Correlation between aggregated molecular cancer subtypes and selected clinical features

Pancreatic Adenocarcinoma (Primary solid tumor)

23 May 2013 | analyses__2013_05_23

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): Correlation between aggregated molecular cancer subtypes and selected clinical features. Broad Institute of MIT and Harvard. doi:10.7908/C1WS8R97

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 10 clinical features across 26 patients, no significant finding 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.
3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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 do not correlate to any clinical features.
5 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.
2 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 10 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.


Clinical Features	Statistical Tests	Copy Number Ratio CNMF subtypes	METHLYATION CNMF	RNAseq CNMF subtypes	RNAseq cHierClus subtypes	MIRSEQ CNMF	MIRSEQ CHIERARCHICAL	MIRseq Mature CNMF subtypes	MIRseq Mature cHierClus subtypes
Time to Death	logrank test	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)	100 (1.00)
AGE	ANOVA	0.262 (1.00)	0.268 (1.00)	0.0527 (1.00)	0.0268 (1.00)	0.0251 (1.00)	0.328 (1.00)	0.22 (1.00)	0.46 (1.00)
GENDER	Fisher's exact test	1 (1.00)	0.142 (1.00)	0.0536 (1.00)	0.101 (1.00)	0.0759 (1.00)	0.395 (1.00)	0.37 (1.00)	0.284 (1.00)
HISTOLOGICAL TYPE	Chi-square test	0.809 (1.00)	0.716 (1.00)	0.358 (1.00)	0.295 (1.00)	0.246 (1.00)	0.55 (1.00)	0.648 (1.00)	0.634 (1.00)
DISTANT METASTASIS	Chi-square test	0.0182 (1.00)	0.412 (1.00)	0.57 (1.00)	0.32 (1.00)	0.324 (1.00)	0.498 (1.00)	0.564 (1.00)	0.6 (1.00)
LYMPH NODE METASTASIS	Fisher's exact test	0.0637 (1.00)	0.172 (1.00)	1 (1.00)	0.846 (1.00)	0.0639 (1.00)	0.0987 (1.00)	1 (1.00)	0.0219 (1.00)
COMPLETENESS OF RESECTION	Chi-square test	0.411 (1.00)	0.0899 (1.00)	0.0627 (1.00)	0.0633 (1.00)	0.112 (1.00)	0.441 (1.00)	0.114 (1.00)	0.279 (1.00)
NUMBER OF LYMPH NODES	ANOVA	0.0219 (1.00)	0.508 (1.00)	0.673 (1.00)	0.899 (1.00)	0.0388 (1.00)	0.137 (1.00)	0.719 (1.00)	0.0602 (1.00)
TUMOR STAGECODE	ANOVA
NEOPLASM DISEASESTAGE	Chi-square test	0.262 (1.00)	0.339 (1.00)	0.615 (1.00)	0.787 (1.00)	0.602 (1.00)	0.359 (1.00)	0.623 (1.00)	0.247 (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	5	12	3

'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	20	1	0.1 - 66.2 (0.9)
subtype1	5	1	0.1 - 66.2 (5.9)
subtype2	12	0	0.1 - 66.2 (0.8)
subtype3	3	0	0.2 - 12.0 (0.7)

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 = 0.262 (ANOVA), Q value = 1

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

	nPatients	Mean (Std.Dev)
ALL	20	66.3 (9.1)
subtype1	5	69.4 (10.6)
subtype2	12	67.0 (8.5)
subtype3	3	58.7 (7.0)

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 = 1 (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	10	10
subtype1	2	3
subtype2	6	6
subtype3	2	1

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 = 0.809 (Chi-square test), Q value = 1

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

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	17	2	1
subtype1	4	1	0
subtype2	10	1	1
subtype3	3	0	0

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 'DISTANT.METASTASIS'

P value = 0.0182 (Chi-square test), Q value = 1

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

nPatients	M0	M1	MX
ALL	3	1	16
subtype1	2	0	3
subtype2	0	0	12
subtype3	1	1	1

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

'Copy Number Ratio CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0637 (Fisher's exact test), Q value = 1

