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 3 clinical features across 138 patients, no significant finding detected with P value < 0.05.
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4 subtypes identified in current cancer cohort by 'CN CNMF'. These subtypes do not correlate to any clinical features.
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3 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 RPPA data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on RPPA data identified 3 subtypes that do not correlate to any clinical features.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 4 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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CNMF clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based miR expression data identified 3 subtypes that do not correlate to any clinical features.
Clinical Features |
Time to Death |
AGE | GENDER |
Statistical Tests | logrank test | ANOVA | Fisher's exact test |
CN CNMF | 0.193 | 0.162 | 0.134 |
METHLYATION CNMF | 0.247 | 0.291 | 0.21 |
RPPA CNMF subtypes | 0.0844 | 0.45 | 0.745 |
RPPA cHierClus subtypes | 0.165 | 0.585 | 0.313 |
RNAseq CNMF subtypes | 0.455 | 0.165 | 0.293 |
RNAseq cHierClus subtypes | 0.503 | 0.564 | 0.206 |
MIRseq CNMF subtypes | 0.167 | 0.526 | 0.945 |
MIRseq cHierClus subtypes | 0.853 | 0.704 | 0.753 |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 26 | 44 | 43 | 25 |
P value = 0.193 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 18 | 11 | 0.2 - 131.1 (41.8) |
subtype1 | 5 | 4 | 5.6 - 131.1 (15.3) |
subtype2 | 5 | 3 | 12.6 - 62.8 (39.6) |
subtype3 | 4 | 3 | 0.2 - 117.9 (48.4) |
subtype4 | 4 | 1 | 27.0 - 84.7 (80.2) |
P value = 0.162 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 56.8 (16.0) |
subtype1 | 5 | 44.2 (17.1) |
subtype2 | 6 | 64.3 (16.9) |
subtype3 | 4 | 55.0 (11.5) |
subtype4 | 4 | 63.0 (10.4) |
P value = 0.134 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 92 |
subtype1 | 4 | 22 |
subtype2 | 16 | 28 |
subtype3 | 18 | 25 |
subtype4 | 8 | 17 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 29 | 56 | 53 |
P value = 0.247 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 18 | 11 | 0.2 - 131.1 (41.8) |
subtype1 | 6 | 3 | 0.2 - 131.1 (52.0) |
subtype2 | 4 | 3 | 26.4 - 120.5 (90.3) |
subtype3 | 8 | 5 | 5.6 - 84.7 (29.7) |
P value = 0.291 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 19 | 56.8 (16.0) |
subtype1 | 6 | 52.3 (14.9) |
subtype2 | 4 | 68.0 (17.6) |
subtype3 | 9 | 54.8 (15.5) |
P value = 0.21 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 46 | 92 |
subtype1 | 11 | 18 |
subtype2 | 22 | 34 |
subtype3 | 13 | 40 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 35 | 40 | 24 |
P value = 0.0844 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 11 | 5.6 - 131.1 (41.8) |
subtype1 | 7 | 5 | 10.1 - 131.1 (39.6) |
subtype2 | 3 | 1 | 62.8 - 120.5 (80.0) |
subtype3 | 6 | 5 | 5.6 - 84.7 (23.9) |
P value = 0.45 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 56.2 (16.9) |
subtype1 | 7 | 59.7 (18.0) |
subtype2 | 4 | 61.0 (15.4) |
subtype3 | 6 | 49.0 (16.8) |
P value = 0.745 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 35 | 64 |
subtype1 | 11 | 24 |
subtype2 | 16 | 24 |
subtype3 | 8 | 16 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 23 | 36 | 40 |
P value = 0.165 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 11 | 5.6 - 131.1 (41.8) |
subtype1 | 5 | 4 | 5.6 - 84.7 (32.5) |
subtype2 | 3 | 1 | 62.8 - 120.5 (80.0) |
subtype3 | 8 | 6 | 10.1 - 131.1 (33.3) |
