This pipeline uses various statistical tests to identify RPPAs whose expression levels correlated to selected clinical features.
Testing the association between 175 genes and 7 clinical features across 53 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.
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BAK1|BAK-R-C
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No genes correlated to 'Time to Death', 'AGE', 'GENDER', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', and 'TOBACCOSMOKINGHISTORYINDICATOR'.
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 | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
TOBACCOSMOKINGHISTORYINDICATOR | Spearman correlation test | N=0 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=1 | higher number.of.lymph.nodes | N=0 | lower number.of.lymph.nodes | N=1 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.4-118.9 (median=8.3) |
censored | N = 32 | |
death | N = 20 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 67.31 (9.8) |
Significant markers | N = 0 |
Table S3. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 21 | |
MALE | 32 | |
Significant markers | N = 0 |
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S4. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 80 (17) |
Score | N | |
40 | 1 | |
60 | 1 | |
70 | 1 | |
80 | 1 | |
90 | 7 | |
Significant markers | N = 0 |
Table S5. Basic characteristics of clinical feature: 'NUMBERPACKYEARSSMOKED'
NUMBERPACKYEARSSMOKED | Mean (SD) | 32.15 (18) |
Significant markers | N = 0 |
No gene related to 'TOBACCOSMOKINGHISTORYINDICATOR'.
Table S6. Basic characteristics of clinical feature: 'TOBACCOSMOKINGHISTORYINDICATOR'
TOBACCOSMOKINGHISTORYINDICATOR | Mean (SD) | 3.25 (0.96) |
Value | N | |
2 | 1 | |
3 | 1 | |
4 | 2 | |
Significant markers | N = 0 |
Table S7. Basic characteristics of clinical feature: 'NUMBER.OF.LYMPH.NODES'
NUMBER.OF.LYMPH.NODES | Mean (SD) | 1.53 (3) |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
Table S8. Get Full Table List of one gene significantly correlated to 'NUMBER.OF.LYMPH.NODES' by Spearman correlation test
SpearmanCorr | corrP | Q | |
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BAK1|BAK-R-C | -0.6234 | 8.197e-05 | 0.0143 |
Figure S1. Get High-res Image As an example, this figure shows the association of BAK1|BAK-R-C to 'NUMBER.OF.LYMPH.NODES'. P value = 8.2e-05 with Spearman correlation analysis. The straight line presents the best linear regression.
![](V7ex.png)
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Expresson data file = BLCA-TP.rppa.txt
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Clinical data file = BLCA-TP.clin.merged.picked.txt
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Number of patients = 53
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Number of genes = 175
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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 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.