Stomach Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features<BR>(primary solid tumor cohort)

Stomach Adenocarcinoma: Correlation between molecular cancer subtypes and selected clinical features
(primary solid tumor cohort)

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

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 11 clinical features across 178 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.
4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'AGE' and 'PATHOLOGY.T'.
CNMF clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that correlate to 'TUMOR.STAGE'.
3 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 correlate to 'HISTOLOGICAL.TYPE'.

Results

Overview of the results

Table 1. Get Full Table Overview of the association between subtypes identified by 6 different clustering approaches and 11 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	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL
Time to Death	logrank test	0.32 (1.00)	0.843 (1.00)	0.667 (1.00)	0.8 (1.00)	0.41 (1.00)	0.76 (1.00)
AGE	ANOVA	0.0304 (1.00)	0.00201 (0.126)	0.143 (1.00)	0.447 (1.00)	0.0549 (1.00)	0.799 (1.00)
GENDER	Fisher's exact test	0.0189 (1.00)	0.935 (1.00)	0.547 (1.00)	0.917 (1.00)	0.492 (1.00)	0.709 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.0266 (1.00)	0.0758 (1.00)	0.0766 (1.00)	0.407 (1.00)	0.00806 (0.476)	0.00123 (0.0787)
PATHOLOGY T	Chi-square test	0.00436 (0.262)	0.00279 (0.173)	0.242 (1.00)	0.425 (1.00)	0.302 (1.00)	0.175 (1.00)
PATHOLOGY N	Chi-square test	0.303 (1.00)	0.689 (1.00)	0.174 (1.00)	0.0334 (1.00)	0.14 (1.00)	0.878 (1.00)
PATHOLOGICSPREAD(M)	Chi-square test	0.309 (1.00)	0.517 (1.00)	0.327 (1.00)	0.474 (1.00)	0.0922 (1.00)	0.795 (1.00)
TUMOR STAGE	Chi-square test	0.439 (1.00)	0.0543 (1.00)	0.0535 (1.00)	0.00304 (0.186)	0.697 (1.00)	0.636 (1.00)
RADIATIONS RADIATION REGIMENINDICATION	Fisher's exact test	0.226 (1.00)	0.204 (1.00)			0.0152 (0.866)	0.524 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.673 (1.00)	0.462 (1.00)	0.468 (1.00)	0.61 (1.00)	0.00856 (0.496)	0.356 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0568 (1.00)	0.708 (1.00)	0.931 (1.00)	0.563 (1.00)	0.0334 (1.00)	0.902 (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
Number of samples	88	71	18

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

P value = 0.32 (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	140	18	0.1 - 72.2 (1.2)
subtype1	70	8	0.1 - 70.1 (0.9)
subtype2	56	7	0.1 - 72.2 (4.0)
subtype3	14	3	0.2 - 53.0 (11.5)

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 = 0.0304 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	170	66.9 (11.1)
subtype1	87	65.3 (12.1)
subtype2	67	67.4 (10.0)
subtype3	16	73.1 (7.1)

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 = 0.0189 (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	70	107
subtype1	44	44
subtype2	21	50
subtype3	5	13

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 'HISTOLOGICAL.TYPE'

P value = 0.0266 (Chi-square test), Q value = 1

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

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH ADENOCARCINOMA - SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	25	87	1	10	3	10	36
subtype1	15	49	1	5	1	0	15
subtype2	9	30	0	5	2	9	13
subtype3	1	8	0	0	0	1	8

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

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.00436 (Chi-square test), Q value = 0.26

Table S6. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	54	64	43
subtype1	3	18	31	31
subtype2	1	30	26	10
subtype3	2	6	7	2

Figure S5. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.303 (Chi-square test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	56	61	29	20
subtype1	30	32	14	7
subtype2	18	27	12	10
subtype3	8	2	3	3

Figure S6. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'Copy Number Ratio CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.309 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	146	18	13
subtype1	72	10	6
subtype2	56	8	7
subtype3	18	0	0

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'Copy Number Ratio CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.439 (Chi-square test), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	26	48	58	29
subtype1	11	24	32	13
subtype2	10	20	20	15
subtype3	5	4	6	1

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

'Copy Number Ratio CNMF subtypes' versus 'RADIATIONS.RADIATION.REGIMENINDICATION'

P value = 0.226 (Fisher's exact test), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	172
subtype1	1	87
subtype2	4	67
subtype3	0	18

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.673 (Chi-square test), Q value = 1

Table S11. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	123	7	12	25
subtype1	62	2	6	15
subtype2	47	4	6	8
subtype3	14	1	0	2

