This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18265 genes and 4 clinical features across 65 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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9 genes correlated to 'GENDER'.
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USP9Y|8287 , ZFY|7544 , PRKY|5616 , XIST|7503 , CYORF15A|246126 , ...
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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=9 | male | N=7 | female | N=2 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 |
Time to Death | Duration (Months) | 0.4-118.9 (median=7.8) |
censored | N = 42 | |
death | N = 23 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 67.51 (11) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 22 | |
MALE | 43 | |
Significant markers | N = 9 | |
Higher in MALE | 7 | |
Higher in FEMALE | 2 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
USP9Y|8287 | 26.19 | 5.98e-29 | 1.09e-24 | 1 |
ZFY|7544 | 25.48 | 4.084e-26 | 7.45e-22 | 1 |
PRKY|5616 | 19.16 | 3.403e-25 | 6.21e-21 | 1 |
XIST|7503 | -16.43 | 2.466e-19 | 4.5e-15 | 0.9983 |
CYORF15A|246126 | 19.28 | 4.126e-18 | 7.53e-14 | 1 |
RPS4Y1|6192 | 28.83 | 6.875e-18 | 1.25e-13 | 1 |
TSIX|9383 | -13.62 | 2.817e-15 | 5.14e-11 | 0.9979 |
NLGN4Y|22829 | 13.58 | 4.287e-15 | 7.82e-11 | 0.995 |
DDX3Y|8653 | 24.08 | 5.843e-15 | 1.07e-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 = 65
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Number of genes = 18265
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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.