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 200 patients, 6 significant findings detected with P value < 0.05 and Q value < 0.25.
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4 subtypes identified in current cancer cohort by 'Copy Number Ratio CNMF subtypes'. These subtypes correlate to 'Time to Death' and 'AGE'.
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5 subtypes identified in current cancer cohort by 'METHLYATION CNMF'. These subtypes correlate to 'Time to Death' and 'AGE'.
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CNMF clustering analysis on sequencing-based mRNA expression data identified 3 subtypes that do not correlate to any clinical features.
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Consensus hierarchical clustering analysis on sequencing-based mRNA expression data identified 2 subtypes that correlate to 'AGE'.
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4 subtypes identified in current cancer cohort by 'MIRSEQ CNMF'. These subtypes correlate to 'AGE'.
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3 subtypes identified in current cancer cohort by 'MIRSEQ CHIERARCHICAL'. These subtypes do not correlate to any clinical features.
Clinical Features |
Time to Death |
AGE | GENDER |
Statistical Tests | logrank test | ANOVA | Fisher's exact test |
Copy Number Ratio CNMF subtypes |
0.00908 (0.127) |
0.00934 (0.127) |
0.427 (1.00) |
METHLYATION CNMF |
8.16e-05 (0.00139) |
1.01e-10 (1.81e-09) |
0.319 (1.00) |
RNAseq CNMF subtypes |
0.568 (1.00) |
0.208 (1.00) |
0.0989 (0.989) |
RNAseq cHierClus subtypes |
0.43 (1.00) |
0.0076 (0.122) |
1 (1.00) |
MIRSEQ CNMF |
0.0754 (0.829) |
0.00806 (0.122) |
0.922 (1.00) |
MIRSEQ CHIERARCHICAL |
0.0492 (0.59) |
0.172 (1.00) |
0.78 (1.00) |
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 142 | 15 | 15 | 18 | 1 |
P value = 0.00908 (logrank test), Q value = 0.13
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 165 | 103 | 0.9 - 94.1 (12.0) |
subtype1 | 124 | 71 | 0.9 - 94.1 (14.0) |
subtype2 | 11 | 8 | 0.9 - 42.0 (10.0) |
subtype3 | 12 | 9 | 1.0 - 24.0 (7.5) |
subtype4 | 18 | 15 | 1.0 - 73.0 (12.0) |
P value = 0.00934 (ANOVA), Q value = 0.13
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 190 | 55.2 (16.1) |
subtype1 | 142 | 53.0 (16.0) |
subtype2 | 15 | 63.1 (12.9) |
subtype3 | 15 | 63.9 (16.0) |
subtype4 | 18 | 58.2 (15.5) |
P value = 0.427 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 87 | 103 |
subtype1 | 69 | 73 |
subtype2 | 6 | 9 |
subtype3 | 4 | 11 |
subtype4 | 8 | 10 |
Cluster Labels | 1 | 2 | 3 | 4 | 5 |
---|---|---|---|---|---|
Number of samples | 48 | 45 | 65 | 14 | 22 |
P value = 8.16e-05 (logrank test), Q value = 0.0014
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 167 | 104 | 0.9 - 94.1 (12.0) |
subtype1 | 42 | 30 | 1.0 - 69.0 (8.1) |
subtype2 | 41 | 12 | 0.9 - 94.1 (22.1) |
subtype3 | 54 | 42 | 0.9 - 56.1 (12.0) |
subtype4 | 13 | 7 | 1.0 - 42.0 (13.0) |
subtype5 | 17 | 13 | 4.0 - 73.0 (12.0) |
P value = 1.01e-10 (ANOVA), Q value = 1.8e-09
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 194 | 55.1 (16.0) |
subtype1 | 48 | 55.9 (14.1) |
subtype2 | 45 | 45.6 (15.5) |
subtype3 | 65 | 63.1 (12.9) |
subtype4 | 14 | 63.4 (11.2) |
subtype5 | 22 | 43.9 (16.0) |
P value = 0.319 (Chi-square test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 89 | 105 |
subtype1 | 24 | 24 |
