Correlation between aggregated molecular cancer subtypes and selected clinical features

Acute Myeloid Leukemia (Primary blood derived cancer - Peripheral blood)

28 January 2016 | analyses__2016_01_28

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 (2016): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1PG1R4C

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 6 different clustering approaches and 5 clinical features across 200 patients, 7 significant findings detected with P value < 0.05 and Q value < 0.25.

4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'RACE'.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 6 subtypes that correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
5 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'Time to Death' and 'YEARS_TO_BIRTH'.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. 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 6 different clustering approaches and 5 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, 7 significant findings detected.


Clinical Features	Time to Death	YEARS TO BIRTH	GENDER	RACE	ETHNICITY
Statistical Tests	logrank test	Kruskal-Wallis (anova)	Fisher's exact test	Fisher's exact test	Fisher's exact test
Copy Number Ratio CNMF subtypes	0.667 (0.87)	0.54 (0.787)	0.317 (0.733)	0.955 (1.00)	0.556 (0.787)
METHLYATION CNMF	4.31e-07 (6.47e-06)	4.2e-07 (6.47e-06)	0.859 (0.991)	0.287 (0.718)	0.454 (0.761)
RNAseq CNMF subtypes	0.495 (0.781)	0.442 (0.761)	0.143 (0.436)	0.0435 (0.187)	0.577 (0.787)
RNAseq cHierClus subtypes	0.000473 (0.00296)	3.26e-05 (0.000326)	0.413 (0.761)	0.145 (0.436)	0.082 (0.308)
MIRSEQ CNMF	0.0151 (0.0753)	0.000493 (0.00296)	0.793 (0.952)	0.763 (0.952)	1 (1.00)
MIRSEQ CHIERARCHICAL	0.161 (0.438)	0.457 (0.761)	0.386 (0.761)	0.957 (1.00)	1 (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
Number of samples	49	44	55	43

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

P value = 0.667 (logrank test), Q value = 0.87

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	178	116	0.0 - 94.1 (11.0)
subtype1	48	34	0.0 - 84.1 (18.0)
subtype2	42	29	0.0 - 82.1 (10.1)
subtype3	48	29	0.0 - 94.1 (8.0)
subtype4	40	24	0.0 - 75.1 (12.0)

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 'YEARS_TO_BIRTH'

P value = 0.54 (Kruskal-Wallis (anova)), Q value = 0.79

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

	nPatients	Mean (Std.Dev)
ALL	191	55.2 (16.1)
subtype1	49	53.4 (15.9)
subtype2	44	55.7 (17.4)
subtype3	55	54.1 (17.1)
subtype4	43	58.2 (13.4)

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

'Copy Number Ratio CNMF subtypes' versus 'GENDER'

P value = 0.317 (Fisher's exact test), Q value = 0.73

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

nPatients	FEMALE	MALE
ALL	87	104
subtype1	27	22
subtype2	18	26
subtype3	21	34
subtype4	21	22

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 = 0.955 (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	2	14	173
subtype1	1	4	44
subtype2	0	4	38
subtype3	1	4	50
subtype4	0	2	41

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

'Copy Number Ratio CNMF subtypes' versus 'ETHNICITY'

P value = 0.556 (Fisher's exact test), Q value = 0.79

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	3	185
subtype1	2	47
subtype2	0	42
subtype3	1	54
subtype4	0	42

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #2: 'METHLYATION CNMF'

Table S7. Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	25	25	62	38	44

'METHLYATION CNMF' versus 'Time to Death'

P value = 4.31e-07 (logrank test), Q value = 6.5e-06

Table S8. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	179	116	0.0 - 94.1 (12.0)
subtype1	25	20	0.0 - 69.0 (7.0)
subtype2	24	8	0.0 - 84.1 (22.0)
subtype3	56	45	0.0 - 56.1 (10.5)
subtype4	34	13	1.0 - 94.1 (20.5)
subtype5	40	30	0.0 - 55.0 (9.0)

Figure S6. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'YEARS_TO_BIRTH'