Figure S10. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0568 (ANOVA), Q value = 1

Table S12. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	133	4.0 (5.7)
subtype1	73	3.1 (4.1)
subtype2	51	5.5 (7.3)
subtype3	9	2.8 (4.5)

Figure S11. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #2: 'METHLYATION CNMF'

Table S13. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3	4
Number of samples	18	28	38	46

'METHLYATION CNMF' versus 'Time to Death'

P value = 0.843 (logrank test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	121	13	0.1 - 72.2 (1.0)
subtype1	18	0	0.1 - 8.7 (0.9)
subtype2	26	4	0.1 - 65.1 (1.0)
subtype3	36	4	0.1 - 70.1 (1.0)
subtype4	41	5	0.1 - 72.2 (1.5)

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.00201 (ANOVA), Q value = 0.13

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	123	65.6 (11.1)
subtype1	17	62.8 (10.0)
subtype2	26	71.0 (10.4)
subtype3	38	61.2 (11.9)
subtype4	42	67.4 (9.7)

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.935 (Fisher's exact test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	48	82
subtype1	7	11
subtype2	9	19
subtype3	15	23
subtype4	17	29

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.0758 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH ADENOCARCINOMA - SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	23	58	1	9	3	9	27
subtype1	4	11	0	0	0	1	2
subtype2	3	11	0	3	1	2	8
subtype3	13	14	1	4	0	0	6
subtype4	3	22	0	2	2	6	11

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'PATHOLOGY.T'

P value = 0.00279 (Chi-square test), Q value = 0.17

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	4	39	53	34
subtype1	0	2	7	9
subtype2	4	10	9	5
subtype3	0	10	16	12
subtype4	0	17	21	8

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'METHLYATION CNMF' versus 'PATHOLOGY.N'

P value = 0.689 (Chi-square test), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	49	43	23	15
subtype1	8	4	3	3
subtype2	14	7	3	4
subtype3	12	14	9	3
subtype4	15	18	8	5

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'METHLYATION CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.517 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	109	11	10
subtype1	13	3	2
subtype2	25	1	2
subtype3	33	4	1
subtype4	38	3	5

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'METHLYATION CNMF' versus 'TUMOR.STAGE'

P value = 0.0543 (Chi-square test), Q value = 1

Table S21. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	19	44	49	18
subtype1	1	4	10	3
subtype2	10	9	7	2
subtype3	4	13	16	5
subtype4	4	18	16	8

Figure S19. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.204 (Fisher's exact test), Q value = 1

Table S22. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	125
subtype1	0	18
subtype2	1	27
subtype3	0	38
subtype4	4	42

Figure S20. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.462 (Chi-square test), Q value = 1

Table S23. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2
ALL	108	6	5
subtype1	15	0	1
subtype2	27	0	1
subtype3	30	4	1
subtype4	36	2	2

Figure S21. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.708 (ANOVA), Q value = 1

Table S24. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	119	4.2 (5.9)
subtype1	16	5.6 (6.6)
subtype2	27	3.4 (7.1)
subtype3	35	4.1 (5.6)
subtype4	41	4.1 (5.2)

Figure S22. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S25. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2
Number of samples	22	21

'RNAseq CNMF subtypes' versus 'Time to Death'

P value = 0.667 (logrank test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	18	5	1.0 - 54.0 (14.0)
subtype1	7	2	1.0 - 22.0 (12.0)
subtype2	11	3	1.0 - 54.0 (18.9)

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.143 (t-test), Q value = 1

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	43	70.4 (10.5)
subtype1	22	68.1 (11.6)
subtype2	21	72.8 (8.8)

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.547 (Fisher's exact test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	23
subtype1	9	13
subtype2	11	10

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.0766 (Chi-square test), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	2	25	1	1	9
subtype1	1	15	1	1	1
subtype2	1	10	0	0	8

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.T'

P value = 0.242 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	2	14	9	9
subtype1	0	6	6	6
subtype2	2	8	3	3

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq CNMF subtypes' versus 'PATHOLOGY.N'

P value = 0.174 (Chi-square test), Q value = 1

Table S31. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	8	14	5	5
subtype1	2	10	2	2
subtype2	6	4	3	3

Figure S28. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq CNMF subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.327 (Chi-square test), Q value = 1

Table S32. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	34	7	2
subtype1	16	4	2
subtype2	18	3	0

Figure S29. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq CNMF subtypes' versus 'TUMOR.STAGE'

P value = 0.0535 (Chi-square test), Q value = 1

Table S33. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	7	4	6	11
subtype1	1	4	3	6
subtype2	6	0	3	5