subtype2 | 24 | 21 |
subtype3 | 26 | 39 |
subtype4 | 8 | 6 |
subtype5 | 7 | 15 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 74 | 54 | 45 |
P value = 0.568 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 149 | 92 | 0.9 - 94.1 (12.0) |
subtype1 | 65 | 39 | 1.0 - 94.1 (16.1) |
subtype2 | 47 | 30 | 0.9 - 75.1 (10.0) |
subtype3 | 37 | 23 | 0.9 - 62.0 (12.0) |
P value = 0.208 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 173 | 55.3 (16.1) |
subtype1 | 74 | 54.4 (17.3) |
subtype2 | 54 | 58.4 (13.9) |
subtype3 | 45 | 52.9 (16.4) |
P value = 0.0989 (Fisher's exact test), Q value = 0.99
nPatients | FEMALE | MALE |
---|---|---|
ALL | 80 | 93 |
subtype1 | 31 | 43 |
subtype2 | 22 | 32 |
subtype3 | 27 | 18 |
Cluster Labels | 1 | 2 |
---|---|---|
Number of samples | 59 | 114 |
P value = 0.43 (logrank test), Q value = 1
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 149 | 92 | 0.9 - 94.1 (12.0) |
subtype1 | 50 | 33 | 0.9 - 75.1 (11.5) |
subtype2 | 99 | 59 | 0.9 - 94.1 (12.9) |
P value = 0.0076 (t-test), Q value = 0.12
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 173 | 55.3 (16.1) |
subtype1 | 59 | 59.4 (13.0) |
subtype2 | 114 | 53.1 (17.2) |
P value = 1 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 80 | 93 |
subtype1 | 27 | 32 |
subtype2 | 53 | 61 |
Cluster Labels | 1 | 2 | 3 | 4 |
---|---|---|---|---|
Number of samples | 60 | 38 | 43 | 47 |
P value = 0.0754 (logrank test), Q value = 0.83
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 162 | 100 | 0.9 - 94.1 (12.0) |
subtype1 | 53 | 38 | 1.0 - 69.0 (9.0) |
subtype2 | 34 | 18 | 0.9 - 62.0 (14.5) |
subtype3 | 35 | 23 | 1.0 - 94.1 (12.0) |
subtype4 | 40 | 21 | 0.9 - 73.0 (16.0) |
P value = 0.00806 (ANOVA), Q value = 0.12
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 188 | 54.9 (16.2) |
subtype1 | 60 | 57.4 (14.3) |
subtype2 | 38 | 56.6 (14.9) |
subtype3 | 43 | 57.4 (18.0) |
subtype4 | 47 | 48.0 (16.2) |
P value = 0.922 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 87 | 101 |
subtype1 | 26 | 34 |
subtype2 | 17 | 21 |
subtype3 | 21 | 22 |
subtype4 | 23 | 24 |
Cluster Labels | 1 | 2 | 3 |
---|---|---|---|
Number of samples | 36 | 82 | 70 |
P value = 0.0492 (logrank test), Q value = 0.59
nPatients | nDeath | Duration Range (Median), Month | |
---|---|---|---|
ALL | 162 | 100 | 0.9 - 94.1 (12.0) |
subtype1 | 32 | 16 | 0.9 - 62.0 (14.5) |
subtype2 | 71 | 50 | 0.9 - 69.0 (10.0) |
subtype3 | 59 | 34 | 1.0 - 94.1 (15.0) |
P value = 0.172 (ANOVA), Q value = 1
nPatients | Mean (Std.Dev) | |
---|---|---|
ALL | 188 | 54.9 (16.2) |
subtype1 | 36 | 57.5 (14.3) |
subtype2 | 82 | 56.1 (14.0) |
subtype3 | 70 | 52.1 (19.0) |
P value = 0.78 (Fisher's exact test), Q value = 1
nPatients | FEMALE | MALE |
---|---|---|
ALL | 87 | 101 |
subtype1 | 15 | 21 |
subtype2 | 40 | 42 |
subtype3 | 32 | 38 |
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Cluster data file = LAML-TB.mergedcluster.txt
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Clinical data file = LAML-TB.clin.merged.picked.txt
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Number of patients = 200
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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
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 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, 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.