P value = 4.2e-07 (Kruskal-Wallis (anova)), Q value = 6.5e-06

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

	nPatients	Mean (Std.Dev)
ALL	194	55.1 (16.0)
subtype1	25	55.3 (14.6)
subtype2	25	49.0 (15.3)
subtype3	62	63.1 (12.9)
subtype4	38	44.4 (17.2)
subtype5	44	56.5 (14.0)

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

'METHLYATION CNMF' versus 'GENDER'

P value = 0.859 (Fisher's exact test), Q value = 0.99

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	89	105
subtype1	12	13
subtype2	13	12
subtype3	26	36
subtype4	16	22
subtype5	22	22

Figure S8. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'RACE'

P value = 0.287 (Fisher's exact test), Q value = 0.72

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	13	177
subtype1	0	0	25
subtype2	0	3	21
subtype3	0	4	58
subtype4	1	1	35
subtype5	1	5	38

Figure S9. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'RACE'

'METHLYATION CNMF' versus 'ETHNICITY'

P value = 0.454 (Fisher's exact test), Q value = 0.76

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	190
subtype1	0	25
subtype2	0	24
subtype3	0	61
subtype4	1	36
subtype5	0	44

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S13. Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2	3
Number of samples	75	52	46

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.495 (logrank test), Q value = 0.78

Table S14. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	160	103	0.0 - 94.1 (11.5)
subtype1	69	43	0.0 - 94.1 (14.0)
subtype2	50	33	0.0 - 75.1 (9.5)
subtype3	41	27	0.0 - 62.0 (10.0)

Figure S11. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'YEARS_TO_BIRTH'

P value = 0.442 (Kruskal-Wallis (anova)), Q value = 0.76

Table S15. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	173	55.3 (16.1)
subtype1	75	54.6 (17.3)
subtype2	52	57.8 (13.7)
subtype3	46	53.5 (16.7)

Figure S12. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.143 (Fisher's exact test), Q value = 0.44

Table S16. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	93
subtype1	32	43
subtype2	21	31
subtype3	27	19

Figure S13. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'RACE'

P value = 0.0435 (Fisher's exact test), Q value = 0.19

Table S17. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	13	156
subtype1	1	3	69
subtype2	1	2	49
subtype3	0	8	38

Figure S14. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'RACE'

'RNAseq CNMF subtypes' versus 'ETHNICITY'

P value = 0.577 (Fisher's exact test), Q value = 0.79

Table S18. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	169
subtype1	0	72
subtype2	1	51
subtype3	0	46

Figure S15. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S19. Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3	4	5	6
Number of samples	16	14	15	58	26	44

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.000473 (logrank test), Q value = 0.003

Table S20. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	160	103	0.0 - 94.1 (11.5)
subtype1	16	5	0.0 - 62.0 (36.0)
subtype2	14	5	0.9 - 75.1 (20.0)
subtype3	12	5	6.0 - 94.1 (20.5)
subtype4	53	41	0.0 - 73.0 (10.1)
subtype5	25	15	0.0 - 62.0 (8.1)
subtype6	40	32	0.0 - 69.0 (8.5)

Figure S16. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'YEARS_TO_BIRTH'

P value = 3.26e-05 (Kruskal-Wallis (anova)), Q value = 0.00033

Table S21. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	173	55.3 (16.1)
subtype1	16	47.8 (14.9)
subtype2	14	57.7 (14.6)
subtype3	15	36.6 (13.9)
subtype4	58	59.8 (15.5)
subtype5	26	53.3 (16.7)
subtype6	44	58.7 (13.0)

Figure S17. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.413 (Fisher's exact test), Q value = 0.76

Table S22. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	80	93
subtype1	9	7
subtype2	6	8
subtype3	6	9
subtype4	22	36
subtype5	16	10
subtype6	21	23

Figure S18. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'RACE'

P value = 0.145 (Fisher's exact test), Q value = 0.44

Table S23. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	13	156
subtype1	0	2	13
subtype2	0	0	14
subtype3	0	0	15
subtype4	1	3	53
subtype5	0	6	20
subtype6	1	2	41

