Correlation between aggregated molecular cancer subtypes and selected clinical features

Sarcoma (Primary solid tumor)

17 October 2014 | analyses__2014_10_17

Maintainer Information

Citation Information

Maintained by TCGA GDAC Team (Broad Institute/MD Anderson Cancer Center/Harvard Medical School)

Cite as Broad Institute TCGA Genome Data Analysis Center (2014): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1W094VD

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 8 different clustering approaches and 4 clinical features across 157 patients, 3 significant findings detected with P value < 0.05 and Q value < 0.25.

6 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
7 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'GENDER'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 4 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 7 subtypes that correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'AGE'.
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
8 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 8 different clustering approaches and 4 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	Time to Death	AGE	GENDER	RACE
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.458 (1.00)	0.515 (1.00)	0.0281 (0.675)	0.848 (1.00)
METHLYATION CNMF	0.0237 (0.612)	0.0232 (0.612)	0.00508 (0.157)	0.245 (1.00)
RNAseq CNMF subtypes	0.0105 (0.305)	0.0121 (0.338)	0.0598 (1.00)	0.876 (1.00)
RNAseq cHierClus subtypes	0.194 (1.00)	0.00302 (0.0965)	0.0227 (0.612)	0.558 (1.00)
MIRSEQ CNMF	0.562 (1.00)	0.449 (1.00)	0.113 (1.00)	0.731 (1.00)
MIRSEQ CHIERARCHICAL	0.208 (1.00)	0.00774 (0.232)	0.117 (1.00)	0.793 (1.00)
MIRseq Mature CNMF subtypes	0.264 (1.00)	0.409 (1.00)	0.315 (1.00)	0.537 (1.00)
MIRseq Mature cHierClus subtypes	0.628 (1.00)	0.0484 (1.00)	0.09 (1.00)	0.116 (1.00)

Clustering Approach #1: 'Copy Number Ratio CNMF subtypes'

Table S1. Description of clustering approach #1: 'Copy Number Ratio CNMF subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	18	34	27	33	22	21	1

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

P value = 0.458 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	51	0.1 - 175.0 (18.1)
subtype1	18	8	1.1 - 93.4 (17.2)
subtype2	34	12	0.7 - 116.9 (24.1)
subtype3	26	11	0.5 - 108.1 (14.6)
subtype4	33	7	0.1 - 175.0 (15.0)
subtype5	22	7	0.7 - 81.0 (19.8)
subtype6	21	6	1.1 - 66.3 (22.6)

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

'Copy Number Ratio CNMF subtypes' versus 'AGE'

P value = 0.515 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	155	61.7 (13.3)
subtype1	18	65.2 (14.2)
subtype2	34	59.1 (16.4)
subtype3	27	61.4 (12.3)
subtype4	33	60.7 (10.4)
subtype5	22	64.1 (14.5)
subtype6	21	62.1 (11.1)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.0281 (Fisher's exact test), Q value = 0.68

Table S4. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	87	68
subtype1	8	10
subtype2	16	18
subtype3	10	17
subtype4	22	11
subtype5	17	5
subtype6	14	7

Figure S3. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'Copy Number Ratio CNMF subtypes' versus 'RACE'

P value = 0.848 (Fisher's exact test), Q value = 1

Table S5. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	114
subtype1	0	2	14
subtype2	1	2	22
subtype3	0	0	18
subtype4	1	3	28
subtype5	2	1	17
subtype6	1	2	15

Figure S4. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #4: 'RACE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S6. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	25	25	42	18	15	22	5

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.0237 (logrank test), Q value = 0.61

Table S7. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	151	50	0.1 - 175.0 (18.1)
subtype1	24	8	2.0 - 108.1 (19.0)
subtype2	25	9	0.7 - 70.5 (17.8)
subtype3	42	14	0.1 - 81.0 (25.8)
subtype4	18	9	0.1 - 74.7 (7.2)
subtype5	15	3	0.1 - 116.9 (22.6)
subtype6	22	6	1.1 - 143.4 (16.9)
subtype7	5	1	0.5 - 175.0 (13.6)

Figure S5. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.0232 (Kruskal-Wallis (anova)), Q value = 0.61

