This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 7 different clustering approaches and 5 clinical features across 10 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.
-
2 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 2 subtypes that do not correlate to any clinical features.
-
Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
-
2 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE'.
-
3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
-
2 subtypes identified in current cancer cohort by 'MIRseq Mature CNMF subtypes'. These subtypes correlate to 'AGE'.
-
3 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 |
NEOPLASM DISEASESTAGE |
PATHOLOGY T STAGE |
GENDER |
Statistical Tests | logrank test | ANOVA | Chi-square test | NULL | Fisher's exact test |
METHLYATION CNMF |
100 (1.00) |
0.8 (1.00) |
1 (1.00) |
1 (1.00) |
|
RNAseq CNMF subtypes |
0.0746 (1.00) |
0.164 (1.00) |
0.0302 (0.725) |
0.486 (1.00) |
|
RNAseq cHierClus subtypes |
0.214 (1.00) |
0.456 (1.00) |
0.0838 (1.00) |
0.286 (1.00) |
|
MIRSEQ CNMF |
100 (1.00) |
0.00866 (0.242) |
0.0302 (0.725) |
0.486 (1.00) |
|
MIRSEQ CHIERARCHICAL |
0.802 (1.00) |
0.00987 (0.257) |
0.233 (1.00) |
0.571 (1.00) |
|
MIRseq Mature CNMF subtypes |
100 (1.00) |
0.00866 (0.242) |
0.0302 (0.725) |
0.486 (1.00) |
|
MIRseq Mature cHierClus subtypes |
0.802 (1.00) |
0.00987 (0.257) |
0.233 (1.00) |
0.571 (1.00) |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 2 | 1 | 4 | 3 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 7 | 2 | 10.2 - 121.2 (29.2) |
subtype3 | 4 | 1 | 10.2 - 57.5 (35.0) |
subtype4 | 3 | 1 | 18.1 - 121.2 (29.2) |
P value = 0.8 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 7 | 42.9 (15.5) |
subtype3 | 4 | 44.2 (18.8) |
subtype4 | 3 | 41.0 (13.5) |
P value = 1 (Fisher's exact test), Q value = 1
nPatients | STAGE I | STAGE IV |
---|---|---|
ALL | 3 | 3 |
subtype3 | 1 | 2 |
subtype4 | 2 | 1 |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 4 | 2 | 1 | 3 |
P value = 0.0746 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 7 | 3 | 11.3 - 121.2 (29.2) |
subtype1 | 4 | 3 | 11.3 - 37.1 (18.1) |
subtype4 | 3 | 0 | 29.2 - 121.2 (46.8) |
P value = 0.164 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 7 | 41.0 (16.4) |
subtype1 | 4 | 48.5 (16.1) |
subtype4 | 3 | 31.0 (12.3) |
P value = 0.0302 (Chi-square test), Q value = 0.72
nPatients | STAGE I | STAGE II | STAGE IV |
---|---|---|---|
ALL | 3 | 2 | 2 |
subtype1 | 0 | 2 | 2 |
subtype4 | 3 | 0 | 0 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 3 | 4 | 3 |
P value = 0.214 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 10 | 4 | 10.2 - 121.2 (26.3) |
subtype1 | 3 | 2 | 11.3 - 37.1 (18.1) |
subtype2 | 4 | 1 | 18.1 - 121.2 (38.0) |
subtype3 | 3 | 1 | 10.2 - 57.5 (23.3) |
P value = 0.456 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 10 | 44.2 (15.8) |
subtype1 | 3 | 47.3 (19.5) |
subtype2 | 4 | 36.2 (14.5) |
subtype3 | 3 | 51.7 (14.0) |
P value = 0.0838 (Chi-square test), Q value = 1
nPatients | STAGE I | STAGE II | STAGE IV |
---|---|---|---|
ALL | 3 | 2 | 4 |
subtype1 | 0 | 2 | 1 |
subtype2 | 3 | 0 | 1 |
