This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 171 genes and 6 clinical features across 211 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', 'HISTOLOGICAL.TYPE', and 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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=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 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 |
Time to Death | Duration (Months) | 0.1-108.8 (median=8.2) |
censored | N = 54 | |
death | N = 157 | |
Significant markers | N = 1 | |
associated with shorter survival | 1 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
PTGS2|COX-2-R-C | 1.46 | 0.0001779 | 0.03 | 0.582 |
AGE | Mean (SD) | 59.88 (14) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 85 | |
MALE | 126 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.68 (15) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
GLIOBLASTOMA MULTIFORME (GBM) | 1 | |
TREATED PRIMARY GBM | 3 | |
UNTREATED PRIMARY (DE NOVO) GBM | 207 | |
Significant markers | N = 0 |
-
Expresson data file = GBM-TP.rppa.txt
-
Clinical data file = GBM-TP.merged_data.txt
-
Number of patients = 211
-
Number of genes = 171
-
Number of clinical features = 6
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.