Figure S19. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'RACE'

'RNAseq cHierClus subtypes' versus 'ETHNICITY'

P value = 0.082 (Fisher's exact test), Q value = 0.31

Table S24. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	169
subtype1	0	15
subtype2	1	13
subtype3	0	15
subtype4	0	56
subtype5	0	26
subtype6	0	44

Figure S20. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S25. Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	60	31	43	33	21

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.0151 (logrank test), Q value = 0.075

Table S26. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	174	112	0.0 - 94.1 (12.0)
subtype1	58	42	0.0 - 69.0 (8.1)
subtype2	30	15	0.0 - 62.0 (18.0)
subtype3	37	19	1.0 - 82.1 (19.0)
subtype4	30	21	0.0 - 94.1 (10.0)
subtype5	19	15	0.0 - 56.1 (9.0)

Figure S21. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'YEARS_TO_BIRTH'

P value = 0.000493 (Kruskal-Wallis (anova)), Q value = 0.003

Table S27. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	188	54.9 (16.2)
subtype1	60	56.9 (15.1)
subtype2	31	56.2 (14.7)
subtype3	43	46.7 (15.1)
subtype4	33	54.8 (18.7)
subtype5	21	64.1 (12.7)

Figure S22. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.793 (Fisher's exact test), Q value = 0.95

Table S28. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	87	101
subtype1	26	34
subtype2	12	19
subtype3	22	21
subtype4	17	16
subtype5	10	11

Figure S23. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'RACE'

P value = 0.763 (Fisher's exact test), Q value = 0.95

Table S29. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	13	171
subtype1	1	3	56
subtype2	0	1	30
subtype3	1	4	37
subtype4	0	2	30
subtype5	0	3	18

Figure S24. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'RACE'

'MIRSEQ CNMF' versus 'ETHNICITY'

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

Table S30. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	184
subtype1	1	59
subtype2	0	31
subtype3	0	42
subtype4	0	31
subtype5	0	21

Figure S25. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'ETHNICITY'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S31. Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	70	35	83

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.161 (logrank test), Q value = 0.44

Table S32. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	174	112	0.0 - 94.1 (12.0)
subtype1	68	48	0.0 - 69.0 (8.1)
subtype2	33	19	0.0 - 62.0 (12.0)
subtype3	73	45	0.0 - 94.1 (12.9)

Figure S26. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'YEARS_TO_BIRTH'

P value = 0.457 (Kruskal-Wallis (anova)), Q value = 0.76

Table S33. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

	nPatients	Mean (Std.Dev)
ALL	188	54.9 (16.2)
subtype1	70	55.8 (14.8)
subtype2	35	57.3 (15.2)
subtype3	83	53.1 (17.6)

Figure S27. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'YEARS_TO_BIRTH'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.386 (Fisher's exact test), Q value = 0.76

Table S34. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	87	101
subtype1	36	34
subtype2	13	22
subtype3	38	45

Figure S28. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'RACE'

P value = 0.957 (Fisher's exact test), Q value = 1

Table S35. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

nPatients	ASIAN	BLACK OR AFRICAN AMERICAN	WHITE
ALL	2	13	171
subtype1	1	6	63
subtype2	0	2	33
subtype3	1	5	75

Figure S29. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'RACE'

'MIRSEQ CHIERARCHICAL' versus 'ETHNICITY'

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

Table S36. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

nPatients	HISPANIC OR LATINO	NOT HISPANIC OR LATINO
ALL	1	184
subtype1	0	70
subtype2	0	35
subtype3	1	79

Figure S30. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'ETHNICITY'

Methods & Data

Input

Cluster data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/GDAC_mergedClustering/LAML-TB/22541002/LAML-TB.mergedcluster.txt
Clinical data file = /xchip/cga/gdac-prod/tcga-gdac/jobResults/Append_Data/LAML-TB/22506504/LAML-TB.merged_data.txt
Number of patients = 200
Number of clustering approaches = 6
Number of selected clinical features = 5
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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