Correlation between aggregated molecular cancer subtypes and selected clinical features
Uterine Corpus Endometrioid Carcinoma (Primary solid tumor)
04 October 2018 | None
Maintainer Information
Maintained by Broad Institute GDAC (Broad Institute of MIT & Harvard)
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 3 different clustering approaches and 17 clinical features across 100 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

- Consensus hierarchical clustering analysis on array-based mRNA expression data identified 5 subtypes that do not correlate to any clinical features.

- CNMF clustering analysis on methylation data identified 4 subtypes that correlate to 'HISTOLOGICAL_TYPE'.

- 7 subtypes identified in current cancer cohort by 'METHYLATION CHIERARCHICAL'. These subtypes correlate to 'HISTOLOGICAL_TYPE' and 'RADIATION_THERAPY'.

Results
Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 3 different clustering approaches and 17 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 3 significant findings detected.

Clinical Features | Statistical Tests | mRNA cHierClus subtypes | Methylation CNMF subtypes | METHYLATION CHIERARCHICAL
DAYS TO DEATH OR LAST FUP | logrank test | 0.412 (0.83) | 0.818 (0.909) | 0.585 (0.847)
HISTOLOGICAL TYPE | Fisher's exact test | 0.713 (0.909) | 0.0012 (0.0306) | 0.00011 (0.00561)
FIGO GRADE | Fisher's exact test | 0.315 (0.81) | 0.027 (0.344) | 0.472 (0.83)
KARNOFSKY PERFORMANCE SCORE | Kruskal-Wallis (anova) | 0.526 (0.838) | 0.0369 (0.377) | 0.133 (0.519)
MSI | Fisher's exact test | 0.574 (0.847) | 1 (1.00) | 0.479 (0.83)
PATHOLOGY T STAGE | Fisher's exact test | 0.108 (0.459) | 0.148 (0.519) | 0.584 (0.847)
PATHOLOGY N STAGE | Fisher's exact test | 0.386 (0.83) | 0.818 (0.909) | 0.45 (0.83)
PATHOLOGIC STAGE | Fisher's exact test | 0.36 (0.83) | 0.488 (0.83) | 0.484 (0.83)
YEARS TO BIRTH | Kruskal-Wallis (anova) | 0.517 (0.838) | 0.82 (0.909) | 0.47 (0.83)
ETHNICITY | Fisher's exact test | 0.28 (0.792) | 0.0768 (0.426) | 0.844 (0.909)
RACE | Fisher's exact test | 0.849 (0.909) | 0.364 (0.83) | 0.266 (0.792)
RADIATION THERAPY | Fisher's exact test | 0.919 (0.956) | 0.105 (0.459) | 0.0145 (0.246)
DIABETES | Fisher's exact test | 0.83 (0.909) | 0.0655 (0.422) | 0.67 (0.909)
BMI | Fisher's exact test | 0.0661 (0.422) | 0.0515 (0.422) | 0.598 (0.847)
NUMBER PACK YEARS SMOKED | Kruskal-Wallis (anova) | 0.318 (0.81) | 0.153 (0.519) | 0.0835 (0.426)
SMOKER | Fisher's exact test | 0.855 (0.909) | 0.26 (0.792) | 0.734 (0.909)
COUNTRY OF ORIGIN | Fisher's exact test | 0.728 (0.909) | 0.679 (0.909) | 0.947 (0.966)

Clustering Approach #1: 'mRNA cHierClus subtypes'

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

Cluster Labels | 1 | 2 | 3 | 4 | 5
--- | --- | --- | --- | --- | ---
Number of samples | 28 | 20 | 11 | 20 | 21

'mRNA cHierClus subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.412 (logrank test), Q value = 0.83

Table S2. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 99 | 3 | 0.0 - 130.4 (11.6)
subtype1 | 28 | 2 | 5.4 - 24.3 (11.2)
subtype2 | 19 | 1 | 0.0 - 23.3 (10.8)
subtype3 | 11 | 0 | 1.9 - 23.5 (14.0)
subtype4 | 20 | 0 | 6.0 - 130.4 (12.6)
subtype5 | 20 | 0 | 6.6 - 24.8 (11.8)

Figure S1. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'mRNA cHierClus subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.713 (Fisher's exact test), Q value = 0.91

