This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18117 genes and 3 clinical features across 125 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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17 genes correlated to 'GENDER'.
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ZFY|7544 , XIST|7503 , PRKY|5616 , RPS4Y1|6192 , DDX3Y|8653 , ...
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No genes correlated to 'Time to Death', and 'AGE'.
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=17 | male | N=12 | female | N=5 |
Time to Death | Duration (Months) | 10.1-131.1 (median=44) |
censored | N = 5 | |
death | N = 10 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 55.38 (17) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 43 | |
MALE | 82 | |
Significant markers | N = 17 | |
Higher in MALE | 12 | |
Higher in FEMALE | 5 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
ZFY|7544 | 27.21 | 1.028e-49 | 1.86e-45 | 0.9978 |
XIST|7503 | -19.04 | 2.467e-36 | 4.47e-32 | 0.9747 |
PRKY|5616 | 21.69 | 9.385e-33 | 1.7e-28 | 0.9983 |
RPS4Y1|6192 | 24.59 | 1.686e-31 | 3.05e-27 | 1 |
DDX3Y|8653 | 26.4 | 4.798e-29 | 8.69e-25 | 1 |
KDM5D|8284 | 25.11 | 2.159e-23 | 3.91e-19 | 1 |
TSIX|9383 | -12.66 | 2.532e-21 | 4.58e-17 | 0.9632 |
EIF1AY|9086 | 22.96 | 1.154e-18 | 2.09e-14 | 1 |
TTTY15|64595 | 18.92 | 7.358e-16 | 1.33e-11 | 0.997 |
USP9Y|8287 | 18.18 | 4.112e-15 | 7.44e-11 | 0.9981 |
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Expresson data file = SKCM.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = SKCM.clin.merged.picked.txt
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Number of patients = 125
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Number of genes = 18117
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Number of clinical features = 3
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.