Table S7. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	12
subtype1	0	5
subtype2	7	5
subtype3	1	2

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

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

P value = 0.411 (Chi-square test), Q value = 1

Table S8. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	5	2
subtype1	3	1	1
subtype2	9	2	1
subtype3	1	2	0

Figure S7. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0219 (ANOVA), Q value = 1

Table S9. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	20	2.0 (2.9)
subtype1	5	5.0 (4.3)
subtype2	12	1.0 (1.5)
subtype3	3	1.3 (1.2)

Figure S8. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'Copy Number Ratio CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.262 (Chi-square test), Q value = 1

Table S10. Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	12	1	1
subtype1	0	0	0	5	0	0
subtype2	2	1	3	5	1	0
subtype3	0	0	0	2	0	1

Figure S9. Get High-res Image Clustering Approach #1: 'Copy Number Ratio CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #2: 'METHLYATION CNMF'

Table S11. Get Full Table Description of clustering approach #2: 'METHLYATION CNMF'

Cluster Labels	1	2	3
Number of samples	9	13	4

'METHLYATION CNMF' versus 'Time to Death'

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

Table S12. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	26	1	0.1 - 66.2 (1.0)
subtype1	9	0	0.1 - 66.2 (1.5)
subtype2	13	0	0.1 - 66.2 (1.0)
subtype3	4	1	0.3 - 66.2 (3.3)

Figure S10. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'

'METHLYATION CNMF' versus 'AGE'

P value = 0.268 (ANOVA), Q value = 1

Table S13. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	26	67.9 (9.5)
subtype1	9	63.7 (9.7)
subtype2	13	70.0 (8.9)
subtype3	4	70.5 (10.2)

Figure S11. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'

'METHLYATION CNMF' versus 'GENDER'

P value = 0.142 (Fisher's exact test), Q value = 1

Table S14. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	13	13
subtype1	5	4
subtype2	8	5
subtype3	0	4

Figure S12. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'

'METHLYATION CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.716 (Chi-square test), Q value = 1

Table S15. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	21	4	1
subtype1	7	2	0
subtype2	10	2	1
subtype3	4	0	0

Figure S13. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'METHLYATION CNMF' versus 'DISTANT.METASTASIS'

P value = 0.412 (Chi-square test), Q value = 1

Table S16. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	22
subtype1	2	1	6
subtype2	1	0	12
subtype3	0	0	4

Figure S14. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'METHLYATION CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.172 (Chi-square test), Q value = 1

Table S17. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1	N1B
ALL	8	17	1
subtype1	2	7	0
subtype2	5	8	0
subtype3	1	2	1

Figure S15. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0899 (Chi-square test), Q value = 1

Table S18. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	14	9	2
subtype1	3	5	1
subtype2	10	2	0
subtype3	1	2	1

Figure S16. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.508 (ANOVA), Q value = 1

Table S19. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	26	2.1 (2.6)
subtype1	9	1.7 (1.1)
subtype2	13	2.0 (3.1)
subtype3	4	3.5 (3.4)

Figure S17. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'METHLYATION CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.339 (Chi-square test), Q value = 1

Table S20. Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	18	1	1
subtype1	0	0	1	7	0	1
subtype2	2	1	2	8	0	0
subtype3	0	0	0	3	1	0

Figure S18. Get High-res Image Clustering Approach #2: 'METHLYATION CNMF' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #3: 'RNAseq CNMF subtypes'

Table S21. Get Full Table Description of clustering approach #3: 'RNAseq CNMF subtypes'

Cluster Labels	1	2
Number of samples	10	8

'RNAseq CNMF subtypes' versus 'Time to Death'

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

Table S22. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	Duration Range (Median), Month
ALL	18	0.1 - 66.2 (0.8)
subtype1	10	0.1 - 12.1 (0.8)
subtype2	8	0.1 - 66.2 (0.8)

Figure S19. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq CNMF subtypes' versus 'AGE'