Table S3. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

nPatients | CLEAR CELL CARCINOMA | ENDOMETRIOID CARCINOMA | MIXED CELL ADENOCARCINOMA | SEROUS CARCINOMA
--- | --- | --- | --- | ---
ALL | 1 | 77 | 1 | 21
subtype1 | 0 | 22 | 0 | 6
subtype2 | 0 | 15 | 1 | 4
subtype3 | 0 | 10 | 0 | 1
subtype4 | 1 | 13 | 0 | 6
subtype5 | 0 | 17 | 0 | 4

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

'mRNA cHierClus subtypes' versus 'FIGO_GRADE'

P value = 0.315 (Fisher's exact test), Q value = 0.81

Table S4. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #3: 'FIGO_GRADE'

nPatients | FIGO GRADE 1 | FIGO GRADE 2 | FIGO GRADE 3
--- | --- | --- | ---
ALL | 32 | 34 | 7
subtype1 | 8 | 11 | 2
subtype2 | 5 | 8 | 2
subtype3 | 8 | 1 | 0
subtype4 | 4 | 7 | 1
subtype5 | 7 | 7 | 2

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

'mRNA cHierClus subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.526 (Kruskal-Wallis (anova)), Q value = 0.84

Table S5. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 58 | 91.9 (8.5)
subtype1 | 16 | 94.4 (5.1)
subtype2 | 13 | 92.3 (9.3)
subtype3 | 7 | 90.0 (14.1)
subtype4 | 13 | 91.5 (8.0)
subtype5 | 9 | 88.9 (7.8)

Figure S4. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

'mRNA cHierClus subtypes' versus 'MSI'

P value = 0.574 (Fisher's exact test), Q value = 0.85

Table S6. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'MSI'

nPatients | MSI-H | MSI-L | MSS
--- | --- | --- | ---
ALL | 4 | 5 | 23
subtype1 | 1 | 2 | 9
subtype2 | 2 | 1 | 2
subtype3 | 1 | 0 | 4
subtype4 | 0 | 0 | 3
subtype5 | 0 | 2 | 5

Figure S5. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #5: 'MSI'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.108 (Kruskal-Wallis (anova)), Q value = 0.46

Table S7. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_T_STAGE'

| T1 | T2 | T3
--- | --- | --- | ---
ALL | 78 | 11 | 10
subtype1 | 23 | 1 | 4
subtype2 | 15 | 4 | 1
subtype3 | 9 | 1 | 0
subtype4 | 16 | 0 | 4
subtype5 | 15 | 5 | 1

Figure S6. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY_T_STAGE'

'mRNA cHierClus subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.386 (Kruskal-Wallis (anova)), Q value = 0.83

Table S8. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGY_N_STAGE'

| N0 | N1 | N2
--- | --- | --- | ---
ALL | 46 | 6 | 3
subtype1 | 13 | 1 | 0
subtype2 | 12 | 1 | 0
subtype3 | 6 | 0 | 0
subtype4 | 9 | 2 | 1
subtype5 | 6 | 2 | 2

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

'mRNA cHierClus subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.36 (Fisher's exact test), Q value = 0.83

Table S9. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGIC_STAGE'

nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE III | STAGE IV | STAGE IVB
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 72 | 1 | 1 | 8 | 15 | 2 | 1
subtype1 | 21 | 0 | 0 | 1 | 5 | 1 | 0
subtype2 | 15 | 0 | 0 | 3 | 2 | 0 | 0
subtype3 | 9 | 0 | 0 | 1 | 0 | 0 | 1
subtype4 | 14 | 1 | 1 | 0 | 3 | 1 | 0
subtype5 | 13 | 0 | 0 | 3 | 5 | 0 | 0

Figure S8. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #8: 'PATHOLOGIC_STAGE'

'mRNA cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.517 (Kruskal-Wallis (anova)), Q value = 0.84

Table S10. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 100 | 63.6 (10.1)
subtype1 | 28 | 64.6 (12.0)
subtype2 | 20 | 65.0 (9.6)
subtype3 | 11 | 58.7 (10.2)
subtype4 | 20 | 64.3 (10.7)
subtype5 | 21 | 62.6 (6.2)

Figure S9. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #9: 'YEARS_TO_BIRTH'

'mRNA cHierClus subtypes' versus 'ETHNICITY'

P value = 0.28 (Fisher's exact test), Q value = 0.79

Table S11. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 4 | 41
subtype1 | 1 | 14
subtype2 | 1 | 7
subtype3 | 0 | 4
subtype4 | 2 | 5
subtype5 | 0 | 11

Figure S10. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #10: 'ETHNICITY'

'mRNA cHierClus subtypes' versus 'RACE'

