Correlation between molecular cancer subtypes and selected clinical features

Prostate Adenocarcinoma (Primary solid tumor)

21 April 2013 | analyses__2013_04_21

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 (2013): Prostate Adenocarcinoma (Primary solid tumor cohort) - 21 April 2013: Correlation between molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1542KKD

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 155 patients, 3 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 correlate to 'AGE' and 'NUMBER.OF.LYMPH.NODES'.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes 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, 3 significant findings detected.


Clinical Features	Time to Death	AGE	RADIATIONS RADIATION REGIMENINDICATION	COMPLETENESS OF RESECTION	NUMBER OF LYMPH NODES
Statistical Tests	logrank test	ANOVA	Fisher's exact test	Chi-square test	ANOVA
Copy Number Ratio CNMF subtypes	100 (1.00)	0.00523 (0.146)	0.85 (1.00)	0.647 (1.00)	0.00288 (0.0837)
METHLYATION CNMF	100 (1.00)	0.00237 (0.0711)	0.862 (1.00)	0.211 (1.00)	0.123 (1.00)
RNAseq CNMF subtypes	100 (1.00)	0.191 (1.00)	0.868 (1.00)	0.258 (1.00)	0.164 (1.00)
RNAseq cHierClus subtypes	100 (1.00)	0.86 (1.00)	0.735 (1.00)	0.166 (1.00)	0.152 (1.00)
MIRSEQ CNMF	100 (1.00)	0.0265 (0.715)	0.466 (1.00)	0.454 (1.00)	0.0459 (1.00)
MIRSEQ CHIERARCHICAL	100 (1.00)	0.399 (1.00)	0.527 (1.00)	0.697 (1.00)	0.387 (1.00)

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

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

Cluster Labels	1	2	3	4
Number of samples	28	72	52	2

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

P value = 100 (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	152	1	0.3 - 66.0 (15.0)
subtype1	28	0	0.3 - 63.3 (11.2)
subtype2	72	0	1.0 - 65.9 (16.9)
subtype3	52	1	0.9 - 66.0 (16.3)

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 = 0.00523 (ANOVA), Q value = 0.15

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

	nPatients	Mean (Std.Dev)
ALL	151	60.6 (6.7)
subtype1	28	62.1 (5.7)
subtype2	71	58.8 (7.1)
subtype3	52	62.3 (6.1)

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 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.85 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	5	147
subtype1	1	27
subtype2	3	69
subtype3	1	51

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

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

P value = 0.647 (Chi-square test), Q value = 1

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

nPatients	R0	R1	RX
ALL	116	29	2
subtype1	19	6	0
subtype2	56	13	2
subtype3	41	10	0

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

'Copy Number Ratio CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.00288 (ANOVA), Q value = 0.084

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	133	0.2 (0.7)
subtype1	26	0.0 (0.0)
subtype2	59	0.1 (0.3)
subtype3	48	0.5 (1.2)

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

Clustering Approach #2: 'METHLYATION CNMF'

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

Cluster Labels	1	2	3
Number of samples	47	65	43

'METHLYATION CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	155	1	0.3 - 66.0 (15.0)
subtype1	47	0	0.3 - 65.9 (15.1)
subtype2	65	1	1.0 - 66.0 (16.6)
subtype3	43	0	1.1 - 62.4 (13.9)

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

'METHLYATION CNMF' versus 'AGE'

P value = 0.00237 (ANOVA), Q value = 0.071

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

	nPatients	Mean (Std.Dev)
ALL	154	60.4 (6.9)
subtype1	46	61.9 (6.3)
subtype2	65	61.4 (6.8)
subtype3	43	57.4 (6.7)

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

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

P value = 0.862 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	150
subtype1	1	46
subtype2	2	63
subtype3	2	41

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

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

P value = 0.211 (Chi-square test), Q value = 1

Table S11. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	118	30	2
subtype1	35	11	0
subtype2	51	11	0
subtype3	32	8	2

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

'METHLYATION CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.123 (ANOVA), Q value = 1

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	136	0.2 (0.7)
subtype1	43	0.2 (0.5)
subtype2	58	0.3 (1.0)
subtype3	35	0.0 (0.2)

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

Clustering Approach #3: 'RNAseq CNMF subtypes'

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

Cluster Labels	1	2	3
Number of samples	51	49	53

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	153	1	0.3 - 66.0 (14.8)
subtype1	51	0	0.3 - 65.9 (16.0)
subtype2	49	0	1.0 - 62.4 (12.8)
subtype3	53	1	1.0 - 66.0 (16.6)

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

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.191 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	60.5 (6.9)
subtype1	50	60.8 (6.6)
subtype2	49	59.1 (7.1)
subtype3	53	61.5 (6.8)

