This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 19847 genes and 4 clinical features across 152 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one genes.
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9 genes correlated to 'AGE'.
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LOC153328 , CCL18 , IKZF1 , LAMB3 , ZIM2__1 , ...
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8 genes correlated to 'GENDER'.
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ALG11__1 , UTP14C , FAM35A , GLUD1 , ASF1A , ...
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No genes correlated to 'Time to Death', and 'RACE'.
Complete statistical result table is provided in Supplement Table 1
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=9 | older | N=0 | younger | N=9 |
GENDER | Wilcoxon test | N=8 | male | N=8 | female | N=0 |
RACE | Kruskal-Wallis test | N=0 |
Time to Death | Duration (Months) | 0.1-175 (median=18.1) |
censored | N = 101 | |
death | N = 50 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 61.48 (14) |
Significant markers | N = 9 | |
pos. correlated | 0 | |
neg. correlated | 9 |
GENDER | Labels | N |
FEMALE | 87 | |
MALE | 65 | |
Significant markers | N = 8 | |
Higher in MALE | 8 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
ALG11__1 | 5095 | 3.102e-17 | 6.16e-13 | 0.901 |
UTP14C | 5095 | 3.102e-17 | 6.16e-13 | 0.901 |
FAM35A | 1305 | 1.443e-08 | 0.000286 | 0.7692 |
GLUD1 | 1305 | 1.443e-08 | 0.000286 | 0.7692 |
ASF1A | 4113 | 1.705e-06 | 0.0338 | 0.7273 |
TET2 | 4020 | 4.19e-06 | 0.0831 | 0.7191 |
TBX2 | 4044 | 5.938e-06 | 0.118 | 0.7151 |
ITM2C | 4030 | 7.59e-06 | 0.151 | 0.7126 |
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Expresson data file = SARC-TP.meth.by_min_clin_corr.data.txt
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Clinical data file = SARC-TP.merged_data.txt
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Number of patients = 152
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Number of genes = 19847
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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.