P value = 0.849 (Fisher's exact test), Q value = 0.91

Table S12. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 1 | 3 | 58
subtype1 | 0 | 1 | 19
subtype2 | 0 | 1 | 11
subtype3 | 0 | 0 | 7
subtype4 | 0 | 0 | 11
subtype5 | 1 | 1 | 10

Figure S11. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #11: 'RACE'

'mRNA cHierClus subtypes' versus 'RADIATION_THERAPY'

P value = 0.919 (Fisher's exact test), Q value = 0.96

Table S13. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 43 | 54
subtype1 | 11 | 17
subtype2 | 9 | 9
subtype3 | 5 | 6
subtype4 | 8 | 12
subtype5 | 10 | 10

Figure S12. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'mRNA cHierClus subtypes' versus 'DIABETES'

P value = 0.83 (Fisher's exact test), Q value = 0.91

Table S14. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'DIABETES'

nPatients | NO | YES
--- | --- | ---
ALL | 70 | 28
subtype1 | 22 | 6
subtype2 | 12 | 7
subtype3 | 8 | 3
subtype4 | 14 | 6
subtype5 | 14 | 6

Figure S13. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #13: 'DIABETES'

'mRNA cHierClus subtypes' versus 'BMI'

P value = 0.0661 (Fisher's exact test), Q value = 0.42

Table S15. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'BMI'

nPatients | NORMAL | OBESE | OVERWEIGHT | SEVERELY OBESE | UNDERWEIGHT
--- | --- | --- | --- | --- | ---
ALL | 8 | 47 | 21 | 21 | 3
subtype1 | 5 | 11 | 7 | 3 | 2
subtype2 | 1 | 12 | 1 | 6 | 0
subtype3 | 0 | 5 | 2 | 3 | 1
subtype4 | 0 | 8 | 9 | 3 | 0
subtype5 | 2 | 11 | 2 | 6 | 0

Figure S14. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #14: 'BMI'

'mRNA cHierClus subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.318 (Kruskal-Wallis (anova)), Q value = 0.81

Table S16. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'NUMBER_PACK_YEARS_SMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 14.5 (11.9)
subtype1 | 5 | 21.6 (18.7)
subtype2 | 4 | 13.3 (8.6)
subtype3 | 3 | 4.5 (4.4)
subtype4 | 4 | 12.6 (7.5)
subtype5 | 2 | 17.8 (2.5)

Figure S15. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #15: 'NUMBER_PACK_YEARS_SMOKED'

'mRNA cHierClus subtypes' versus 'SMOKER'

P value = 0.855 (Fisher's exact test), Q value = 0.91

Table S17. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #16: 'SMOKER'

nPatients | NON-SMOKER | SMOKER
--- | --- | ---
ALL | 73 | 22
subtype1 | 21 | 6
subtype2 | 12 | 4
subtype3 | 7 | 4
subtype4 | 16 | 4
subtype5 | 17 | 4

Figure S16. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #16: 'SMOKER'

'mRNA cHierClus subtypes' versus 'COUNTRY_OF_ORIGIN'

P value = 0.728 (Fisher's exact test), Q value = 0.91

Table S18. Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #17: 'COUNTRY_OF_ORIGIN'

nPatients | MEXICO | POLAND | UKRAINE | UNITED STATES
--- | --- | --- | --- | ---
ALL | 2 | 5 | 32 | 36
subtype1 | 0 | 0 | 8 | 12
subtype2 | 1 | 1 | 6 | 4
subtype3 | 0 | 1 | 3 | 4
subtype4 | 1 | 2 | 7 | 6
subtype5 | 0 | 1 | 8 | 10

Figure S17. Get High-res Image Clustering Approach #1: 'mRNA cHierClus subtypes' versus Clinical Feature #17: 'COUNTRY_OF_ORIGIN'

Clustering Approach #2: 'Methylation CNMF subtypes'

Table S19. Description of clustering approach #2: 'Methylation CNMF subtypes'

Cluster Labels | 1 | 2 | 3 | 4
--- | --- | --- | --- | ---
Number of samples | 35 | 33 | 16 | 12

'Methylation CNMF subtypes' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.818 (logrank test), Q value = 0.91

Table S20. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 95 | 3 | 1.9 - 130.4 (11.6)
subtype1 | 34 | 1 | 6.1 - 130.4 (11.6)
subtype2 | 32 | 1 | 2.3 - 24.3 (10.9)
subtype3 | 16 | 1 | 1.9 - 24.8 (11.4)
subtype4 | 12 | 0 | 9.6 - 22.9 (14.5)