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	152	61.5 (13.5)
subtype1	25	63.0 (13.2)
subtype2	25	63.3 (15.2)
subtype3	42	60.8 (12.1)
subtype4	18	54.7 (17.1)
subtype5	15	54.8 (7.8)
subtype6	22	68.6 (12.1)
subtype7	5	63.6 (6.7)

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00508 (Fisher's exact test), Q value = 0.16

Table S9. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	87	65
subtype1	8	17
subtype2	11	14
subtype3	25	17
subtype4	14	4
subtype5	12	3
subtype6	12	10
subtype7	5	0

Figure S7. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RACE'

P value = 0.245 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	111
subtype1	0	1	17
subtype2	2	1	15
subtype3	0	3	38
subtype4	1	0	13
subtype5	1	3	8
subtype6	1	2	16
subtype7	0	0	4

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S11. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3	4
Number of samples	37	56	33	29

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.0105 (logrank test), Q value = 0.3

Table S12. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	51	0.1 - 175.0 (18.4)
subtype1	37	11	0.7 - 108.1 (23.5)
subtype2	56	16	0.1 - 116.9 (24.7)
subtype3	33	11	1.8 - 143.4 (15.7)
subtype4	28	13	0.1 - 175.0 (6.8)

Figure S9. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0121 (Kruskal-Wallis (anova)), Q value = 0.34

Table S13. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	155	61.2 (13.3)
subtype1	37	63.7 (13.1)
subtype2	56	59.0 (11.2)
subtype3	33	66.7 (13.4)
subtype4	29	56.2 (14.8)

Figure S10. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0598 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	88	67
subtype1	15	22
subtype2	38	18
subtype3	17	16
subtype4	18	11

Figure S11. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.876 (Fisher's exact test), Q value = 1

Table S15. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	114
subtype1	2	1	21
subtype2	1	5	47
subtype3	1	2	26
subtype4	1	2	20

Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S16. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7
Number of samples	26	11	39	21	30	19	9

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.194 (logrank test), Q value = 1

Table S17. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	51	0.1 - 175.0 (18.4)
subtype1	26	11	0.7 - 70.5 (21.9)
subtype2	11	1	2.4 - 59.5 (23.1)
subtype3	39	13	0.1 - 81.0 (27.2)
subtype4	20	6	0.1 - 108.1 (11.5)
subtype5	30	12	1.1 - 143.4 (13.8)
subtype6	19	3	0.1 - 116.9 (21.0)
subtype7	9	5	0.1 - 175.0 (10.5)

Figure S13. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.00302 (Kruskal-Wallis (anova)), Q value = 0.097

Table S18. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	155	61.2 (13.3)
subtype1	26	67.0 (11.4)
subtype2	11	62.1 (13.5)
subtype3	39	61.1 (11.5)
subtype4	21	60.3 (13.0)
subtype5	30	65.5 (13.0)
subtype6	19	53.9 (9.1)
subtype7	9	47.0 (19.3)

Figure S14. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.0227 (Fisher's exact test), Q value = 0.61

Table S19. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	88	67
subtype1	11	15
subtype2	5	6
subtype3	23	16
subtype4	9	12
subtype5	16	14
subtype6	16	3
subtype7	8	1

Figure S15. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.558 (Fisher's exact test), Q value = 1

Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	114
subtype1	1	0	15
subtype2	1	1	5
subtype3	0	3	35
subtype4	1	1	16
subtype5	1	3	21
subtype6	1	2	14
subtype7	0	0	8

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S21. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	67	21	67

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.562 (logrank test), Q value = 1

Table S22. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	52	0.1 - 175.0 (18.4)
subtype1	66	25	0.1 - 175.0 (17.8)
subtype2	21	7	0.7 - 93.4 (18.1)
subtype3	67	20	0.1 - 116.9 (21.4)

Figure S17. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.449 (Kruskal-Wallis (anova)), Q value = 1

Table S23. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	155	61.4 (13.5)
subtype1	67	61.5 (14.2)
subtype2	21	64.9 (13.7)
subtype3	67	60.3 (12.6)

Figure S18. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.113 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	87	68
subtype1	32	35
subtype2	11	10
subtype3	44	23

Figure S19. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.731 (Fisher's exact test), Q value = 1

Table S25. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	115
subtype1	1	4	46
subtype2	1	2	14
subtype3	3	4	55

