This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18257 genes and 4 clinical features across 154 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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1 gene correlated to 'AGE'.
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ADAP2|55803
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4 genes correlated to 'LYMPH.NODE.METASTASIS'.
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CRHR2|1395 , NKX6-2|84504 , NUP62CL|54830 , SEC24A|10802
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1 gene correlated to 'COMPLETENESS.OF.RESECTION'.
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GLP2R|9340
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No genes correlated to 'NUMBER.OF.LYMPH.NODES'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
LYMPH NODE METASTASIS | t test | N=4 | n1 | N=2 | n0 | N=2 |
COMPLETENESS OF RESECTION | ANOVA test | N=1 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |
AGE | Mean (SD) | 60.46 (6.9) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
ADAP2|55803 | 0.3828 | 1.041e-06 | 0.019 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 121 | |
N1 | 15 | |
Significant markers | N = 4 | |
Higher in N1 | 2 | |
Higher in N0 | 2 |
T(pos if higher in 'N1') | ttestP | Q | AUC | |
---|---|---|---|---|
CRHR2|1395 | -8.37 | 7.97e-08 | 0.00145 | 0.9429 |
NKX6-2|84504 | -6.35 | 2.202e-07 | 0.00402 | 0.8547 |
NUP62CL|54830 | 6.2 | 1.301e-06 | 0.0237 | 0.8225 |
SEC24A|10802 | 5.66 | 2.035e-06 | 0.0371 | 0.7708 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 117 | |
R1 | 29 | |
RX | 2 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
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GLP2R|9340 | 9.218e-10 | 1.68e-05 |
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Expresson data file = PRAD-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = PRAD-TP.clin.merged.picked.txt
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Number of patients = 154
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Number of genes = 18257
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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 multi-class clinical features (ordinal or nominal), one-way analysis of variance (Howell 2002) was applied to compare the log2-expression levels between different clinical classes using 'anova' 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.