Figure S18. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'Methylation CNMF subtypes' versus 'HISTOLOGICAL_TYPE'

P value = 0.0012 (Fisher's exact test), Q value = 0.031

Table S21. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

nPatients | CLEAR CELL CARCINOMA | ENDOMETRIOID CARCINOMA | MIXED CELL ADENOCARCINOMA | SEROUS CARCINOMA
--- | --- | --- | --- | ---
ALL | 1 | 77 | 1 | 17
subtype1 | 0 | 33 | 0 | 2
subtype2 | 1 | 24 | 1 | 7
subtype3 | 0 | 8 | 0 | 8
subtype4 | 0 | 12 | 0 | 0

Figure S19. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

'Methylation CNMF subtypes' versus 'FIGO_GRADE'

P value = 0.027 (Fisher's exact test), Q value = 0.34

Table S22. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #3: 'FIGO_GRADE'

nPatients | FIGO GRADE 1 | FIGO GRADE 2 | FIGO GRADE 3
--- | --- | --- | ---
ALL | 32 | 34 | 7
subtype1 | 15 | 14 | 3
subtype2 | 10 | 12 | 0
subtype3 | 0 | 4 | 3
subtype4 | 7 | 4 | 1

Figure S20. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #3: 'FIGO_GRADE'

'Methylation CNMF subtypes' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.0369 (Kruskal-Wallis (anova)), Q value = 0.38

Table S23. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 55 | 92.7 (7.6)
subtype1 | 17 | 92.4 (7.5)
subtype2 | 25 | 94.4 (8.7)
subtype3 | 9 | 88.9 (3.3)
subtype4 | 4 | 92.5 (5.0)

Figure S21. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

'Methylation CNMF subtypes' versus 'MSI'

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

Table S24. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #5: 'MSI'

nPatients | MSI-H | MSI-L | MSS
--- | --- | --- | ---
ALL | 4 | 5 | 23
subtype1 | 1 | 2 | 8
subtype2 | 1 | 1 | 7
subtype3 | 1 | 1 | 5
subtype4 | 1 | 1 | 3

Figure S22. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #5: 'MSI'

'Methylation CNMF subtypes' versus 'PATHOLOGY_T_STAGE'

P value = 0.148 (Kruskal-Wallis (anova)), Q value = 0.52

Table S25. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_T_STAGE'

| T1 | T2 | T3
--- | --- | --- | ---
ALL | 75 | 11 | 9
subtype1 | 27 | 5 | 3
subtype2 | 28 | 3 | 1
subtype3 | 9 | 3 | 4
subtype4 | 11 | 0 | 1

Figure S23. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY_T_STAGE'

'Methylation CNMF subtypes' versus 'PATHOLOGY_N_STAGE'

P value = 0.818 (Kruskal-Wallis (anova)), Q value = 0.91

Table S26. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_N_STAGE'

| N0 | N1 | N2
--- | --- | --- | ---
ALL | 44 | 5 | 2
subtype1 | 15 | 1 | 1
subtype2 | 16 | 3 | 0
subtype3 | 9 | 1 | 1
subtype4 | 4 | 0 | 0

Figure S24. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGY_N_STAGE'

'Methylation CNMF subtypes' versus 'PATHOLOGIC_STAGE'

P value = 0.488 (Fisher's exact test), Q value = 0.83

Table S27. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGIC_STAGE'

nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE III | STAGE IV | STAGE IVB
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 70 | 1 | 1 | 8 | 14 | 1 | 1
subtype1 | 25 | 1 | 0 | 3 | 5 | 1 | 0
subtype2 | 26 | 0 | 0 | 2 | 4 | 0 | 1
subtype3 | 8 | 0 | 1 | 3 | 4 | 0 | 0
subtype4 | 11 | 0 | 0 | 0 | 1 | 0 | 0

Figure S25. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #8: 'PATHOLOGIC_STAGE'

'Methylation CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.82 (Kruskal-Wallis (anova)), Q value = 0.91

Table S28. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #9: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 96 | 63.5 (10.2)
subtype1 | 35 | 62.6 (8.3)
subtype2 | 33 | 63.5 (10.6)
subtype3 | 16 | 67.1 (13.4)
subtype4 | 12 | 60.9 (8.6)

Figure S26. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #9: 'YEARS_TO_BIRTH'

'Methylation CNMF subtypes' versus 'ETHNICITY'

P value = 0.0768 (Fisher's exact test), Q value = 0.43

Table S29. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 4 | 40
subtype1 | 1 | 19
subtype2 | 0 | 8
subtype3 | 3 | 6
subtype4 | 0 | 7

