Correlation between aggregated molecular cancer subtypes and selected clinical features

Sarcoma (Primary solid tumor)

15 July 2014 | analyses__2014_07_15

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/C1VT1QWT

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 114 patients, 4 significant findings detected with P value < 0.05 and Q value < 0.25.

3 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 correlate to 'Time to Death'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 5 subtypes that correlate to 'GENDER'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes correlate to 'Time to Death'.
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 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, 4 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	0.808 (1.00)	0.0694 (1.00)	0.0385 (1.00)	0.784 (1.00)
METHLYATION CNMF	0.102 (1.00)	0.327 (1.00)	0.00476 (0.143)	0.0695 (1.00)
RNAseq CNMF subtypes	0.00756 (0.219)	0.35 (1.00)	0.0282 (0.789)	0.646 (1.00)
RNAseq cHierClus subtypes	0.601 (1.00)	0.0937 (1.00)	0.00262 (0.0812)	0.567 (1.00)
MIRSEQ CNMF	0.768 (1.00)	0.139 (1.00)	0.339 (1.00)	0.456 (1.00)
MIRSEQ CHIERARCHICAL	3.53e-11 (1.13e-09)	0.126 (1.00)	0.262 (1.00)	0.484 (1.00)
MIRseq Mature CNMF subtypes	0.435 (1.00)	0.162 (1.00)	0.355 (1.00)	0.548 (1.00)
MIRseq Mature cHierClus subtypes	0.628 (1.00)	0.0996 (1.00)	0.56 (1.00)	0.86 (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
Number of samples	40	36	38

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

P value = 0.808 (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	114	36	0.1 - 143.4 (17.9)
subtype1	40	13	0.1 - 108.1 (17.9)
subtype2	36	11	0.1 - 81.0 (12.7)
subtype3	38	12	0.1 - 143.4 (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 = 0.0694 (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	114	61.3 (14.1)
subtype1	40	63.6 (13.1)
subtype2	36	62.2 (16.2)
subtype3	38	57.9 (12.6)

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 = 0.0385 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	62	52
subtype1	16	24
subtype2	25	11
subtype3	21	17

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 = 0.784 (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	4	8	77
subtype1	2	1	21
subtype2	1	3	27
subtype3	1	4	29

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	19	21	33	13	13	11	4

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.102 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	114	36	0.1 - 143.4 (17.9)
subtype1	19	6	1.7 - 108.1 (15.1)
subtype2	21	6	0.7 - 70.5 (16.3)
subtype3	33	11	0.1 - 81.0 (25.1)
subtype4	13	6	0.1 - 74.7 (5.7)
subtype5	13	3	0.1 - 116.9 (22.6)
subtype6	11	3	1.1 - 143.4 (19.3)
subtype7	4	1	0.5 - 41.3 (8.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.327 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	114	61.3 (14.1)
subtype1	19	65.8 (13.9)
subtype2	21	63.0 (16.2)
subtype3	33	60.4 (12.3)
subtype4	13	61.9 (12.5)
subtype5	13	55.7 (8.1)
subtype6	11	58.1 (22.4)
subtype7	4	62.5 (7.1)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.00476 (Fisher's exact test), Q value = 0.14

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

nPatients	FEMALE	MALE
ALL	62	52
subtype1	5	14
subtype2	9	12
subtype3	18	15
subtype4	11	2
subtype5	10	3
subtype6	5	6
subtype7	4	0

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

'METHLYATION CNMF' versus 'RACE'

P value = 0.0695 (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	4	8	77
subtype1	0	0	12
subtype2	2	1	11
subtype3	0	2	30
subtype4	1	0	8
subtype5	1	3	6
subtype6	0	2	7
subtype7	0	0	3

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	20	31	15	45

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.00756 (logrank test), Q value = 0.22

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	111	35	0.1 - 143.4 (18.1)
subtype1	20	7	1.8 - 143.4 (16.1)
subtype2	31	8	0.7 - 108.1 (17.8)
subtype3	15	7	0.1 - 74.7 (4.6)
subtype4	45	13	0.1 - 116.9 (24.0)

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 = 0.35 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	111	60.7 (13.8)
subtype1	20	59.3 (19.4)
subtype2	31	63.9 (13.9)
subtype3	15	62.0 (12.3)
subtype4	45	58.7 (11.1)

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

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0282 (Fisher's exact test), Q value = 0.79

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

nPatients	FEMALE	MALE
ALL	60	51
subtype1	9	11
subtype2	11	20
subtype3	11	4
subtype4	29	16

