This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 6 different clustering approaches and 3 clinical features across 26 patients, one 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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3 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death'.
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2 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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2 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 | GENDER |
Statistical Tests | logrank test | t-test | Fisher's exact test |
Copy Number Ratio CNMF subtypes |
0.191 (1.00) |
0.722 (1.00) |
0.0835 (1.00) |
METHLYATION CNMF |
0.0116 (0.209) |
0.32 (1.00) |
0.866 (1.00) |
MIRSEQ CNMF |
0.824 (1.00) |
0.416 (1.00) |
0.524 (1.00) |
MIRSEQ CHIERARCHICAL |
0.824 (1.00) |
0.478 (1.00) |
0.524 (1.00) |
MIRseq Mature CNMF subtypes |
0.824 (1.00) |
0.416 (1.00) |
0.524 (1.00) |
MIRseq Mature cHierClus subtypes |
0.824 (1.00) |
0.478 (1.00) |
0.524 (1.00) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 8 | 5 | 9 |
P value = 0.191 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 22 | 6 | 0.1 - 53.3 (11.3) |
subtype1 | 8 | 2 | 0.1 - 46.7 (9.4) |
subtype2 | 5 | 3 | 2.0 - 24.5 (9.7) |
subtype3 | 9 | 1 | 0.1 - 53.3 (13.6) |
P value = 0.722 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 22 | 64.6 (7.7) |
subtype1 | 8 | 66.0 (7.7) |
subtype2 | 5 | 65.4 (6.5) |
subtype3 | 9 | 63.0 (8.8) |
P value = 0.0835 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 10 | 12 |
subtype1 | 5 | 3 |
subtype2 | 0 | 5 |
subtype3 | 5 | 4 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 10 | 13 | 3 |
P value = 0.0116 (logrank test), Q value = 0.21
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 26 | 8 | 0.1 - 76.4 (13.2) |
subtype1 | 10 | 2 | 5.2 - 76.4 (30.7) |
subtype2 | 13 | 6 | 0.1 - 74.7 (8.6) |
subtype3 | 3 | 0 | 0.1 - 4.1 (0.1) |
P value = 0.32 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 26 | 63.8 (9.4) |
subtype1 | 10 | 64.3 (12.6) |
subtype2 | 13 | 65.2 (6.8) |
subtype3 | 3 | 56.0 (2.0) |
P value = 0.866 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 12 | 14 |
subtype1 | 4 | 6 |
subtype2 | 6 | 7 |
subtype3 | 2 | 1 |
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 6 | 4 |
P value = 0.824 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 10 | 3 | 0.1 - 53.3 (9.1) |
subtype1 | 6 | 2 | 0.5 - 53.3 (9.1) |
subtype2 | 4 | 1 | 0.1 - 26.9 (6.8) |
P value = 0.416 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 10 | 64.0 (8.8) |
subtype1 | 6 | 61.8 (7.6) |
subtype2 | 4 | 67.2 (10.6) |
P value = 0.524 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 5 | 5 |
subtype1 | 2 | 4 |
subtype2 | 3 | 1 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 1 | 3 | 6 |
P value = 0.824 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 9 | 3 | 0.1 - 53.3 (9.7) |
subtype2 | 3 | 1 | 0.1 - 26.9 (13.6) |
subtype3 | 6 | 2 | 0.5 - 53.3 (9.1) |
P value = 0.478 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 9 | 64.0 (9.3) |
subtype2 | 3 | 68.3 (12.7) |
subtype3 | 6 | 61.8 (7.6) |
P value = 0.524 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 4 | 5 |
subtype2 | 2 | 1 |
subtype3 | 2 | 4 |
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 6 | 4 |
P value = 0.824 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 10 | 3 | 0.1 - 53.3 (9.1) |
subtype1 | 6 | 2 | 0.5 - 53.3 (9.1) |
subtype2 | 4 | 1 | 0.1 - 26.9 (6.8) |
P value = 0.416 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 10 | 64.0 (8.8) |
subtype1 | 6 | 61.8 (7.6) |
subtype2 | 4 | 67.2 (10.6) |
P value = 0.524 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 5 | 5 |
subtype1 | 2 | 4 |
subtype2 | 3 | 1 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 1 | 3 | 6 |
P value = 0.824 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 9 | 3 | 0.1 - 53.3 (9.7) |
subtype2 | 3 | 1 | 0.1 - 26.9 (13.6) |
subtype3 | 6 | 2 | 0.5 - 53.3 (9.1) |
P value = 0.478 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 9 | 64.0 (9.3) |
subtype2 | 3 | 68.3 (12.7) |
subtype3 | 6 | 61.8 (7.6) |
P value = 0.524 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 4 | 5 |
subtype2 | 2 | 1 |
subtype3 | 2 | 4 |
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Cluster data file = SARC-TP.mergedcluster.txt
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Clinical data file = SARC-TP.clin.merged.picked.txt
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Number of patients = 26
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Number of clustering approaches = 6
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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
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
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.