P value = 0.0527 (t-test), Q value = 1

Table S23. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	18	64.9 (8.4)
subtype1	10	61.7 (9.4)
subtype2	8	69.0 (5.0)

Figure S20. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq CNMF subtypes' versus 'GENDER'

P value = 0.0536 (Fisher's exact test), Q value = 1

Table S24. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	8
subtype1	8	2
subtype2	2	6

Figure S21. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.358 (Chi-square test), Q value = 1

Table S25. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	16	1	1
subtype1	9	0	1
subtype2	7	1	0

Figure S22. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.57 (Chi-square test), Q value = 1

Table S26. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	14
subtype1	2	1	7
subtype2	1	0	7

Figure S23. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RNAseq CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S27. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	10
subtype1	4	6
subtype2	4	4

Figure S24. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0627 (Chi-square test), Q value = 1

Table S28. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	4	1
subtype1	5	4	1
subtype2	8	0	0

Figure S25. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.673 (t-test), Q value = 1

Table S29. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	18	1.8 (2.7)
subtype1	10	1.5 (1.4)
subtype2	8	2.1 (3.8)

Figure S26. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'RNAseq CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.615 (Chi-square test), Q value = 1

Table S30. Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	10	1	1
subtype1	1	0	2	6	0	1
subtype2	1	1	1	4	1	0

Figure S27. Get High-res Image Clustering Approach #3: 'RNAseq CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #4: 'RNAseq cHierClus subtypes'

Table S31. Get Full Table Description of clustering approach #4: 'RNAseq cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	4	8	6

'RNAseq cHierClus subtypes' versus 'Time to Death'

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

Table S32. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	Duration Range (Median), Month
ALL	18	0.1 - 66.2 (0.8)
subtype1	4	0.3 - 12.1 (1.7)
subtype2	8	0.1 - 66.2 (0.8)
subtype3	6	0.1 - 12.0 (0.8)

Figure S28. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

'RNAseq cHierClus subtypes' versus 'AGE'

P value = 0.0268 (ANOVA), Q value = 1

Table S33. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	18	64.9 (8.4)
subtype1	4	67.5 (12.1)
subtype2	8	69.0 (5.0)
subtype3	6	57.8 (5.1)

Figure S29. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'RNAseq cHierClus subtypes' versus 'GENDER'

P value = 0.101 (Fisher's exact test), Q value = 1

Table S34. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	8
subtype1	3	1
subtype2	2	6
subtype3	5	1

Figure S30. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'RNAseq cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.295 (Chi-square test), Q value = 1

Table S35. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	16	1	1
subtype1	3	0	1
subtype2	7	1	0
subtype3	6	0	0

Figure S31. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'RNAseq cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.32 (Chi-square test), Q value = 1

Table S36. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	14
subtype1	0	0	4
subtype2	1	0	7
subtype3	2	1	3

Figure S32. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'RNAseq cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.846 (Fisher's exact test), Q value = 1

Table S37. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	10
subtype1	2	2
subtype2	4	4
subtype3	2	4

Figure S33. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.0633 (Chi-square test), Q value = 1

Table S38. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	4	1
subtype1	2	1	1
subtype2	8	0	0
subtype3	3	3	0

Figure S34. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.899 (ANOVA), Q value = 1

Table S39. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	18	1.8 (2.7)
subtype1	4	1.5 (1.9)
subtype2	8	2.1 (3.8)
subtype3	6	1.5 (1.2)

Figure S35. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'RNAseq cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.787 (Chi-square test), Q value = 1

Table S40. Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	10	1	1
subtype1	1	0	1	2	0	0
subtype2	1	1	1	4	1	0
subtype3	0	0	1	4	0	1

Figure S36. Get High-res Image Clustering Approach #4: 'RNAseq cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #5: 'MIRSEQ CNMF'

Table S41. Get Full Table Description of clustering approach #5: 'MIRSEQ CNMF'

Cluster Labels	1	2	3	4	5
Number of samples	5	3	5	3	4

'MIRSEQ CNMF' versus 'Time to Death'

