This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18210 genes and 5 clinical features across 151 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.
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26 genes correlated to 'GENDER'.
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XIST|7503 , RPS4Y1|6192 , KDM5D|8284 , DDX3Y|8653 , USP9Y|8287 , ...
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1 gene correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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ZBTB33|10009
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No genes correlated to 'Time to Death', 'AGE', and 'KARNOFSKY.PERFORMANCE.SCORE'.
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=26 | male | N=15 | female | N=11 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=1 | yes | N=0 | no | N=1 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
Time to Death | Duration (Months) | 0.2-54 (median=8.6) |
censored | N = 53 | |
death | N = 98 | |
Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
AGE | Mean (SD) | 60 (13) |
Significant markers | N = 0 |
Table S3. Basic characteristics of clinical feature: 'GENDER'
GENDER | Labels | N |
FEMALE | 53 | |
MALE | 98 | |
Significant markers | N = 26 | |
Higher in MALE | 15 | |
Higher in FEMALE | 11 |
Table S4. Get Full Table List of top 10 genes differentially expressed by 'GENDER'
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
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XIST|7503 | -39.55 | 1.604e-57 | 2.92e-53 | 1 |
RPS4Y1|6192 | 48.85 | 3.901e-54 | 7.1e-50 | 1 |
KDM5D|8284 | 47.43 | 6.112e-51 | 1.11e-46 | 1 |
DDX3Y|8653 | 51.68 | 6.086e-50 | 1.11e-45 | 1 |
USP9Y|8287 | 51.46 | 5.254e-48 | 9.56e-44 | 1 |
CYORF15A|246126 | 42.68 | 6.12e-44 | 1.11e-39 | 1 |
TSIX|9383 | -22.63 | 5.778e-42 | 1.05e-37 | 0.9977 |
EIF1AY|9086 | 42.09 | 2.671e-41 | 4.86e-37 | 1 |
ZFY|7544 | 50.15 | 2.927e-41 | 5.33e-37 | 1 |
PRKY|5616 | 25.73 | 2.98e-35 | 5.42e-31 | 0.9989 |
Figure S1. Get High-res Image As an example, this figure shows the association of XIST|7503 to 'GENDER'. P value = 1.6e-57 with T-test analysis.
![](V3ex.png)
No gene related to 'KARNOFSKY.PERFORMANCE.SCORE'.
Table S5. Basic characteristics of clinical feature: 'KARNOFSKY.PERFORMANCE.SCORE'
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 75.75 (14) |
Significant markers | N = 0 |
One gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
Table S6. Basic characteristics of clinical feature: 'RADIATIONS.RADIATION.REGIMENINDICATION'
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 96 | |
YES | 55 | |
Significant markers | N = 1 | |
Higher in YES | 0 | |
Higher in NO | 1 |
Table S7. Get Full Table List of one gene differentially expressed by 'RADIATIONS.RADIATION.REGIMENINDICATION'
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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ZBTB33|10009 | -5.15 | 1.079e-06 | 0.0197 | 0.7299 |
Figure S2. Get High-res Image As an example, this figure shows the association of ZBTB33|10009 to 'RADIATIONS.RADIATION.REGIMENINDICATION'. P value = 1.08e-06 with T-test analysis.
![](V5ex.png)
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Expresson data file = GBM-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = GBM-TP.clin.merged.picked.txt
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Number of patients = 151
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Number of genes = 18210
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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.