Figure S27. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #10: 'ETHNICITY'

'Methylation CNMF subtypes' versus 'RACE'

P value = 0.364 (Fisher's exact test), Q value = 0.83

Table S30. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #11: 'RACE'

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 1 | 3 | 57
subtype1 | 0 | 1 | 23
subtype2 | 0 | 1 | 19
subtype3 | 0 | 1 | 8
subtype4 | 1 | 0 | 7

Figure S28. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #11: 'RACE'

'Methylation CNMF subtypes' versus 'RADIATION_THERAPY'

P value = 0.105 (Fisher's exact test), Q value = 0.46

Table S31. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 43 | 52
subtype1 | 18 | 16
subtype2 | 13 | 20
subtype3 | 4 | 12
subtype4 | 8 | 4

Figure S29. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #12: 'RADIATION_THERAPY'

'Methylation CNMF subtypes' versus 'DIABETES'

P value = 0.0655 (Fisher's exact test), Q value = 0.42

Table S32. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #13: 'DIABETES'

nPatients | NO | YES
--- | --- | ---
ALL | 68 | 27
subtype1 | 23 | 11
subtype2 | 24 | 9
subtype3 | 15 | 1
subtype4 | 6 | 6

Figure S30. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #13: 'DIABETES'

'Methylation CNMF subtypes' versus 'BMI'

P value = 0.0515 (Fisher's exact test), Q value = 0.42

Table S33. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #14: 'BMI'

nPatients | NORMAL | OBESE | OVERWEIGHT | SEVERELY OBESE | UNDERWEIGHT
--- | --- | --- | --- | --- | ---
ALL | 7 | 45 | 20 | 21 | 3
subtype1 | 3 | 12 | 7 | 10 | 3
subtype2 | 1 | 21 | 4 | 7 | 0
subtype3 | 3 | 5 | 7 | 1 | 0
subtype4 | 0 | 7 | 2 | 3 | 0

Figure S31. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #14: 'BMI'

'Methylation CNMF subtypes' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.153 (Kruskal-Wallis (anova)), Q value = 0.52

Table S34. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #15: 'NUMBER_PACK_YEARS_SMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 14.5 (11.9)
subtype1 | 7 | 18.8 (13.2)
subtype2 | 9 | 12.0 (11.0)
subtype4 | 2 | 10.5 (13.4)

Figure S32. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #15: 'NUMBER_PACK_YEARS_SMOKED'

'Methylation CNMF subtypes' versus 'SMOKER'

P value = 0.26 (Fisher's exact test), Q value = 0.79

Table S35. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #16: 'SMOKER'

nPatients | NON-SMOKER | SMOKER
--- | --- | ---
ALL | 70 | 21
subtype1 | 25 | 9
subtype2 | 20 | 9
subtype3 | 15 | 1
subtype4 | 10 | 2

Figure S33. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #16: 'SMOKER'

'Methylation CNMF subtypes' versus 'COUNTRY_OF_ORIGIN'

P value = 0.679 (Fisher's exact test), Q value = 0.91

Table S36. Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #17: 'COUNTRY_OF_ORIGIN'

nPatients | MEXICO | POLAND | UKRAINE | UNITED STATES
--- | --- | --- | --- | ---
ALL | 2 | 3 | 31 | 35
subtype1 | 1 | 2 | 9 | 16
subtype2 | 0 | 1 | 11 | 6
subtype3 | 1 | 0 | 7 | 8
subtype4 | 0 | 0 | 4 | 5

Figure S34. Get High-res Image Clustering Approach #2: 'Methylation CNMF subtypes' versus Clinical Feature #17: 'COUNTRY_OF_ORIGIN'

Clustering Approach #3: 'METHYLATION CHIERARCHICAL'

Table S37. Description of clustering approach #3: 'METHYLATION CHIERARCHICAL'

Cluster Labels | 1 | 2 | 3 | 4 | 5 | 6 | 7
--- | --- | --- | --- | --- | --- | --- | ---
Number of samples | 34 | 11 | 9 | 7 | 6 | 20 | 9

'METHYLATION CHIERARCHICAL' versus 'DAYS_TO_DEATH_OR_LAST_FUP'