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

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.646 (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	4	8	74
subtype1	0	2	14
subtype2	2	1	14
subtype3	1	1	9
subtype4	1	4	37

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
Number of samples	30	22	16	30	13

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.601 (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	111	35	0.1 - 143.4 (18.1)
subtype1	30	10	0.7 - 70.5 (17.4)
subtype2	22	8	0.1 - 108.1 (9.6)
subtype3	16	4	1.8 - 143.4 (18.7)
subtype4	30	10	0.1 - 81.0 (25.8)
subtype5	13	3	0.1 - 116.9 (18.1)

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 = 0.0937 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	111	60.7 (13.8)
subtype1	30	66.5 (12.1)
subtype2	22	57.5 (13.8)
subtype3	16	57.6 (20.4)
subtype4	30	60.7 (11.6)
subtype5	13	56.5 (9.7)

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

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.00262 (Fisher's exact test), Q value = 0.081

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

nPatients	FEMALE	MALE
ALL	60	51
subtype1	13	17
subtype2	12	10
subtype3	6	10
subtype4	16	14
subtype5	13	0

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

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.567 (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	4	8	74
subtype1	2	1	13
subtype2	1	2	15
subtype3	0	2	10
subtype4	0	2	27
subtype5	1	1	9

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	45	54	14

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.768 (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	113	36	0.1 - 143.4 (17.8)
subtype1	45	16	0.1 - 143.4 (17.8)
subtype2	54	16	0.1 - 116.9 (18.1)
subtype3	14	4	0.7 - 70.5 (16.2)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.139 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	113	61.3 (14.1)
subtype1	45	60.8 (15.8)
subtype2	54	59.8 (12.5)
subtype3	14	68.4 (13.5)

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

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.339 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	61	52
subtype1	21	24
subtype2	33	21
subtype3	7	7

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

'MIRSEQ CNMF' versus 'RACE'

P value = 0.456 (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	4	8	77
subtype1	1	3	25
subtype2	2	3	44
subtype3	1	2	8

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	5
Number of samples	35	9	16	6	47

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 3.53e-11 (logrank test), Q value = 1.1e-09

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	113	36	0.1 - 143.4 (17.8)
subtype1	35	12	0.1 - 143.4 (23.1)
subtype2	9	3	0.1 - 15.1 (5.3)
subtype3	16	4	0.7 - 70.5 (17.4)
subtype4	6	4	1.1 - 8.6 (4.8)
subtype5	47	13	0.1 - 116.9 (24.0)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.126 (Kruskal-Wallis (anova)), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	113	61.3 (14.1)
subtype1	35	61.3 (16.7)
subtype2	9	62.6 (16.3)
subtype3	16	69.1 (12.7)
subtype4	6	57.5 (13.4)
subtype5	47	58.9 (11.5)

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

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.262 (Fisher's exact test), Q value = 1

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

nPatients	FEMALE	MALE
ALL	61	52
subtype1	15	20
subtype2	4	5
subtype3	8	8
subtype4	5	1
subtype5	29	18

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

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.484 (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	4	8	77
subtype1	1	1	18
subtype2	1	0	5
subtype3	1	2	10
subtype4	0	1	4
subtype5	1	4	40

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	44	54	15

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

P value = 0.435 (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	36	0.1 - 143.4 (17.8)
subtype1	44	16	0.1 - 143.4 (17.0)
subtype2	54	15	0.1 - 116.9 (19.6)
subtype3	15	5	0.7 - 70.5 (15.1)

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 = 0.162 (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	113	61.3 (14.1)
subtype1	44	61.2 (15.8)
subtype2	54	59.6 (11.8)
subtype3	15	67.7 (15.7)

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 = 0.355 (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	61	52
subtype1	21	23
subtype2	33	21
subtype3	7	8

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 = 0.548 (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	8	77
subtype1	1	2	25
subtype2	2	4	44
subtype3	1	2	8

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
Number of samples	42	54	17

'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	36	0.1 - 143.4 (17.8)
subtype1	42	16	0.1 - 143.4 (12.0)
subtype2	54	16	0.1 - 116.9 (18.1)
subtype3	17	4	0.7 - 70.5 (17.8)

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 = 0.0996 (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	113	61.3 (14.1)
subtype1	42	60.8 (16.0)
subtype2	54	59.5 (12.5)
subtype3	17	68.1 (13.0)

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 = 0.56 (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	61	52
subtype1	21	21
subtype2	32	22
subtype3	8	9

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 = 0.86 (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	8	77
subtype1	1	2	23
subtype2	2	4	43
subtype3	1	2	11

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 = 114
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)

Made with Nozzle