This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 20645 genes and 5 clinical features across 31 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 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
AGE | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | Wilcoxon test | N=0 | ||||
ETHNICITY | Wilcoxon test | N=0 |
AGE | Mean (SD) | 30.68 (8.4) |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 5 | |
STAGE IA | 1 | |
STAGE IB | 2 | |
STAGE II | 3 | |
STAGE IIA | 1 | |
STAGE IIC | 1 | |
STAGE III | 2 | |
STAGE IIIB | 2 | |
STAGE IIIC | 1 | |
STAGE IS | 13 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 1.58 (0.62) |
N | ||
1 | 15 | |
2 | 14 | |
3 | 2 | |
Significant markers | N = 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 13 | |
class1 | 4 | |
Significant markers | N = 0 |
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Expresson data file = TGCT-TP.meth.by_min_clin_corr.data.txt
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Clinical data file = TGCT-TP.merged_data.txt
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Number of patients = 31
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Number of genes = 20645
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Number of clinical features = 5
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.