Correlation between molecular cancer subtypes and selected clinical features
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 2 different clustering approaches and 2 clinical features across 45 patients, no significant finding detected with P value < 0.05.

  • CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.

  • Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that 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 2 different clustering approaches and 2 clinical features. Shown in the table are P values from statistical tests. Thresholded by P value < 0.05, no significant finding detected.

Clinical
Features 		VITALSTATUS GENDER
Statistical Tests Fisher's exact test Fisher's exact test
MIRseq CNMF subtypes 0.793 0.419
MIRseq cHierClus subtypes 0.912 0.374
Clustering Approach #1: 'MIRseq CNMF subtypes'

Table S1.  Get Full Table Description of clustering approach #1: 'MIRseq CNMF subtypes'

Cluster Labels 1 2 3
Number of samples 9 16 20
'MIRseq CNMF subtypes' versus 'VITALSTATUS'

P value = 0.793 (Fisher's exact test)

Table S2.  Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'VITALSTATUS'

nPatients Class0 Class1
ALL 24 21
subtype1 4 5
subtype2 8 8
subtype3 12 8

Figure S1.  Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #1: 'VITALSTATUS'

'MIRseq CNMF subtypes' versus 'GENDER'

P value = 0.419 (Fisher's exact test)

Table S3.  Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'GENDER'

nPatients FEMALE MALE
ALL 18 27
subtype1 2 7
subtype2 8 8
subtype3 8 12

Figure S2.  Get High-res Image Clustering Approach #1: 'MIRseq CNMF subtypes' versus Clinical Feature #2: 'GENDER'

Clustering Approach #2: 'MIRseq cHierClus subtypes'

Table S4.  Get Full Table Description of clustering approach #2: 'MIRseq cHierClus subtypes'

Cluster Labels 1 2 3
Number of samples 8 29 8
'MIRseq cHierClus subtypes' versus 'VITALSTATUS'

P value = 0.912 (Fisher's exact test)

Table S5.  Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'VITALSTATUS'

nPatients Class0 Class1
ALL 24 21
subtype1 4 4
subtype2 15 14
subtype3 5 3

Figure S3.  Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #1: 'VITALSTATUS'

'MIRseq cHierClus subtypes' versus 'GENDER'

P value = 0.374 (Fisher's exact test)

Table S6.  Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

nPatients FEMALE MALE
ALL 18 27
subtype1 2 6
subtype2 14 15
subtype3 2 6

Figure S4.  Get High-res Image Clustering Approach #2: 'MIRseq cHierClus subtypes' versus Clinical Feature #2: 'GENDER'

Methods & Data
Input

  • Cluster data file = LIHC.mergedcluster.txt

  • Clinical data file = LIHC.clin.merged.picked.txt

  • Number of patients = 45

  • Number of clustering approaches = 2

  • Number of selected clinical features = 2

  • 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)

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

References
[1] Brunet et al., Metagenes and molecular pattern discovery using matrix factorization, PNAS 101(12):4164-9 (2004)
[3] 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)
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