This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 171 genes and 5 clinical features across 210 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
-
1 gene correlated to 'Time to Death'.
-
PTGS2|COX-2-R-C
-
No genes correlated to 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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 Q value < 0.05.
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=1 | shorter survival | N=1 | longer survival | N=0 |
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.1-108.8 (median=7.9) |
censored | N = 73 | |
death | N = 137 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
Table S2. Get Full Table List of one gene significantly associated with 'Time to Death' by Cox regression test
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
PTGS2|COX-2-R-C | 1.51 | 8.798e-05 | 0.015 | 0.595 |
Figure S1. Get High-res Image As an example, this figure shows the association of PTGS2|COX-2-R-C to 'Time to Death'. four curves present the cumulative survival rates of 4 quartile subsets of patients. P value = 8.8e-05 with univariate Cox regression analysis using continuous log-2 expression values.
![](V1ex.png)
Table S3. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 59.94 (14) |
Significant markers | N = 0 |
Table S4. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 85 | |
MALE | 125 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.6 (15) |
Significant markers | N = 0 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 150 | |
YES | 60 | |
Significant markers | N = 0 |
-
Expresson data file = GBM-TP.rppa.txt
-
Clinical data file = GBM-TP.clin.merged.picked.txt
-
Number of patients = 210
-
Number of genes = 171
-
Number of clinical features = 5
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 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.