This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 20008 genes and 5 clinical features across 162 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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1 gene correlated to 'AGE'.
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LYNX1
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15 genes correlated to 'PATHOLOGY.N.STAGE'.
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DYRK2 , DLEU2__2 , CCL2 , TDRKH , KCNJ2 , ...
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7 genes correlated to 'COMPLETENESS.OF.RESECTION'.
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CTNNA3__1 , LRRTM3__1 , CTNNA3 , LRRTM3 , AKR1B10 , ...
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No genes correlated to 'PATHOLOGY.T.STAGE', and 'NUMBER.OF.LYMPH.NODES'.
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=1 | older | N=1 | younger | N=0 |
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | t test | N=15 | class1 | N=0 | class0 | N=15 |
COMPLETENESS OF RESECTION | ANOVA test | N=7 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |
AGE | Mean (SD) | 60.04 (6.8) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
LYNX1 | 0.3624 | 2.313e-06 | 0.0463 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.61 (0.55) |
N | ||
2 | 68 | |
3 | 88 | |
4 | 5 | |
Significant markers | N = 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 128 | |
class1 | 15 | |
Significant markers | N = 15 | |
Higher in class1 | 0 | |
Higher in class0 | 15 |
T(pos if higher in 'class1') | ttestP | Q | AUC | |
---|---|---|---|---|
DYRK2 | -6.8 | 3.481e-09 | 6.96e-05 | 0.7432 |
DLEU2__2 | -6.19 | 6.253e-08 | 0.00125 | 0.8177 |
CCL2 | -6.08 | 7.085e-08 | 0.00142 | 0.8333 |
TDRKH | -5.68 | 1.115e-07 | 0.00223 | 0.7054 |
KCNJ2 | -5.59 | 1.283e-07 | 0.00257 | 0.7307 |
RRM2 | -5.79 | 3.398e-07 | 0.0068 | 0.7583 |
NUP54 | -5.28 | 8.503e-07 | 0.017 | 0.6745 |
DDX21 | -5.26 | 8.622e-07 | 0.0172 | 0.7172 |
ABCE1 | -5.6 | 1.092e-06 | 0.0218 | 0.7635 |
ANAPC10 | -5.6 | 1.092e-06 | 0.0218 | 0.7635 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 119 | |
R1 | 31 | |
RX | 3 | |
Significant markers | N = 7 |
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Expresson data file = PRAD-TP.meth.by_min_expr_corr.data.txt
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Clinical data file = PRAD-TP.clin.merged.picked.txt
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Number of patients = 162
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Number of genes = 20008
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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 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.