Figure S20. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S26. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3	4
Number of samples	50	18	76	11

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.208 (logrank test), Q value = 1

Table S27. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	52	0.1 - 175.0 (18.4)
subtype1	49	20	0.1 - 143.4 (19.4)
subtype2	18	5	0.1 - 93.4 (18.7)
subtype3	76	21	0.1 - 116.9 (21.2)
subtype4	11	6	1.1 - 175.0 (8.6)

Figure S21. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.00774 (Kruskal-Wallis (anova)), Q value = 0.23

Table S28. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	155	61.4 (13.5)
subtype1	50	63.2 (13.3)
subtype2	18	69.7 (12.8)
subtype3	76	59.8 (12.0)
subtype4	11	51.2 (17.0)

Figure S22. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.117 (Fisher's exact test), Q value = 1

Table S29. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	87	68
subtype1	22	28
subtype2	9	9
subtype3	48	28
subtype4	8	3

Figure S23. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.793 (Fisher's exact test), Q value = 1

Table S30. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	5	10	115
subtype1	1	1	33
subtype2	1	1	13
subtype3	3	7	60
subtype4	0	1	9

Figure S24. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S31. Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	50	44	20

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

P value = 0.264 (logrank test), Q value = 1

Table S32. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	40	0.1 - 175.0 (17.8)
subtype1	49	22	0.1 - 175.0 (11.9)
subtype2	44	14	0.1 - 81.0 (22.0)
subtype3	20	4	0.7 - 55.5 (16.4)

Figure S25. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature CNMF subtypes' versus 'AGE'

P value = 0.409 (Kruskal-Wallis (anova)), Q value = 1

Table S33. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	114	62.2 (13.6)
subtype1	50	61.1 (14.5)
subtype2	44	61.4 (12.0)
subtype3	20	66.5 (14.3)

Figure S26. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.315 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	68	46
subtype1	28	22
subtype2	30	14
subtype3	10	10

Figure S27. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'RACE'

P value = 0.537 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	86
subtype1	1	2	36
subtype2	1	3	36
subtype3	2	1	14

Figure S28. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'RACE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S36. Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6	7	8
Number of samples	10	20	30	14	8	12	8	12

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

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

Table S37. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	40	0.1 - 175.0 (17.8)
subtype1	10	2	0.1 - 36.7 (13.9)
subtype2	19	9	0.5 - 108.1 (20.1)
subtype3	30	11	0.1 - 81.0 (26.2)
subtype4	14	4	0.1 - 26.9 (14.6)
subtype5	8	1	4.5 - 36.4 (21.2)
subtype6	12	5	5.1 - 143.4 (19.8)
subtype7	8	2	0.7 - 55.5 (5.1)
subtype8	12	6	0.1 - 175.0 (7.2)

Figure S29. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'MIRseq Mature cHierClus subtypes' versus 'AGE'

P value = 0.0484 (Kruskal-Wallis (anova)), Q value = 1

Table S38. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	114	62.2 (13.6)
subtype1	10	62.2 (8.9)
subtype2	20	61.9 (10.9)
subtype3	30	62.4 (11.6)
subtype4	14	63.8 (12.9)
subtype5	8	54.6 (11.9)
subtype6	12	70.2 (10.1)
subtype7	8	71.8 (18.5)
subtype8	12	50.9 (18.6)

Figure S30. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.09 (Fisher's exact test), Q value = 1

Table S39. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	68	46
subtype1	4	6
subtype2	7	13
subtype3	22	8
subtype4	9	5
subtype5	5	3
subtype6	7	5
subtype7	4	4
subtype8	10	2

Figure S31. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'RACE'

P value = 0.116 (Fisher's exact test), Q value = 1

Table S40. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	4	6	86
subtype1	0	1	8
subtype2	0	0	11
subtype3	0	1	27
subtype4	1	1	9
subtype5	1	2	5
subtype6	0	0	12
subtype7	1	0	5
subtype8	1	1	9

Figure S32. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'RACE'

Methods & Data

Input

Cluster data file = SARC-TP.mergedcluster.txt
Clinical data file = SARC-TP.merged_data.txt
Number of patients = 157
Number of clustering approaches = 8
Number of selected clinical features = 4
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

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.

Download Results

In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.

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] 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)

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