Figure S30. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.931 (t-test), Q value = 1

Table S34. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	11	2.8 (2.2)
subtype1	7	2.9 (2.7)
subtype2	4	2.8 (1.3)

Figure S31. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S35. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	15	7	21

'RNAseq cHierClus subtypes' versus 'Time to Death'

P value = 0.8 (logrank test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	18	5	1.0 - 54.0 (14.0)
subtype1	9	3	1.0 - 54.0 (18.9)
subtype2	2	0	1.0 - 30.0 (15.5)
subtype3	7	2	1.0 - 22.0 (12.0)

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.447 (ANOVA), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	43	70.4 (10.5)
subtype1	15	72.8 (8.9)
subtype2	7	66.9 (11.4)
subtype3	21	69.8 (11.2)

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.917 (Fisher's exact test), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	20	23
subtype1	8	7
subtype2	3	4
subtype3	9	12

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.407 (Chi-square test), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	2	25	1	1	9
subtype1	1	7	0	0	5
subtype2	0	4	0	0	3
subtype3	1	14	1	1	1

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.T'

P value = 0.425 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	2	14	9	9
subtype1	1	6	3	3
subtype2	1	3	1	0
subtype3	0	5	5	6

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'PATHOLOGY.T'

'RNAseq cHierClus subtypes' versus 'PATHOLOGY.N'

P value = 0.0334 (Chi-square test), Q value = 1

Table S41. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	8	14	5	5
subtype1	3	2	3	3
subtype2	4	2	0	0
subtype3	1	10	2	2

Figure S37. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'PATHOLOGY.N'

'RNAseq cHierClus subtypes' versus 'PATHOLOGICSPREAD(M)'

P value = 0.474 (Chi-square test), Q value = 1

Table S42. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	34	7	2
subtype1	12	3	0
subtype2	6	0	1
subtype3	16	4	1

Figure S38. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'RNAseq cHierClus subtypes' versus 'TUMOR.STAGE'

P value = 0.00304 (Chi-square test), Q value = 0.19

Table S43. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	7	4	6	11
subtype1	3	0	3	5
subtype2	4	0	0	0
subtype3	0	4	3	6

Figure S39. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.563 (ANOVA), Q value = 1

Table S44. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	11	2.8 (2.2)
subtype1	2	2.0 (1.4)
subtype2	3	2.3 (2.1)
subtype3	6	3.3 (2.7)

Figure S40. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S45. Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3
Number of samples	84	36	45

'MIRSEQ CNMF' versus 'Time to Death'

P value = 0.41 (logrank test), Q value = 1

Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	128	17	0.1 - 72.2 (1.4)
subtype1	62	12	0.1 - 70.1 (2.5)
subtype2	29	2	0.1 - 65.1 (1.2)
subtype3	37	3	0.1 - 72.2 (1.2)

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0549 (ANOVA), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	158	67.5 (10.6)
subtype1	83	68.2 (10.1)
subtype2	36	63.9 (12.0)
subtype3	39	69.4 (9.5)

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.492 (Fisher's exact test), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	66	99
subtype1	37	47
subtype2	14	22
subtype3	15	30

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.00806 (Chi-square test), Q value = 0.48

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH ADENOCARCINOMA - SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	21	80	1	9	3	10	36
subtype1	10	40	1	2	0	5	22
subtype2	10	16	0	4	0	1	4
subtype3	1	24	0	3	3	4	10

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'PATHOLOGY.T'

P value = 0.302 (Chi-square test), Q value = 1

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	53	62	34
subtype1	4	33	29	13
subtype2	0	9	14	10
subtype3	2	11	19	11

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CNMF' versus 'PATHOLOGY.N'

P value = 0.14 (Chi-square test), Q value = 1

Table S51. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	53	57	25	19
subtype1	24	29	13	10
subtype2	9	13	5	8
subtype3	20	15	7	1

Figure S46. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CNMF' versus 'PATHOLOGICSPREAD(M)'

P value = 0.0922 (Chi-square test), Q value = 1

Table S52. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	134	18	13
subtype1	65	14	5
subtype2	32	2	2
subtype3	37	2	6

Figure S47. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CNMF' versus 'TUMOR.STAGE'

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

Table S53. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	26	44	50	29
subtype1	13	19	24	17
subtype2	4	10	14	5
subtype3	9	15	12	7

Figure S48. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.0152 (Fisher's exact test), Q value = 0.87

Table S54. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	160
subtype1	0	84
subtype2	1	35
subtype3	4	41

Figure S49. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.00856 (Chi-square test), Q value = 0.5