subtype3 | 0 | 0 | 2 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 4 | 2 | 4 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 8 | 2 | 10.2 - 121.2 (33.2) |
subtype1 | 4 | 1 | 10.2 - 37.1 (17.3) |
subtype3 | 4 | 1 | 29.2 - 121.2 (52.2) |
P value = 0.00866 (t-test), Q value = 0.24
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 8 | 45.6 (16.0) |
subtype1 | 4 | 58.8 (5.3) |
subtype3 | 4 | 32.5 (10.5) |
P value = 0.0302 (Chi-square test), Q value = 0.72
nPatients | STAGE I | STAGE II | STAGE IV |
---|---|---|---|
ALL | 3 | 2 | 2 |
subtype1 | 0 | 2 | 2 |
subtype3 | 3 | 0 | 0 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 3 | 3 | 4 |
P value = 0.802 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 10 | 4 | 10.2 - 121.2 (26.3) |
subtype1 | 3 | 2 | 18.1 - 121.2 (18.1) |
subtype2 | 3 | 1 | 29.2 - 57.5 (46.8) |
subtype3 | 4 | 1 | 10.2 - 37.1 (17.3) |
P value = 0.00987 (ANOVA), Q value = 0.26
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 10 | 44.2 (15.8) |
subtype1 | 3 | 40.7 (14.0) |
subtype2 | 3 | 28.3 (7.8) |
subtype3 | 4 | 58.8 (5.3) |
P value = 0.233 (Chi-square test), Q value = 1
nPatients | STAGE I | STAGE II | STAGE IV |
---|---|---|---|
ALL | 3 | 2 | 4 |
subtype1 | 1 | 0 | 2 |
subtype2 | 2 | 0 | 0 |
subtype3 | 0 | 2 | 2 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 4 | 2 | 4 |
P value = 100 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 8 | 2 | 10.2 - 121.2 (33.2) |
subtype1 | 4 | 1 | 10.2 - 37.1 (17.3) |
subtype3 | 4 | 1 | 29.2 - 121.2 (52.2) |
P value = 0.00866 (t-test), Q value = 0.24
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 8 | 45.6 (16.0) |
subtype1 | 4 | 58.8 (5.3) |
subtype3 | 4 | 32.5 (10.5) |
P value = 0.0302 (Chi-square test), Q value = 0.72
nPatients | STAGE I | STAGE II | STAGE IV |
---|---|---|---|
ALL | 3 | 2 | 2 |
subtype1 | 0 | 2 | 2 |
subtype3 | 3 | 0 | 0 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 4 | 3 | 3 |
P value = 0.802 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 10 | 4 | 10.2 - 121.2 (26.3) |
subtype1 | 4 | 1 | 10.2 - 37.1 (17.3) |
subtype2 | 3 | 1 | 29.2 - 57.5 (46.8) |
subtype3 | 3 | 2 | 18.1 - 121.2 (18.1) |
P value = 0.00987 (ANOVA), Q value = 0.26
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 10 | 44.2 (15.8) |
subtype1 | 4 | 58.8 (5.3) |
subtype2 | 3 | 28.3 (7.8) |
subtype3 | 3 | 40.7 (14.0) |
P value = 0.233 (Chi-square test), Q value = 1
nPatients | STAGE I | STAGE II | STAGE IV |
---|---|---|---|
ALL | 3 | 2 | 4 |
subtype1 | 0 | 2 | 2 |
subtype2 | 2 | 0 | 0 |
subtype3 | 1 | 0 | 2 |
-
Cluster data file = ACC-TP.mergedcluster.txt
-
Clinical data file = ACC-TP.merged_data.txt
-
Number of patients = 10
-
Number of clustering approaches = 7
-
Number of selected clinical features = 5
-
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, 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 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 multi-class clinical features (nominal or ordinal), Chi-square tests (Greenwood and Nikulin 1996) were used to estimate the P values using the 'chisq.test' 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 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.