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

Table S42. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	20	1	0.1 - 66.2 (0.9)
subtype1	5	0	0.1 - 5.9 (0.7)
subtype2	3	0	0.7 - 1.0 (0.9)
subtype3	5	0	0.1 - 66.2 (0.6)
subtype4	3	0	3.0 - 12.1 (7.9)
subtype5	4	1	0.3 - 66.2 (6.3)

Figure S37. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CNMF' versus 'AGE'

P value = 0.0251 (ANOVA), Q value = 1

Table S43. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	20	66.3 (9.1)
subtype1	5	56.2 (3.5)
subtype2	3	66.0 (5.6)
subtype3	5	70.8 (4.1)
subtype4	3	73.7 (12.3)
subtype5	4	68.2 (9.9)

Figure S38. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #2: 'AGE'

'MIRSEQ CNMF' versus 'GENDER'

P value = 0.0759 (Chi-square test), Q value = 1

Table S44. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	10
subtype1	5	0
subtype2	1	2
subtype3	1	4
subtype4	2	1
subtype5	1	3

Figure S39. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CNMF' versus 'HISTOLOGICAL.TYPE'

P value = 0.246 (Chi-square test), Q value = 1

Table S45. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	17	2	1
subtype1	5	0	0
subtype2	3	0	0
subtype3	4	1	0
subtype4	1	1	1
subtype5	4	0	0

Figure S40. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CNMF' versus 'DISTANT.METASTASIS'

P value = 0.324 (Chi-square test), Q value = 1

Table S46. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	16
subtype1	2	1	2
subtype2	1	0	2
subtype3	0	0	5
subtype4	0	0	3
subtype5	0	0	4

Figure S41. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRSEQ CNMF' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0639 (Chi-square test), Q value = 1

Table S47. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	12
subtype1	2	3
subtype2	0	3
subtype3	4	1
subtype4	2	1
subtype5	0	4

Figure S42. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.112 (Chi-square test), Q value = 1

Table S48. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	5	2
subtype1	2	3	0
subtype2	3	0	0
subtype3	5	0	0
subtype4	2	0	1
subtype5	1	2	1

Figure S43. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.0388 (ANOVA), Q value = 1

Table S49. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	20	2.0 (2.9)
subtype1	5	1.4 (1.3)
subtype2	3	5.3 (5.1)
subtype3	5	0.2 (0.4)
subtype4	3	0.3 (0.6)
subtype5	4	4.0 (2.8)

Figure S44. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CNMF' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.602 (Chi-square test), Q value = 1

Table S50. Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	12	1	1
subtype1	0	0	1	3	0	1
subtype2	0	0	0	3	0	0
subtype3	1	1	1	1	1	0
subtype4	1	0	1	1	0	0
subtype5	0	0	0	4	0	0

Figure S45. Get High-res Image Clustering Approach #5: 'MIRSEQ CNMF' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #6: 'MIRSEQ CHIERARCHICAL'

Table S51. Get Full Table Description of clustering approach #6: 'MIRSEQ CHIERARCHICAL'

Cluster Labels	1	2	3
Number of samples	8	5	7

'MIRSEQ CHIERARCHICAL' versus 'Time to Death'

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

Table S52. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	20	1	0.1 - 66.2 (0.9)
subtype1	8	1	0.2 - 66.2 (6.9)
subtype2	5	0	0.1 - 66.2 (0.6)
subtype3	7	0	0.1 - 1.0 (0.7)

Figure S46. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #1: 'Time to Death'

'MIRSEQ CHIERARCHICAL' versus 'AGE'

P value = 0.328 (ANOVA), Q value = 1

Table S53. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	20	66.3 (9.1)
subtype1	8	66.8 (12.3)
subtype2	5	70.8 (4.1)
subtype3	7	62.7 (6.4)

Figure S47. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #2: 'AGE'

'MIRSEQ CHIERARCHICAL' versus 'GENDER'

P value = 0.395 (Fisher's exact test), Q value = 1

Table S54. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	10
subtype1	5	3
subtype2	1	4
subtype3	4	3

