This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between 'METHLYATION CNMF' and 3 clinical features across 16 patients, no significant finding 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.
Table 1. Get Full Table Overview of the association between subtypes identified by 1 different clustering approaches and 3 clinical features. Shown in the table are P values (Q values). Thresholded by P value < 0.05 and Q value < 0.25, no significant finding detected.
|
Clinical Features |
Statistical Tests |
METHLYATION CNMF |
| Time to Death | logrank test |
0.366 (1.00) |
| AGE | t-test |
0.93 (1.00) |
| GENDER | Fisher's exact test |
0.615 (1.00) |
Table S1. Description of clustering approach #1: 'METHLYATION CNMF'
| Cluster Labels | 1 | 2 |
|---|---|---|
| Number of samples | 7 | 9 |
P value = 0.366 (logrank test), Q value = 1
Table S2. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
| nPatients | nDeath | Duration Range (Median), Month | |
|---|---|---|---|
| ALL | 16 | 4 | 4.1 - 211.2 (38.3) |
| subtype1 | 7 | 1 | 22.3 - 196.6 (52.0) |
| subtype2 | 9 | 3 | 4.1 - 211.2 (31.1) |
Figure S1. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #1: 'Time to Death'
P value = 0.93 (t-test), Q value = 1
Table S3. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
| nPatients | Mean (Std.Dev) | |
|---|---|---|
| ALL | 16 | 54.4 (13.3) |
| subtype1 | 7 | 54.7 (12.1) |
| subtype2 | 9 | 54.1 (15.0) |
Figure S2. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #2: 'AGE'
P value = 0.615 (Fisher's exact test), Q value = 1
Table S4. Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
| nPatients | FEMALE | MALE |
|---|---|---|
| ALL | 9 | 7 |
| subtype1 | 3 | 4 |
| subtype2 | 6 | 3 |
Figure S3. Get High-res Image Clustering Approach #1: 'METHLYATION CNMF' versus Clinical Feature #3: 'GENDER'
-
Cluster data file = DLBC-TP.mergedcluster.txt
-
Clinical data file = DLBC-TP.clin.merged.picked.txt
-
Number of patients = 16
-
Number of clustering approaches = 1: 'METHLYATION CNMF'
-
Number of selected clinical features = 3
-
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, 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 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.