This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 19740 genes and 4 clinical features across 35 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, no clinical feature related to at least one genes.
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No genes correlated to 'Time to Death', 'AGE', 'GENDER', and 'RACE'.
Complete statistical result table is provided in Supplement Table 1
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of genes that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
RACE | Kruskal-Wallis test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.2-211.2 (median=26) |
censored | N = 29 | |
death | N = 5 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 57.14 (12) |
Significant markers | N = 0 |
Table S3. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 18 | |
MALE | 17 | |
Significant markers | N = 0 |
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Expresson data file = DLBC-TP.meth.by_min_clin_corr.data.txt
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Clinical data file = DLBC-TP.merged_data.txt
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Number of patients = 35
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Number of genes = 19740
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Number of clinical features = 4
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 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 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 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.