2 (21.6)
subtype1	8	93.8 (9.2)
subtype2	42	82.4 (22.8)
subtype3	21	84.3 (22.3)

Figure S149. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'KARNOFSKY.PERFORMANCE.SCORE'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0743 (Chi-square test), Q value = 1

Table S162. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

nPatients	LUNG ACINAR ADENOCARCINOMA	LUNG ADENOCARCINOMA MIXED SUBTYPE	LUNG ADENOCARCINOMA- NOT OTHERWISE SPECIFIED (NOS)	LUNG BRONCHIOLOALVEOLAR CARCINOMA MUCINOUS	LUNG BRONCHIOLOALVEOLAR CARCINOMA NONMUCINOUS	LUNG CLEAR CELL ADENOCARCINOMA	LUNG MICROPAPILLARY ADENOCARCINOMA	LUNG MUCINOUS ADENOCARCINOMA	LUNG PAPILLARY ADENOCARCINOMA	LUNG SIGNET RING ADENOCARCINOMA	LUNG SOLID PATTERN PREDOMINANT ADENOCARCINOMA	MUCINOUS (COLLOID) CARCINOMA
ALL	13	76	245	4	17	1	2	2	17	1	4	7
subtype1	0	6	33	2	1	0	1	0	2	0	0	0
subtype2	7	32	124	0	6	1	0	0	5	0	3	2
subtype3	6	38	88	2	10	0	1	2	10	1	1	5

Figure S150. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #9: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.869 (Fisher's exact test), Q value = 1

Table S163. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	18	371
subtype1	1	44
subtype2	9	171
subtype3	8	156

Figure S151. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'RADIATIONS.RADIATION.REGIMENINDICATION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBERPACKYEARSSMOKED'

P value = 0.0767 (ANOVA), Q value = 1

Table S164. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

	nPatients	Mean (Std.Dev)
ALL	268	41.4 (27.4)
subtype1	31	43.8 (24.9)
subtype2	129	44.7 (29.7)
subtype3	108	36.8 (24.7)

Figure S152. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #11: 'NUMBERPACKYEARSSMOKED'

'MIRseq Mature cHierClus subtypes' versus 'YEAROFTOBACCOSMOKINGONSET'

P value = 0.00755 (ANOVA), Q value = 1

Table S165. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

	nPatients	Mean (Std.Dev)
ALL	220	1964.8 (12.2)
subtype1	26	1963.4 (9.5)
subtype2	107	1967.4 (12.5)
subtype3	87	1962.1 (11.9)

Figure S153. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #12: 'YEAROFTOBACCOSMOKINGONSET'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.716 (Chi-square test), Q value = 1

Table S166. Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	256	8	1	15
subtype1	33	1	0	1
subtype2	120	2	0	7
subtype3	103	5	1	7

Figure S154. Get High-res Image Clustering Approach #12: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #13: 'COMPLETENESS.OF.RESECTION'

Methods & Data

Input

Cluster data file = LUAD-TP.mergedcluster.txt
Clinical data file = LUAD-TP.merged_data.txt
Number of patients = 459
Number of clustering approaches = 12
Number of selected clinical features = 13
Exclude small clusters that include fewer than K patients, K = 3

Clustering approaches

CNMF clustering

consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)

Consensus hierarchical clustering

Resampling-based clustering method (Monti et al. 2003)

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

ANOVA analysis

For continuous numerical clinical features, one-way analysis of variance (Howell 2002) was applied to compare the clinical values between tumor subtypes using 'anova' 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

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

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] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)

[2] Monti et al., Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data, Machine Learning 52(1):91-118 (2003)

[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)

[4] Howell, D, Statistical Methods for Psychology. (5th ed.), Duxbury Press:324-5 (2002)

[5] Greenwood and Nikulin, A guide to chi-squared testing, Wiley, New York. ISBN 047155779X (1996)

[6] 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)

[7] 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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