This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 14047 genes and 8 clinical features across 574 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one genes.
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348 genes correlated to 'AGE'.
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PCDHGA1__4 , PCDHGA10__3 , PCDHGA2__4 , PCDHGA3__4 , PCDHGA4__4 , ...
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No genes correlated to 'Time to Death', 'PRIMARY.SITE.OF.DISEASE', 'KARNOFSKY.PERFORMANCE.SCORE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'COMPLETENESS.OF.RESECTION', 'RACE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=348 | older | N=146 | younger | N=202 |
PRIMARY SITE OF DISEASE | Kruskal-Wallis test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | Wilcoxon test | N=0 | ||||
COMPLETENESS OF RESECTION | Kruskal-Wallis test | N=0 | ||||
RACE | Kruskal-Wallis test | N=0 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 0.3-180.2 (median=28.5) |
censored | N = 263 | |
death | N = 295 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 59.79 (12) |
Significant markers | N = 348 | |
pos. correlated | 146 | |
neg. correlated | 202 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
PCDHGA1__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA10__3 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA2__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA3__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA4__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA5__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA6__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA7__4 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA8__3 | 0.3019 | 4.094e-13 | 5.75e-09 |
PCDHGA9__3 | 0.3019 | 4.094e-13 | 5.75e-09 |
PRIMARY.SITE.OF.DISEASE | Labels | N |
OMENTUM | 2 | |
OVARY | 560 | |
PERITONEUM OVARY | 2 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.64 (13) |
Score | N | |
40 | 2 | |
60 | 20 | |
80 | 49 | |
100 | 7 | |
Significant markers | N = 0 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 3 | |
YES | 571 | |
Significant markers | N = 0 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 14 | |
R1 | 26 | |
R2 | 2 | |
Significant markers | N = 0 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 3 | |
ASIAN | 18 | |
BLACK OR AFRICAN AMERICAN | 24 | |
NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER | 1 | |
WHITE | 486 | |
Significant markers | N = 0 |
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Expresson data file = OV-TP.meth.by_min_clin_corr.data.txt
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Clinical data file = OV-TP.merged_data.txt
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Number of patients = 574
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Number of genes = 14047
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Number of clinical features = 8
For survival clinical features, Wald's test in univariate Cox regression analysis with proportional hazards model (Andersen and Gill 1982) was used to estimate the P values using the 'coxph' function in R. Kaplan-Meier survival curves were plot using the four quartile subgroups of patients based on expression levels
For continuous numerical clinical features, Spearman's rank correlation coefficients (Spearman 1904) and two-tailed P values were estimated using 'cor.test' function in R
For multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' function in R
For two-class clinical features, two-tailed Student's t test with unequal variance (Lehmann and Romano 2005) was applied to compare the log2-expression levels between the two clinical classes 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.