This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 8 different clustering approaches and 2 clinical features across 15 patients, no significant finding detected with P value < 0.05 and Q value < 0.25.
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3 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes do not correlate to any clinical features.
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4 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
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2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
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3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
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2 subtypes identified in current cancer cohort by 'MIRseq Mature cHierClus subtypes'. These subtypes do not correlate to any clinical features.
Clinical Features |
Time to Death |
AGE |
Statistical Tests | logrank test | t-test |
Copy Number Ratio CNMF subtypes |
0.363 (1.00) |
0.413 (1.00) |
METHLYATION CNMF |
0.519 (1.00) |
0.23 (1.00) |
RNAseq CNMF subtypes |
0.7 (1.00) |
0.916 (1.00) |
RNAseq cHierClus subtypes |
0.616 (1.00) |
0.141 (1.00) |
MIRSEQ CNMF |
0.378 (1.00) |
0.882 (1.00) |
MIRSEQ CHIERARCHICAL |
0.226 (1.00) |
0.483 (1.00) |
MIRseq Mature CNMF subtypes |
0.378 (1.00) |
0.882 (1.00) |
MIRseq Mature cHierClus subtypes |
0.151 (1.00) |
0.82 (1.00) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 4 | 5 | 1 | 4 |
P value = 0.363 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 13 | 5 | 4.8 - 69.6 (13.9) |
subtype1 | 4 | 1 | 9.7 - 69.6 (18.7) |
subtype2 | 5 | 3 | 7.2 - 15.2 (13.7) |
subtype4 | 4 | 1 | 4.8 - 59.7 (9.9) |
P value = 0.413 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 13 | 75.0 (8.8) |
subtype1 | 4 | 75.5 (10.1) |
subtype2 | 5 | 78.4 (8.6) |
subtype4 | 4 | 70.2 (7.4) |
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 3 | 3 | 3 | 1 | 5 |
P value = 0.519 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 14 | 6 | 4.8 - 102.4 (14.3) |
subtype1 | 3 | 1 | 4.8 - 102.4 (13.9) |
subtype2 | 3 | 1 | 5.5 - 69.6 (59.7) |
subtype3 | 3 | 1 | 7.2 - 15.2 (14.6) |
subtype5 | 5 | 3 | 9.7 - 23.5 (13.7) |
P value = 0.23 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 14 | 74.8 (8.5) |
subtype1 | 3 | 69.7 (5.7) |
subtype2 | 3 | 69.3 (7.5) |
subtype3 | 3 | 81.0 (8.2) |
subtype5 | 5 | 77.4 (8.8) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 6 | 4 | 5 |
P value = 0.7 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 15 | 6 | 4.8 - 102.4 (14.4) |
subtype1 | 6 | 3 | 4.8 - 102.4 (8.8) |
subtype2 | 4 | 1 | 14.4 - 16.3 (14.9) |
subtype3 | 5 | 2 | 9.7 - 69.6 (23.5) |
P value = 0.916 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 15 | 74.2 (8.5) |
subtype1 | 6 | 73.5 (8.6) |
subtype2 | 4 | 73.5 (7.0) |
subtype3 | 5 | 75.6 (10.9) |
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 1 | 1 | 5 | 3 | 5 |
P value = 0.616 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 13 | 6 | 4.8 - 102.4 (13.9) |
subtype3 | 5 | 3 | 4.8 - 102.4 (10.4) |
subtype4 | 3 | 2 | 9.7 - 23.5 (13.7) |
subtype5 | 5 | 1 | 7.2 - 16.3 (14.6) |
P value = 0.141 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 13 | 75.5 (8.2) |
subtype3 | 5 | 70.6 (5.5) |
subtype4 | 3 | 82.3 (7.5) |
subtype5 | 5 | 76.4 (8.9) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 4 | 6 | 1 | 4 |
P value = 0.378 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 14 | 6 | 4.8 - 102.4 (14.2) |
subtype1 | 4 | 2 | 4.8 - 102.4 (6.3) |
subtype2 | 6 | 4 | 10.4 - 59.7 (14.3) |
subtype4 | 4 | 0 | 9.7 - 16.3 (14.8) |
P value = 0.882 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 14 | 74.6 (8.7) |
subtype1 | 4 | 76.5 (9.4) |
subtype2 | 6 | 73.5 (10.5) |
subtype4 | 4 | 74.2 (7.0) |
Cluster Labels | 2 | 3 |
---|---|---|
Number of samples | 8 | 7 |
P value = 0.226 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 15 | 6 | 4.8 - 102.4 (14.4) |
subtype2 | 8 | 2 | 4.8 - 102.4 (12.4) |
subtype3 | 7 | 4 | 10.4 - 59.7 (14.4) |
P value = 0.483 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 15 | 74.2 (8.5) |
subtype2 | 8 | 75.8 (7.3) |
subtype3 | 7 | 72.4 (10.0) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 4 | 6 | 1 | 4 |
P value = 0.378 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 14 | 6 | 4.8 - 102.4 (14.2) |
subtype1 | 4 | 2 | 4.8 - 102.4 (6.3) |
subtype2 | 6 | 4 | 10.4 - 59.7 (14.3) |
subtype4 | 4 | 0 | 9.7 - 16.3 (14.8) |
P value = 0.882 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 14 | 74.6 (8.7) |
subtype1 | 4 | 76.5 (9.4) |
subtype2 | 6 | 73.5 (10.5) |
subtype4 | 4 | 74.2 (7.0) |
Cluster Labels | 2 | 3 |
---|---|---|
Number of samples | 6 | 9 |
P value = 0.151 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 15 | 6 | 4.8 - 102.4 (14.4) |
subtype2 | 6 | 4 | 10.4 - 59.7 (14.3) |
subtype3 | 9 | 2 | 4.8 - 102.4 (14.4) |
P value = 0.82 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 15 | 74.2 (8.5) |
subtype2 | 6 | 73.5 (10.5) |
subtype3 | 9 | 74.7 (7.6) |
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Cluster data file = UCS-TP.mergedcluster.txt
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Clinical data file = UCS-TP.clin.merged.picked.txt
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Number of patients = 15
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Number of clustering approaches = 8
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Number of selected clinical features = 2
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Exclude small clusters that include fewer than K patients, K = 3
consensus non-negative matrix factorization clustering approach (Brunet et al. 2004)
Resampling-based clustering method (Monti et al. 2003)
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
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
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
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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.