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 82 patients, 2 significant findings 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 correlate to 'GENDER'.
-
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 3 subtypes that correlate to 'GENDER'.
-
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that do not correlate to any clinical features.
-
4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes do not correlate to any clinical features.
-
2 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
-
3 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes do not correlate to any clinical features.
-
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 |
0.354 (1.00) |
0.366 (1.00) |
0.00039 (0.00937) |
METHLYATION CNMF |
0.376 (1.00) |
0.0844 (1.00) |
0.328 (1.00) |
RNAseq CNMF subtypes |
0.812 (1.00) |
0.711 (1.00) |
0.0014 (0.0321) |
RNAseq cHierClus subtypes |
0.369 (1.00) |
0.0302 (0.665) |
0.0922 (1.00) |
MIRSEQ CNMF |
0.0512 (1.00) |
0.145 (1.00) |
0.187 (1.00) |
MIRSEQ CHIERARCHICAL |
0.833 (1.00) |
0.229 (1.00) |
0.213 (1.00) |
MIRseq Mature CNMF subtypes |
0.533 (1.00) |
0.163 (1.00) |
0.0422 (0.887) |
MIRseq Mature cHierClus subtypes |
0.99 (1.00) |
0.35 (1.00) |
0.0563 (1.00) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 29 | 30 | 23 |
P value = 0.354 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 82 | 24 | 0.1 - 143.4 (18.1) |
subtype1 | 29 | 10 | 0.5 - 108.1 (15.0) |
subtype2 | 30 | 9 | 0.1 - 74.7 (22.0) |
subtype3 | 23 | 5 | 0.1 - 143.4 (25.1) |
P value = 0.366 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 82 | 62.1 (12.8) |
subtype1 | 29 | 63.1 (13.3) |
subtype2 | 30 | 63.6 (11.5) |
subtype3 | 23 | 58.9 (13.8) |
P value = 0.00039 (Fisher's exact test), Q value = 0.0094
nPatients | FEMALE | MALE |
---|---|---|
ALL | 40 | 42 |
subtype1 | 8 | 21 |
subtype2 | 23 | 7 |
subtype3 | 9 | 14 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 24 | 25 | 33 |
P value = 0.376 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 82 | 24 | 0.1 - 143.4 (18.1) |
subtype1 | 24 | 8 | 1.7 - 76.4 (25.7) |
subtype2 | 25 | 8 | 0.1 - 143.4 (10.7) |
subtype3 | 33 | 8 | 0.1 - 116.9 (25.1) |
P value = 0.0844 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 82 | 62.1 (12.8) |
subtype1 | 24 | 63.9 (14.7) |
subtype2 | 25 | 65.4 (10.5) |
subtype3 | 33 | 58.4 (12.4) |
P value = 0.328 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 40 | 42 |
subtype1 | 9 | 15 |
subtype2 | 12 | 13 |
subtype3 | 19 | 14 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 30 | 21 | 21 |
P value = 0.812 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 72 | 23 | 0.1 - 143.4 (19.3) |
subtype1 | 30 | 11 | 1.7 - 108.1 (18.1) |
subtype2 | 21 | 6 | 0.1 - 143.4 (12.8) |
subtype3 | 21 | 6 | 0.1 - 75.5 (26.5) |
P value = 0.711 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 72 | 61.5 (12.5) |
subtype1 | 30 | 62.6 (13.7) |
subtype2 | 21 | 61.8 (11.2) |
subtype3 | 21 | 59.6 (12.4) |
P value = 0.0014 (Fisher's exact test), Q value = 0.032
nPatients | FEMALE | MALE |
---|---|---|
ALL | 37 | 35 |
subtype1 | 9 | 21 |
subtype2 | 17 | 4 |
subtype3 | 11 | 10 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 29 | 41 | 2 |
P value = 0.369 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 70 | 22 | 0.1 - 143.4 (19.3) |
subtype1 | 29 | 8 | 0.1 - 116.9 (25.2) |
subtype2 | 41 | 14 | 0.1 - 143.4 (15.0) |
P value = 0.0302 (t-test), Q value = 0.66
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 70 | 61.8 (12.5) |
subtype1 | 29 | 58.0 (12.3) |
subtype2 | 41 | 64.6 (12.1) |
P value = 0.0922 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 37 | 33 |
subtype1 | 19 | 10 |
subtype2 | 18 | 23 |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 6 | 31 | 12 | 19 |
P value = 0.0512 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 68 | 21 | 0.1 - 143.4 (18.9) |
subtype1 | 6 | 3 | 0.1 - 14.0 (8.0) |
subtype2 | 31 | 10 | 0.5 - 143.4 (19.3) |
subtype3 | 12 | 4 | 4.5 - 70.5 (23.6) |
subtype4 | 19 | 4 | 0.1 - 116.9 (24.3) |
P value = 0.145 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 68 | 62.8 (12.2) |
subtype1 | 6 | 71.5 (13.6) |
subtype2 | 31 | 63.7 (10.9) |
subtype3 | 12 | 62.2 (11.0) |
subtype4 | 19 | 58.7 (13.7) |
P value = 0.187 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 35 | 33 |
subtype1 | 4 | 2 |
subtype2 | 14 | 17 |
subtype3 | 4 | 8 |
subtype4 | 13 | 6 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 2 | 28 | 38 |
P value = 0.833 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 66 | 21 | 0.1 - 143.4 (19.7) |
subtype2 | 28 | 9 | 0.1 - 116.9 (18.2) |
subtype3 | 38 | 12 | 0.1 - 143.4 (19.7) |
P value = 0.229 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 66 | 62.8 (12.4) |
subtype2 | 28 | 60.5 (14.0) |
subtype3 | 38 | 64.4 (10.9) |
P value = 0.213 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 33 | 33 |
subtype2 | 17 | 11 |
subtype3 | 16 | 22 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 13 | 31 | 24 |
P value = 0.533 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 68 | 21 | 0.1 - 143.4 (18.9) |
subtype1 | 13 | 5 | 3.2 - 70.5 (14.0) |
subtype2 | 31 | 10 | 0.1 - 143.4 (19.3) |
subtype3 | 24 | 6 | 0.1 - 116.9 (23.4) |
P value = 0.163 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 68 | 62.8 (12.2) |
subtype1 | 13 | 66.1 (13.4) |
subtype2 | 31 | 64.2 (11.0) |
subtype3 | 24 | 59.0 (12.5) |
P value = 0.0422 (Fisher's exact test), Q value = 0.89
nPatients | FEMALE | MALE |
---|---|---|
ALL | 35 | 33 |
subtype1 | 4 | 9 |
subtype2 | 14 | 17 |
subtype3 | 17 | 7 |
Cluster Labels | 2 | 3 |
---|---|---|
Number of samples | 37 | 31 |
P value = 0.99 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 68 | 21 | 0.1 - 143.4 (18.9) |
subtype2 | 37 | 12 | 0.1 - 143.4 (19.3) |
subtype3 | 31 | 9 | 0.1 - 116.9 (15.0) |
P value = 0.35 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 68 | 62.8 (12.2) |
subtype2 | 37 | 64.1 (10.9) |
subtype3 | 31 | 61.2 (13.7) |
P value = 0.0563 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 35 | 33 |
subtype2 | 15 | 22 |
subtype3 | 20 | 11 |
-
Cluster data file = SARC-TP.mergedcluster.txt
-
Clinical data file = SARC-TP.merged_data.txt
-
Number of patients = 82
-
Number of clustering approaches = 8
-
Number of selected clinical features = 3
-
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
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.