This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17870 genes and 3 clinical features across 72 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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18 genes correlated to 'GENDER'.
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PRKY|5616 , UTY|7404 , DDX3Y|8653 , KDM5D|8284 , EIF1AY|9086 , ...
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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 | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=18 | male | N=13 | female | N=5 |
Time to Death | Duration (Months) | 0.1-143.4 (median=19.3) |
censored | N = 49 | |
death | N = 23 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 61.47 (13) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 37 | |
MALE | 35 | |
Significant markers | N = 18 | |
Higher in MALE | 13 | |
Higher in FEMALE | 5 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
PRKY|5616 | 22.19 | 7.77e-30 | 1.39e-25 | 1 |
UTY|7404 | 25.41 | 6.309e-28 | 1.13e-23 | 1 |
DDX3Y|8653 | 25.79 | 3.639e-24 | 6.5e-20 | 1 |
KDM5D|8284 | 22.28 | 4.258e-24 | 7.61e-20 | 1 |
EIF1AY|9086 | 23.36 | 4.716e-24 | 8.42e-20 | 1 |
RPS4Y1|6192 | 24.03 | 3.97e-23 | 7.09e-19 | 1 |
NLGN4Y|22829 | 17.92 | 1.123e-22 | 2.01e-18 | 1 |
ZFY|7544 | 22.84 | 5.872e-22 | 1.05e-17 | 1 |
XIST|7503 | -14.79 | 5.374e-19 | 9.6e-15 | 0.9709 |
TTTY15|64595 | 20.24 | 6.544e-19 | 1.17e-14 | 1 |
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Expresson data file = SARC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = SARC-TP.merged_data.txt
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Number of patients = 72
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Number of genes = 17870
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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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.