P value = 0.585 (logrank test), Q value = 0.85

Table S38. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

| nPatients | nDeath | Duration Range (Median), Month
--- | --- | --- | ---
ALL | 95 | 3 | 1.9 - 130.4 (11.6)
subtype1 | 33 | 0 | 6.1 - 130.4 (11.1)
subtype2 | 11 | 1 | 5.4 - 24.3 (13.1)
subtype3 | 9 | 1 | 6.7 - 22.4 (17.5)
subtype4 | 7 | 0 | 1.9 - 24.8 (22.6)
subtype5 | 6 | 0 | 10.4 - 22.9 (17.1)
subtype6 | 19 | 1 | 2.3 - 23.5 (9.6)
subtype7 | 9 | 0 | 8.2 - 23.3 (14.0)

Figure S35. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #1: 'DAYS_TO_DEATH_OR_LAST_FUP'

'METHYLATION CHIERARCHICAL' versus 'HISTOLOGICAL_TYPE'

P value = 0.00011 (Fisher's exact test), Q value = 0.0056

Table S39. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

nPatients | CLEAR CELL CARCINOMA | ENDOMETRIOID CARCINOMA | MIXED CELL ADENOCARCINOMA | SEROUS CARCINOMA
--- | --- | --- | --- | ---
ALL | 1 | 77 | 1 | 17
subtype1 | 0 | 32 | 0 | 2
subtype2 | 0 | 3 | 0 | 8
subtype3 | 0 | 9 | 0 | 0
subtype4 | 0 | 4 | 0 | 3
subtype5 | 0 | 6 | 0 | 0
subtype6 | 1 | 15 | 1 | 3
subtype7 | 0 | 8 | 0 | 1

Figure S36. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #2: 'HISTOLOGICAL_TYPE'

'METHYLATION CHIERARCHICAL' versus 'FIGO_GRADE'

P value = 0.472 (Fisher's exact test), Q value = 0.83

Table S40. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #3: 'FIGO_GRADE'

nPatients | FIGO GRADE 1 | FIGO GRADE 2 | FIGO GRADE 3
--- | --- | --- | ---
ALL | 32 | 34 | 7
subtype1 | 12 | 15 | 4
subtype2 | 0 | 2 | 0
subtype3 | 5 | 3 | 1
subtype4 | 0 | 2 | 1
subtype5 | 4 | 1 | 1
subtype6 | 7 | 7 | 0
subtype7 | 4 | 4 | 0

Figure S37. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #3: 'FIGO_GRADE'

'METHYLATION CHIERARCHICAL' versus 'KARNOFSKY_PERFORMANCE_SCORE'

P value = 0.133 (Kruskal-Wallis (anova)), Q value = 0.52

Table S41. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 55 | 92.7 (7.6)
subtype1 | 19 | 90.5 (7.1)
subtype2 | 6 | 93.3 (5.2)
subtype3 | 3 | 96.7 (5.8)
subtype4 | 4 | 90.0 (0.0)
subtype5 | 2 | 95.0 (7.1)
subtype6 | 16 | 94.4 (10.3)
subtype7 | 5 | 94.0 (5.5)

Figure S38. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #4: 'KARNOFSKY_PERFORMANCE_SCORE'

'METHYLATION CHIERARCHICAL' versus 'MSI'

P value = 0.479 (Fisher's exact test), Q value = 0.83

Table S42. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #5: 'MSI'

nPatients | MSI-H | MSI-L | MSS
--- | --- | --- | ---
ALL | 4 | 5 | 23
subtype1 | 2 | 3 | 6
subtype2 | 0 | 0 | 5
subtype3 | 0 | 0 | 5
subtype4 | 0 | 0 | 1
subtype5 | 1 | 1 | 0
subtype6 | 1 | 0 | 4
subtype7 | 0 | 1 | 2

Figure S39. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #5: 'MSI'

'METHYLATION CHIERARCHICAL' versus 'PATHOLOGY_T_STAGE'

P value = 0.584 (Kruskal-Wallis (anova)), Q value = 0.85

Table S43. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_T_STAGE'

| T1 | T2 | T3
--- | --- | --- | ---
ALL | 75 | 11 | 9
subtype1 | 25 | 6 | 3
subtype2 | 7 | 1 | 3
subtype3 | 8 | 0 | 1
subtype4 | 5 | 1 | 1
subtype5 | 6 | 0 | 0
subtype6 | 17 | 1 | 1
subtype7 | 7 | 2 | 0

Figure S40. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY_T_STAGE'

'METHYLATION CHIERARCHICAL' versus 'PATHOLOGY_N_STAGE'

