This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17814 genes and 4 clinical features across 16 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.
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2 genes correlated to 'GENDER'.
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RPS4Y1 , NAP1L3
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1 gene correlated to 'PATHOLOGY.T'.
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SHKBP1
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No genes correlated to 'AGE', and 'TUMOR.STAGE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=2 | male | N=1 | female | N=1 |
PATHOLOGY T | Spearman correlation test | N=1 | higher pT | N=0 | lower pT | N=1 |
TUMOR STAGE | Spearman correlation test | N=0 |
AGE | Mean (SD) | 57.94 (11) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 4 | |
MALE | 12 | |
Significant markers | N = 2 | |
Higher in MALE | 1 | |
Higher in FEMALE | 1 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
RPS4Y1 | 8.47 | 7.923e-07 | 0.0141 | 1 |
NAP1L3 | -8.33 | 1.936e-06 | 0.0345 | 1 |
PATHOLOGY.T | Mean (SD) | 1.69 (0.7) |
N | ||
T1 | 7 | |
T2 | 7 | |
T3 | 2 | |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
SHKBP1 | -0.9131 | 7.931e-07 | 0.0141 |
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Expresson data file = KIRP-TP.medianexp.txt
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Clinical data file = KIRP-TP.clin.merged.picked.txt
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Number of patients = 16
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Number of genes = 17814
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Number of clinical features = 4
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.