This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18296 genes and 4 clinical features across 87 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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11 genes correlated to 'GENDER'.
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USP9Y|8287 , PRKY|5616 , ZFY|7544 , RPS4Y1|6192 , XIST|7503 , ...
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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
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=11 | male | N=9 | female | N=2 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |
Time to Death | Duration (Months) | 0.4-118.9 (median=7.2) |
censored | N = 58 | |
death | N = 25 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 66.99 (11) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 27 | |
MALE | 60 | |
Significant markers | N = 11 | |
Higher in MALE | 9 | |
Higher in FEMALE | 2 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
USP9Y|8287 | 28.69 | 1.228e-36 | 2.24e-32 | 1 |
PRKY|5616 | 21.34 | 1.135e-32 | 2.08e-28 | 0.998 |
ZFY|7544 | 26.72 | 4.522e-29 | 8.27e-25 | 1 |
RPS4Y1|6192 | 32.77 | 3.59e-26 | 6.56e-22 | 1 |
XIST|7503 | -17.4 | 4.701e-26 | 8.59e-22 | 0.9898 |
TSIX|9383 | -13 | 7.3e-18 | 1.33e-13 | 0.9762 |
NLGN4Y|22829 | 14.53 | 4.667e-17 | 8.53e-13 | 0.9824 |
DDX3Y|8653 | 25.59 | 1.894e-16 | 3.46e-12 | 1 |
CYORF15A|246126 | 21.9 | 9.511e-16 | 1.74e-11 | 1 |
KDM5D|8284 | 29.61 | 8.758e-15 | 1.6e-10 | 1 |
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Expresson data file = BLCA.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = BLCA.clin.merged.picked.txt
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Number of patients = 87
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Number of genes = 18296
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Number of clinical features = 4
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.