Table S55. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	110	7	12	25
subtype1	47	3	11	18
subtype2	26	2	0	5
subtype3	37	2	1	2

Figure S50. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0334 (ANOVA), Q value = 1

Table S56. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	121	4.2 (5.8)
subtype1	52	5.0 (7.1)
subtype2	31	5.3 (5.2)
subtype3	38	2.2 (3.4)

Figure S51. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S57. Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	41	73	51

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

P value = 0.76 (logrank test), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	128	17	0.1 - 72.2 (1.4)
subtype1	34	3	0.3 - 55.0 (3.3)
subtype2	56	9	0.1 - 72.2 (1.7)
subtype3	38	5	0.1 - 70.1 (1.0)

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.799 (ANOVA), Q value = 1

Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	158	67.5 (10.6)
subtype1	39	67.8 (8.4)
subtype2	70	68.0 (10.1)
subtype3	49	66.7 (12.7)

Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.709 (Fisher's exact test), Q value = 1

Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	66	99
subtype1	15	26
subtype2	32	41
subtype3	19	32

Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.00123 (Chi-square test), Q value = 0.079

Table S61. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	STOMACH ADENOCARCINOMA - DIFFUSE TYPE	STOMACH ADENOCARCINOMA - NOT OTHERWISE SPECIFIED (NOS)	STOMACH ADENOCARCINOMA - SIGNET RING TYPE	STOMACH INTESTINAL ADENOCARCINOMA - MUCINOUS TYPE	STOMACH INTESTINAL ADENOCARCINOMA - PAPILLARY TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TUBULAR TYPE	STOMACH INTESTINAL ADENOCARCINOMA - TYPE NOT OTHERWISE SPECIFIED (NOS)
ALL	21	80	1	9	3	10	36
subtype1	3	16	0	0	2	8	12
subtype2	9	37	1	3	1	2	17
subtype3	9	27	0	6	0	0	7

Figure S55. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.T'

P value = 0.175 (Chi-square test), Q value = 1

Table S62. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

nPatients	T1	T2	T3	T4
ALL	6	53	62	34
subtype1	0	12	17	10
subtype2	5	28	26	10
subtype3	1	13	19	14

Figure S56. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'PATHOLOGY.T'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGY.N'

P value = 0.878 (Chi-square test), Q value = 1

Table S63. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

nPatients	N0	N1	N2	N3
ALL	53	57	25	19
subtype1	11	13	8	5
subtype2	26	24	11	7
subtype3	16	20	6	7

Figure S57. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'PATHOLOGY.N'

'MIRSEQ CHIERARCHICAL' versus 'PATHOLOGICSPREAD(M)'

P value = 0.795 (Chi-square test), Q value = 1

Table S64. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

nPatients	M0	M1	MX
ALL	134	18	13
subtype1	34	3	4
subtype2	57	10	6
subtype3	43	5	3

Figure S58. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'PATHOLOGICSPREAD(M)'

'MIRSEQ CHIERARCHICAL' versus 'TUMOR.STAGE'

P value = 0.636 (Chi-square test), Q value = 1

Table S65. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

nPatients	I	II	III	IV
ALL	26	44	50	29
subtype1	4	14	11	8
subtype2	15	16	23	11
subtype3	7	14	16	10

Figure S59. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'TUMOR.STAGE'

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

P value = 0.524 (Fisher's exact test), Q value = 1

Table S66. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

nPatients	NO	YES
ALL	5	160
subtype1	2	39
subtype2	1	72
subtype3	2	49

Figure S60. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #9: 'RADIATIONS.RADIATION.REGIMENINDICATION'

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

P value = 0.356 (Chi-square test), Q value = 1

Table S67. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	R2	RX
ALL	110	7	12	25
subtype1	31	1	2	4
subtype2	45	2	8	13
subtype3	34	4	2	8

Figure S61. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.902 (ANOVA), Q value = 1

Table S68. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	121	4.2 (5.8)
subtype1	29	4.0 (4.7)
subtype2	48	4.5 (7.2)
subtype3	44	4.0 (4.8)

Figure S62. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #11: 'NUMBER.OF.LYMPH.NODES'

Methods & Data

Input

Cluster data file = STAD-TP.mergedcluster.txt
Clinical data file = STAD-TP.clin.merged.picked.txt
Number of patients = 178
Number of clustering approaches = 6
Number of selected clinical features = 11
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

Student's t-test analysis

For continuous numerical clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the clinical values between two tumor subtypes using 't.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] Lehmann and Romano, Testing Statistical Hypotheses (3E ed.), New York: Springer. ISBN 0387988645 (2005)

[8] 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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