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

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

P value = 0.868 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	148
subtype1	1	50
subtype2	2	47
subtype3	2	51

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

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

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

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

nPatients	R0	R1	RX
ALL	117	29	2
subtype1	39	12	0
subtype2	38	7	2
subtype3	40	10	0

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

'RNAseq CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.164 (ANOVA), Q value = 1

Table S18. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	135	0.2 (0.7)
subtype1	44	0.2 (0.9)
subtype2	44	0.0 (0.2)
subtype3	47	0.3 (0.8)

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

Clustering Approach #4: 'RNAseq cHierClus subtypes'

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

Cluster Labels	1	2	3
Number of samples	43	64	46

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	153	1	0.3 - 66.0 (14.8)
subtype1	43	0	1.0 - 65.9 (16.0)
subtype2	64	1	0.9 - 66.0 (14.8)
subtype3	46	0	0.3 - 62.4 (12.4)

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

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.86 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	152	60.5 (6.9)
subtype1	42	60.7 (6.9)
subtype2	64	60.7 (6.9)
subtype3	46	60.0 (6.9)

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

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

P value = 0.735 (Fisher's exact test), Q value = 1

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

nPatients	NO	YES
ALL	5	148
subtype1	1	42
subtype2	3	61
subtype3	1	45

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

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

P value = 0.166 (Chi-square test), Q value = 1

Table S23. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	117	29	2
subtype1	32	11	0
subtype2	49	12	0
subtype3	36	6	2

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

'RNAseq cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.152 (ANOVA), Q value = 1

Table S24. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	135	0.2 (0.7)
subtype1	37	0.2 (1.0)
subtype2	57	0.3 (0.8)
subtype3	41	0.0 (0.2)

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

Clustering Approach #5: 'MIRSEQ CNMF'

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

Cluster Labels	1	2	3	4
Number of samples	43	40	26	45

'MIRSEQ CNMF' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	1	0.3 - 66.0 (14.9)
subtype1	43	0	0.9 - 66.0 (16.6)
subtype2	40	0	1.0 - 54.9 (19.1)
subtype3	26	0	0.3 - 64.1 (16.0)
subtype4	45	1	1.0 - 66.0 (5.6)

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

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0265 (ANOVA), Q value = 0.71

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

	nPatients	Mean (Std.Dev)
ALL	153	60.4 (6.9)
subtype1	43	63.0 (6.2)
subtype2	40	58.7 (6.7)
subtype3	26	59.4 (6.8)
subtype4	44	60.0 (7.1)

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

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

P value = 0.466 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	149
subtype1	2	41
subtype2	2	38
subtype3	1	25
subtype4	0	45

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

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

P value = 0.454 (Chi-square test), Q value = 1

Table S29. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	118	29	2
subtype1	33	10	0
subtype2	30	8	1
subtype3	18	7	0
subtype4	37	4	1

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

'MIRSEQ CNMF' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0459 (ANOVA), Q value = 1

Table S30. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	135	0.2 (0.7)
subtype1	40	0.5 (1.2)
subtype2	37	0.1 (0.3)
subtype3	21	0.0 (0.2)
subtype4	37	0.1 (0.4)

Figure S25. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

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

Cluster Labels	1	2	3
Number of samples	31	59	64

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 100 (logrank test), Q value = 1

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

	nPatients	nDeath	Duration Range (Median), Month
ALL	154	1	0.3 - 66.0 (14.9)
subtype1	31	0	0.3 - 64.1 (18.2)
subtype2	59	0	1.0 - 54.9 (19.5)
subtype3	64	1	0.9 - 66.0 (5.6)

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

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.399 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	153	60.4 (6.9)
subtype1	30	59.1 (7.1)
subtype2	59	60.3 (6.8)
subtype3	64	61.1 (6.8)

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

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

P value = 0.527 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	149
subtype1	1	30
subtype2	3	56
subtype3	1	63

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

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

P value = 0.697 (Chi-square test), Q value = 1

Table S35. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	118	29	2
subtype1	22	8	0
subtype2	46	12	1
subtype3	50	9	1

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

'MIRSEQ CHIERARCHICAL' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.387 (ANOVA), Q value = 1

Table S36. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	135	0.2 (0.7)
subtype1	23	0.0 (0.2)
subtype2	55	0.3 (0.8)
subtype3	57	0.2 (0.8)

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

Methods & Data

Input

Cluster data file = PRAD-TP.mergedcluster.txt
Clinical data file = PRAD-TP.clin.merged.picked.txt
Number of patients = 155
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

ANOVA analysis

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

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

Chi-square test

For multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' function in R

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

This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.

References

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

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

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

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

[5] 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)

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

[7] Benjamini and Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society Series B 59:289-300 (1995)

Made with Nozzle