This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 475 genes and 4 clinical features across 154 samples, statistically thresholded by Q value < 0.05, 2 clinical features related to at least one genes.
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3 genes correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-219-2 , HSA-MIR-449A , HSA-LET-7F-1
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1 gene correlated to 'NUMBER.OF.LYMPH.NODES'.
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HSA-MIR-378
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No genes correlated to 'AGE', and 'COMPLETENESS.OF.RESECTION'.
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=0 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=3 | yes | N=2 | no | N=1 |
COMPLETENESS OF RESECTION | ANOVA test | N=0 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=1 | higher number.of.lymph.nodes | N=0 | lower number.of.lymph.nodes | N=1 |
AGE | Mean (SD) | 60.4 (6.9) |
Significant markers | N = 0 |
3 genes related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 5 | |
YES | 149 | |
Significant markers | N = 3 | |
Higher in YES | 2 | |
Higher in NO | 1 |
T(pos if higher in 'YES') | ttestP | Q | AUC | |
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HSA-MIR-219-2 | 11.4 | 1.2e-10 | 5.16e-08 | 0.9788 |
HSA-MIR-449A | 5.76 | 1.993e-05 | 0.00855 | 0.7176 |
HSA-LET-7F-1 | -6.58 | 8.213e-05 | 0.0351 | 0.8282 |
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 118 | |
R1 | 29 | |
RX | 2 | |
Significant markers | N = 0 |
NUMBER.OF.LYMPH.NODES | Mean (SD) | 0.2 (0.74) |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
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HSA-MIR-378 | -0.3401 | 5.449e-05 | 0.0258 |
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Expresson data file = PRAD-TP.miRseq_RPKM_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 = 475
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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.