This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 16900 genes and 4 clinical features across 56 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
-
2 genes correlated to 'AGE'.
-
PPTC7 , ZNF311
-
1 gene correlated to 'GENDER'.
-
UTP14C
-
8 genes correlated to 'COMPLETENESS.OF.RESECTION'.
-
SEPSECS , C5ORF42 , KDELC1 , GOLGA7 , IREB2 , ...
-
No genes correlated to 'Time to Death'
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=2 | older | N=0 | younger | N=2 |
GENDER | t test | N=1 | male | N=1 | female | N=0 |
COMPLETENESS OF RESECTION | ANOVA test | N=8 |
Time to Death | Duration (Months) | 0.1-83.6 (median=13.8) |
censored | N = 31 | |
death | N = 20 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 58.56 (15) |
Significant markers | N = 2 | |
pos. correlated | 0 | |
neg. correlated | 2 |
GENDER | Labels | N |
FEMALE | 21 | |
MALE | 35 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
UTP14C | 8.17 | 2.139e-08 | 0.000361 | 0.9456 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 42 | |
R1 | 4 | |
R2 | 1 | |
RX | 6 | |
Significant markers | N = 8 |
-
Expresson data file = LIHC-TP.meth.for_correlation.filtered_data.txt
-
Clinical data file = LIHC-TP.clin.merged.picked.txt
-
Number of patients = 56
-
Number of genes = 16900
-
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.