P value = 0.45 (Kruskal-Wallis (anova)), Q value = 0.83

Table S44. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_N_STAGE'

| N0 | N1 | N2
--- | --- | --- | ---
ALL | 44 | 5 | 2
subtype1 | 17 | 1 | 1
subtype2 | 4 | 1 | 1
subtype3 | 2 | 0 | 0
subtype4 | 5 | 0 | 0
subtype5 | 2 | 0 | 0
subtype6 | 9 | 3 | 0
subtype7 | 5 | 0 | 0

Figure S41. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGY_N_STAGE'

'METHYLATION CHIERARCHICAL' versus 'PATHOLOGIC_STAGE'

P value = 0.484 (Fisher's exact test), Q value = 0.83

Table S45. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGIC_STAGE'

nPatients | STAGE I | STAGE IA | STAGE IB | STAGE II | STAGE III | STAGE IV | STAGE IVB
--- | --- | --- | --- | --- | --- | --- | ---
ALL | 70 | 1 | 1 | 8 | 14 | 1 | 1
subtype1 | 24 | 1 | 0 | 4 | 5 | 0 | 0
subtype2 | 7 | 0 | 0 | 1 | 3 | 0 | 0
subtype3 | 7 | 0 | 0 | 0 | 1 | 1 | 0
subtype4 | 4 | 0 | 1 | 1 | 1 | 0 | 0
subtype5 | 6 | 0 | 0 | 0 | 0 | 0 | 0
subtype6 | 15 | 0 | 0 | 0 | 4 | 0 | 1
subtype7 | 7 | 0 | 0 | 2 | 0 | 0 | 0

Figure S42. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #8: 'PATHOLOGIC_STAGE'

'METHYLATION CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.47 (Kruskal-Wallis (anova)), Q value = 0.83

Table S46. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #9: 'YEARS_TO_BIRTH'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 96 | 63.5 (10.2)
subtype1 | 34 | 61.5 (8.2)
subtype2 | 11 | 69.7 (12.0)
subtype3 | 9 | 59.6 (13.4)
subtype4 | 7 | 68.6 (11.8)
subtype5 | 6 | 64.7 (2.3)
subtype6 | 20 | 62.8 (12.2)
subtype7 | 9 | 63.9 (4.5)

Figure S43. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #9: 'YEARS_TO_BIRTH'

'METHYLATION CHIERARCHICAL' versus 'ETHNICITY'

P value = 0.844 (Fisher's exact test), Q value = 0.91

Table S47. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

nPatients | HISPANIC OR LATINO | NOT HISPANIC OR LATINO
--- | --- | ---
ALL | 4 | 40
subtype1 | 3 | 14
subtype2 | 1 | 4
subtype3 | 0 | 7
subtype4 | 0 | 3
subtype5 | 0 | 4
subtype6 | 0 | 4
subtype7 | 0 | 4

Figure S44. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #10: 'ETHNICITY'

'METHYLATION CHIERARCHICAL' versus 'RACE'

P value = 0.266 (Fisher's exact test), Q value = 0.79

Table S48. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

nPatients | ASIAN | BLACK OR AFRICAN AMERICAN | WHITE
--- | --- | --- | ---
ALL | 1 | 3 | 57
subtype1 | 0 | 1 | 20
subtype2 | 0 | 0 | 7
subtype3 | 0 | 0 | 7
subtype4 | 0 | 1 | 2
subtype5 | 1 | 0 | 4
subtype6 | 0 | 1 | 12
subtype7 | 0 | 0 | 5

Figure S45. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #11: 'RACE'

'METHYLATION CHIERARCHICAL' versus 'RADIATION_THERAPY'

P value = 0.0145 (Fisher's exact test), Q value = 0.25

Table S49. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #12: 'RADIATION_THERAPY'

nPatients | NO | YES
--- | --- | ---
ALL | 43 | 52
subtype1 | 12 | 21
subtype2 | 5 | 6
subtype3 | 9 | 0
subtype4 | 2 | 5
subtype5 | 4 | 2
subtype6 | 8 | 12
subtype7 | 3 | 6

Figure S46. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #12: 'RADIATION_THERAPY'

'METHYLATION CHIERARCHICAL' versus 'DIABETES'

P value = 0.67 (Fisher's exact test), Q value = 0.91

Table S50. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #13: 'DIABETES'

nPatients | NO | YES
--- | --- | ---
ALL | 68 | 27
subtype1 | 25 | 8
subtype2 | 9 | 2
subtype3 | 5 | 4
subtype4 | 6 | 1
subtype5 | 3 | 3
subtype6 | 14 | 6
subtype7 | 6 | 3

Figure S47. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #13: 'DIABETES'