P value = 0.585 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 56.2 (16.9) |
subtype1 | 6 | 50.7 (16.9) |
subtype2 | 3 | 62.7 (18.4) |
subtype3 | 8 | 58.0 (17.3) |
P value = 0.313 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 35 | 64 |
subtype1 | 5 | 18 |
subtype2 | 14 | 22 |
subtype3 | 16 | 24 |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 22 | 37 | 35 | 40 |
P value = 0.455 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 10 | 0.2 - 131.1 (41.8) |
subtype1 | 3 | 1 | 0.2 - 44.0 (15.3) |
subtype2 | 5 | 4 | 26.4 - 117.9 (62.8) |
subtype3 | 4 | 3 | 12.6 - 131.1 (76.5) |
subtype4 | 4 | 2 | 10.1 - 84.7 (53.5) |
P value = 0.165 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 55.6 (16.5) |
subtype1 | 3 | 43.3 (17.8) |
subtype2 | 5 | 67.0 (18.7) |
subtype3 | 4 | 47.8 (11.5) |
subtype4 | 5 | 57.8 (11.8) |
P value = 0.293 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 45 | 89 |
subtype1 | 4 | 18 |
subtype2 | 13 | 24 |
subtype3 | 15 | 20 |
subtype4 | 13 | 27 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 31 | 66 | 37 |
P value = 0.503 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 10 | 0.2 - 131.1 (41.8) |
subtype1 | 4 | 3 | 12.6 - 131.1 (76.5) |
subtype2 | 7 | 4 | 10.1 - 84.7 (44.0) |
subtype3 | 5 | 3 | 0.2 - 117.9 (39.6) |
P value = 0.564 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 17 | 55.6 (16.5) |
subtype1 | 4 | 47.8 (11.5) |
subtype2 | 8 | 57.0 (15.3) |
subtype3 | 5 | 59.6 (22.2) |
P value = 0.206 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 45 | 89 |
subtype1 | 14 | 17 |
subtype2 | 22 | 44 |
subtype3 | 9 | 28 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 57 | 45 | 31 |
P value = 0.167 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 10 | 0.2 - 131.1 (53.4) |
subtype1 | 3 | 3 | 62.8 - 117.9 (64.4) |
subtype2 | 8 | 6 | 10.1 - 131.1 (29.7) |
subtype3 | 5 | 1 | 0.2 - 120.5 (80.0) |
P value = 0.526 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 16 | 55.1 (16.6) |
subtype1 | 3 | 60.3 (22.5) |
subtype2 | 8 | 50.1 (17.1) |
subtype3 | 5 | 59.8 (13.3) |
P value = 0.945 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 44 | 89 |
subtype1 | 19 | 38 |
subtype2 | 14 | 31 |
subtype3 | 11 | 20 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 15 | 30 | 88 |
P value = 0.853 (logrank test)
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 16 | 10 | 0.2 - 131.1 (53.4) |
subtype1 | 2 | 0 | 0.2 - 84.7 (42.5) |
subtype2 | 7 | 5 | 10.1 - 131.1 (32.5) |
subtype3 | 7 | 5 | 15.3 - 120.5 (64.4) |
P value = 0.704 (ANOVA)
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 16 | 55.1 (16.6) |
subtype1 | 2 | 56.0 (4.2) |
subtype2 | 7 | 53.0 (14.0) |
subtype3 | 7 | 56.9 (22.0) |
P value = 0.753 (Fisher's exact test)
nPatients | FEMALE | MALE |
---|---|---|
ALL | 44 | 89 |
subtype1 | 6 | 9 |
subtype2 | 9 | 21 |
subtype3 | 29 | 59 |
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Cluster data file = SKCM.mergedcluster.txt
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Clinical data file = SKCM.clin.merged.picked.txt
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Number of patients = 138
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Number of clustering approaches = 8
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Number of selected clinical features = 3
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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 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
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.