This pipeline computes the correlation between cancer subtypes identified by different molecular patterns and selected clinical features.
Testing the association between subtypes identified by 3 different clustering approaches and 3 clinical features across 21 patients, 2 significant findings detected with P value < 0.05 and Q value < 0.25.
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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 sequencing-based mRNA expression data identified 2 subtypes that correlate to 'Time to Death'.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'Time to Death'.
Clinical Features |
Time to Death |
AGE | GENDER |
Statistical Tests | logrank test | t-test | Fisher's exact test |
METHLYATION CNMF |
0.585 (1.00) |
0.928 (1.00) |
0.827 (1.00) |
RNAseq CNMF subtypes |
0.0302 (0.241) |
0.0691 (0.468) |
0.0669 (0.468) |
RNAseq cHierClus subtypes |
0.00815 (0.0734) |
0.359 (1.00) |
0.336 (1.00) |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 3 | 8 | 10 |
P value = 0.585 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 21 | 4 | 2.0 - 211.2 (31.7) |
subtype1 | 3 | 0 | 22.3 - 127.3 (27.4) |
subtype2 | 8 | 1 | 24.7 - 196.6 (44.3) |
subtype3 | 10 | 3 | 2.0 - 211.2 (31.4) |
P value = 0.928 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 21 | 54.0 (12.4) |
subtype1 | 3 | 52.7 (9.5) |
subtype2 | 8 | 53.0 (11.4) |
subtype3 | 10 | 55.1 (14.8) |
P value = 0.827 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 13 | 8 |
subtype1 | 2 | 1 |
subtype2 | 4 | 4 |
subtype3 | 7 | 3 |
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 12 | 9 |
P value = 0.0302 (logrank test), Q value = 0.24
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 21 | 4 | 2.0 - 211.2 (31.7) |
subtype1 | 12 | 2 | 2.0 - 211.2 (46.8) |
subtype2 | 9 | 2 | 4.1 - 74.1 (24.7) |
P value = 0.0691 (t-test), Q value = 0.47
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 21 | 54.0 (12.4) |
subtype1 | 12 | 58.4 (10.0) |
subtype2 | 9 | 48.0 (13.3) |
P value = 0.0669 (Fisher's exact test), Q value = 0.47
nPatients | FEMALE | MALE |
---|---|---|
ALL | 13 | 8 |
subtype1 | 5 | 7 |
subtype2 | 8 | 1 |
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 6 | 15 |
P value = 0.00815 (logrank test), Q value = 0.073
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 21 | 4 | 2.0 - 211.2 (31.7) |
subtype1 | 6 | 2 | 4.1 - 74.1 (24.8) |
subtype2 | 15 | 2 | 2.0 - 211.2 (35.5) |
P value = 0.359 (t-test), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 21 | 54.0 (12.4) |
subtype1 | 6 | 49.7 (13.1) |
subtype2 | 15 | 55.7 (12.2) |
P value = 0.336 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 13 | 8 |
subtype1 | 5 | 1 |
subtype2 | 8 | 7 |
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Cluster data file = DLBC-TP.mergedcluster.txt
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Clinical data file = DLBC-TP.merged_data.txt
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Number of patients = 21
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Number of clustering approaches = 3
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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
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.