This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 193 genes and 7 clinical features across 46 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.
-
8 genes correlated to 'Time to Death'.
-
TFRC|TFRC-R-V , FN1|FIBRONECTIN-R-V , CDKN1B|P27_PT157-R-C , MAPK1|ERK2-R-E , CCNB1|CYCLIN_B1-R-V , ...
-
1 gene correlated to 'AGE'.
-
BECN1|BECLIN-G-C
-
4 genes correlated to 'PATHOLOGY.T.STAGE'.
-
CCNB1|CYCLIN_B1-R-V , YAP1|YAP_PS127-R-E , TFRC|TFRC-R-V , GSK3A GSK3B|GSK3-ALPHA-BETA-M-V
-
No genes correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'GENDER', and 'ETHNICITY'.
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=8 | shorter survival | N=6 | longer survival | N=2 |
AGE | Spearman correlation test | N=1 | older | N=0 | younger | N=1 |
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=4 | higher stage | N=4 | lower stage | N=0 |
PATHOLOGY N STAGE | Wilcoxon test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 4.1-153.6 (median=42) |
censored | N = 33 | |
death | N = 13 | |
Significant markers | N = 8 | |
associated with shorter survival | 6 | |
associated with longer survival | 2 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
TFRC|TFRC-R-V | 2.8 | 7.282e-05 | 0.014 | 0.835 |
FN1|FIBRONECTIN-R-V | 2.8 | 0.0002722 | 0.052 | 0.819 |
CDKN1B|P27_PT157-R-C | 0 | 0.0003102 | 0.059 | 0.23 |
MAPK1|ERK2-R-E | 6.4 | 0.0006817 | 0.13 | 0.765 |
CCNB1|CYCLIN_B1-R-V | 2.1 | 0.0009527 | 0.18 | 0.835 |
WWTR1|TAZ-R-V | 121 | 0.001065 | 0.2 | 0.757 |
TSC1|TSC1-R-C | 14 | 0.001297 | 0.24 | 0.775 |
PRKCA |PKC-ALPHA_PS657-R-C | 0.39 | 0.00149 | 0.28 | 0.235 |
AGE | Mean (SD) | 47.17 (14) |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
BECN1|BECLIN-G-C | -0.4659 | 0.001101 | 0.212 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 2 | |
STAGE II | 25 | |
STAGE III | 10 | |
STAGE IV | 8 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.42 (0.81) |
N | ||
1 | 2 | |
2 | 29 | |
3 | 7 | |
4 | 7 | |
Significant markers | N = 4 | |
pos. correlated | 4 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
CCNB1|CYCLIN_B1-R-V | 0.5439 | 0.0001128 | 0.0218 |
YAP1|YAP_PS127-R-E | 0.4916 | 0.0006041 | 0.116 |
TFRC|TFRC-R-V | 0.4721 | 0.001061 | 0.203 |
GSK3A GSK3B|GSK3-ALPHA-BETA-M-V | 0.4596 | 0.001492 | 0.284 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 39 | |
class1 | 6 | |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 28 | |
MALE | 18 | |
Significant markers | N = 0 |
-
Expresson data file = ACC-TP.rppa.txt
-
Clinical data file = ACC-TP.merged_data.txt
-
Number of patients = 46
-
Number of genes = 193
-
Number of clinical features = 7
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 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 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.
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.