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 83 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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PRKY|5616 , UTY|7404 , KDM5D|8284 , DDX3Y|8653 , ZFY|7544 , ...
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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=13 | female | N=4 |
Time to Death | Duration (Months) | 0.1-143.4 (median=18.5) |
censored | N = 56 | |
death | N = 27 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 61.94 (13) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 45 | |
MALE | 38 | |
Significant markers | N = 17 | |
Higher in MALE | 13 | |
Higher in FEMALE | 4 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
PRKY|5616 | 23.62 | 3.368e-34 | 6.02e-30 | 1 |
UTY|7404 | 25.79 | 2.816e-31 | 5.03e-27 | 1 |
KDM5D|8284 | 24.26 | 3.383e-30 | 6.04e-26 | 1 |
DDX3Y|8653 | 28.09 | 1.296e-29 | 2.31e-25 | 1 |
ZFY|7544 | 26.21 | 4.994e-29 | 8.92e-25 | 1 |
RPS4Y1|6192 | 26.03 | 5.14e-29 | 9.18e-25 | 1 |
EIF1AY|9086 | 25.26 | 3.884e-28 | 6.94e-24 | 1 |
NLGN4Y|22829 | 19.05 | 7.423e-26 | 1.33e-21 | 1 |
TTTY15|64595 | 22.81 | 9.7e-25 | 1.73e-20 | 1 |
CYORF15A|246126 | 21.06 | 6.986e-20 | 1.25e-15 | 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 = 83
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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.