'METHYLATION CHIERARCHICAL' versus 'BMI'

P value = 0.598 (Fisher's exact test), Q value = 0.85

Table S51. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #14: 'BMI'

nPatients | NORMAL | OBESE | OVERWEIGHT | SEVERELY OBESE | UNDERWEIGHT
--- | --- | --- | --- | --- | ---
ALL | 7 | 45 | 20 | 21 | 3
subtype1 | 3 | 12 | 8 | 8 | 3
subtype2 | 1 | 5 | 4 | 1 | 0
subtype3 | 0 | 4 | 1 | 4 | 0
subtype4 | 2 | 2 | 3 | 0 | 0
subtype5 | 0 | 4 | 1 | 1 | 0
subtype6 | 1 | 12 | 2 | 5 | 0
subtype7 | 0 | 6 | 1 | 2 | 0

Figure S48. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #14: 'BMI'

'METHYLATION CHIERARCHICAL' versus 'NUMBER_PACK_YEARS_SMOKED'

P value = 0.0835 (Kruskal-Wallis (anova)), Q value = 0.43

Table S52. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #15: 'NUMBER_PACK_YEARS_SMOKED'

| nPatients | Mean (Std.Dev)
--- | --- | ---
ALL | 18 | 14.5 (11.9)
subtype1 | 5 | 20.4 (14.9)
subtype2 | 2 | 4.7 (2.3)
subtype3 | 2 | 13.8 (8.8)
subtype5 | 1 | 1.0 (NA)
subtype6 | 5 | 9.0 (5.5)
subtype7 | 3 | 25.3 (11.4)

Figure S49. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #15: 'NUMBER_PACK_YEARS_SMOKED'

'METHYLATION CHIERARCHICAL' versus 'SMOKER'

P value = 0.734 (Fisher's exact test), Q value = 0.91

Table S53. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #16: 'SMOKER'

nPatients | NON-SMOKER | SMOKER
--- | --- | ---
ALL | 70 | 21
subtype1 | 26 | 7
subtype2 | 8 | 3
subtype3 | 7 | 2
subtype4 | 7 | 0
subtype5 | 5 | 1
subtype6 | 12 | 5
subtype7 | 5 | 3

Figure S50. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #16: 'SMOKER'

'METHYLATION CHIERARCHICAL' versus 'COUNTRY_OF_ORIGIN'

P value = 0.947 (Fisher's exact test), Q value = 0.97

Table S54. Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #17: 'COUNTRY_OF_ORIGIN'

nPatients | MEXICO | POLAND | UKRAINE | UNITED STATES
--- | --- | --- | --- | ---
ALL | 2 | 3 | 31 | 35
subtype1 | 2 | 2 | 11 | 15
subtype2 | 0 | 0 | 4 | 5
subtype3 | 0 | 0 | 2 | 4
subtype4 | 0 | 0 | 4 | 3
subtype5 | 0 | 0 | 1 | 3
subtype6 | 0 | 1 | 6 | 2
subtype7 | 0 | 0 | 3 | 3

Figure S51. Get High-res Image Clustering Approach #3: 'METHYLATION CHIERARCHICAL' versus Clinical Feature #17: 'COUNTRY_OF_ORIGIN'

Methods & Data
Input

- Cluster data file = /cromwell_root/fc-8b2df640-93e1-40a2-b735-5b7a14ef6398/10d02b45-07ea-40cc-ac82-013e5f89ebb2/aggregate_clusters_workflow/b0f8dcb2-0993-4424-ad42-552c0bd1a2d5/call-aggregate_clusters/CPTAC3-UCEC-TP.mergedcluster.txt

- Clinical data file = /cromwell_root/fc-8b2df640-93e1-40a2-b735-5b7a14ef6398/f48b4003-eaf7-47c4-8ca4-0ddbd4729902/normalize_clinical_cptac/0ea0fb0d-64a4-4204-8245-a79f52e6cd0a/call-normalize_clinical_cptac_task_1/CPTAC3-UCEC-TP.clin.merged.picked.txt

- Number of patients = 100

- Number of clustering approaches = 3

- Number of selected clinical features = 17

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

Clustering approaches
Consensus hierarchical clustering

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

CNMF clustering

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

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

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] 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)
[2] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] Bland and Altman, Statistics notes: The logrank test, BMJ 328(7447):1073 (2004)
[4] 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)
[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)
Copyright © 2018 Broad Institute GDAC, in the NCI Genome Data Analysis Network. All rights reserved.
Made with Nozzle