This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17576 genes and 3 clinical features across 21 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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7 genes correlated to 'GENDER'.
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RPS4Y1|6192 , TSIX|9383 , DDX3Y|8653 , PRKY|5616 , 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 | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=7 | male | N=5 | female | N=2 |
Time to Death | Duration (Months) | 2-211.2 (median=31.7) |
censored | N = 17 | |
death | N = 4 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 53.95 (12) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 13 | |
MALE | 8 | |
Significant markers | N = 7 | |
Higher in MALE | 5 | |
Higher in FEMALE | 2 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
RPS4Y1|6192 | 13.46 | 2.056e-10 | 3.53e-06 | 1 |
TSIX|9383 | -13.05 | 2.967e-10 | 5.09e-06 | 1 |
DDX3Y|8653 | 19.73 | 4.682e-10 | 8.04e-06 | 1 |
PRKY|5616 | 11.3 | 3.182e-08 | 0.000546 | 1 |
EIF1AY|9086 | 13.09 | 5.071e-08 | 0.00087 | 1 |
CYORF15A|246126 | 11.94 | 3.058e-07 | 0.00525 | 1 |
XIST|7503 | -7.27 | 1.222e-06 | 0.021 | 1 |
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Expresson data file = DLBC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = DLBC-TP.merged_data.txt
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Number of patients = 21
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Number of genes = 17576
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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.