Figure S48. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #3: 'GENDER'

'MIRSEQ CHIERARCHICAL' versus 'HISTOLOGICAL.TYPE'

P value = 0.55 (Chi-square test), Q value = 1

Table S55. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	17	2	1
subtype1	6	1	1
subtype2	4	1	0
subtype3	7	0	0

Figure S49. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRSEQ CHIERARCHICAL' versus 'DISTANT.METASTASIS'

P value = 0.498 (Chi-square test), Q value = 1

Table S56. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	16
subtype1	2	1	5
subtype2	0	0	5
subtype3	1	0	6

Figure S50. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRSEQ CHIERARCHICAL' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0987 (Fisher's exact test), Q value = 1

Table S57. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	12
subtype1	3	5
subtype2	4	1
subtype3	1	6

Figure S51. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

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

P value = 0.441 (Chi-square test), Q value = 1

Table S58. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	5	2
subtype1	4	3	1
subtype2	5	0	0
subtype3	4	2	1

Figure S52. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

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

P value = 0.137 (ANOVA), Q value = 1

Table S59. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	20	2.0 (2.9)
subtype1	8	1.9 (2.6)
subtype2	5	0.2 (0.4)
subtype3	7	3.6 (3.6)

Figure S53. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'MIRSEQ CHIERARCHICAL' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.359 (Chi-square test), Q value = 1

Table S60. Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	12	1	1
subtype1	1	0	1	5	0	1
subtype2	1	1	1	1	1	0
subtype3	0	0	1	6	0	0

Figure S54. Get High-res Image Clustering Approach #6: 'MIRSEQ CHIERARCHICAL' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #7: 'MIRseq Mature CNMF subtypes'

Table S61. Get Full Table Description of clustering approach #7: 'MIRseq Mature CNMF subtypes'

Cluster Labels	1	2
Number of samples	11	9

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

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

Table S62. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	20	1	0.1 - 66.2 (0.9)
subtype1	11	1	0.1 - 66.2 (3.0)
subtype2	9	0	0.1 - 66.2 (0.7)

Figure S55. 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 = 0.22 (t-test), Q value = 1

Table S63. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	20	66.3 (9.1)
subtype1	11	64.2 (11.3)
subtype2	9	69.0 (4.7)

Figure S56. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature CNMF subtypes' versus 'GENDER'

P value = 0.37 (Fisher's exact test), Q value = 1

Table S64. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	10
subtype1	7	4
subtype2	3	6

Figure S57. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature CNMF subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.648 (Chi-square test), Q value = 1

Table S65. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	17	2	1
subtype1	9	1	1
subtype2	8	1	0

Figure S58. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq Mature CNMF subtypes' versus 'DISTANT.METASTASIS'

P value = 0.564 (Chi-square test), Q value = 1

Table S66. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	16
subtype1	2	1	8
subtype2	1	0	8

Figure S59. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'LYMPH.NODE.METASTASIS'

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

Table S67. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	12
subtype1	4	7
subtype2	4	5

Figure S60. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature CNMF subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.114 (Chi-square test), Q value = 1

Table S68. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	5	2
subtype1	5	4	2
subtype2	8	1	0

Figure S61. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature CNMF subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.719 (t-test), Q value = 1

Table S69. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	20	2.0 (2.9)
subtype1	11	1.8 (2.3)
subtype2	9	2.3 (3.6)

Figure S62. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature CNMF subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.623 (Chi-square test), Q value = 1

Table S70. Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	12	1	1
subtype1	1	0	2	7	0	1
subtype2	1	1	1	5	1	0

Figure S63. Get High-res Image Clustering Approach #7: 'MIRseq Mature CNMF subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Clustering Approach #8: 'MIRseq Mature cHierClus subtypes'

Table S71. Get Full Table Description of clustering approach #8: 'MIRseq Mature cHierClus subtypes'

Cluster Labels	1	2	3
Number of samples	9	5	6

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

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

Table S72. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #1: 'Time to Death'

	nPatients	nDeath	Duration Range (Median), Month
ALL	20	1	0.1 - 66.2 (0.9)
subtype1	9	1	0.2 - 66.2 (5.9)
subtype2	5	0	0.1 - 66.2 (0.6)
subtype3	6	0	0.1 - 1.0 (0.6)

Figure S64. 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 = 0.46 (ANOVA), Q value = 1

Table S73. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

	nPatients	Mean (Std.Dev)
ALL	20	66.3 (9.1)
subtype1	9	65.3 (12.3)
subtype2	5	70.8 (4.1)
subtype3	6	64.2 (5.6)

Figure S65. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #2: 'AGE'

'MIRseq Mature cHierClus subtypes' versus 'GENDER'

P value = 0.284 (Fisher's exact test), Q value = 1

Table S74. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

nPatients	FEMALE	MALE
ALL	10	10
subtype1	6	3
subtype2	1	4
subtype3	3	3

Figure S66. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #3: 'GENDER'

'MIRseq Mature cHierClus subtypes' versus 'HISTOLOGICAL.TYPE'

P value = 0.634 (Chi-square test), Q value = 1

Table S75. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

nPatients	PANCREAS-ADENOCARCINOMA DUCTAL TYPE	PANCREAS-ADENOCARCINOMA-OTHER SUBTYPE	PANCREAS-COLLOID (MUCINOUS NON-CYSTIC) CARCINOMA
ALL	17	2	1
subtype1	7	1	1
subtype2	4	1	0
subtype3	6	0	0

Figure S67. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #4: 'HISTOLOGICAL.TYPE'

'MIRseq Mature cHierClus subtypes' versus 'DISTANT.METASTASIS'

P value = 0.6 (Chi-square test), Q value = 1

Table S76. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

nPatients	M0	M1	MX
ALL	3	1	16
subtype1	2	1	6
subtype2	0	0	5
subtype3	1	0	5

Figure S68. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #5: 'DISTANT.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'LYMPH.NODE.METASTASIS'

P value = 0.0219 (Fisher's exact test), Q value = 1

Table S77. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

nPatients	N0	N1
ALL	8	12
subtype1	4	5
subtype2	4	1
subtype3	0	6

Figure S69. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #6: 'LYMPH.NODE.METASTASIS'

'MIRseq Mature cHierClus subtypes' versus 'COMPLETENESS.OF.RESECTION'

P value = 0.279 (Chi-square test), Q value = 1

Table S78. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

nPatients	R0	R1	RX
ALL	13	5	2
subtype1	4	4	1
subtype2	5	0	0
subtype3	4	1	1

Figure S70. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #7: 'COMPLETENESS.OF.RESECTION'

'MIRseq Mature cHierClus subtypes' versus 'NUMBER.OF.LYMPH.NODES'

P value = 0.0602 (ANOVA), Q value = 1

Table S79. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

	nPatients	Mean (Std.Dev)
ALL	20	2.0 (2.9)
subtype1	9	1.7 (2.5)
subtype2	5	0.2 (0.4)
subtype3	6	4.2 (3.5)

Figure S71. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #8: 'NUMBER.OF.LYMPH.NODES'

'MIRseq Mature cHierClus subtypes' versus 'NEOPLASM.DISEASESTAGE'

P value = 0.247 (Chi-square test), Q value = 1

Table S80. Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

nPatients	STAGE IA	STAGE IB	STAGE IIA	STAGE IIB	STAGE III	STAGE IV
ALL	2	1	3	12	1	1
subtype1	1	0	2	5	0	1
subtype2	1	1	1	1	1	0
subtype3	0	0	0	6	0	0

Figure S72. Get High-res Image Clustering Approach #8: 'MIRseq Mature cHierClus subtypes' versus Clinical Feature #10: 'NEOPLASM.DISEASESTAGE'

Methods & Data

Input

Cluster data file = PAAD-TP.mergedcluster.txt
Clinical data file = PAAD-TP.clin.merged.picked.txt
Number of patients = 26
Number of clustering approaches = 8
Number of selected clinical features = 10
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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