This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 354 genes and 3 clinical features across 188 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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7 genes correlated to 'Time to Death'.
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HSA-MIR-362 , HSA-MIR-532 , HSA-MIR-181B-1 , HSA-MIR-502 , HSA-MIR-660 , ...
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5 genes correlated to 'AGE'.
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HSA-MIR-598 , HSA-MIR-766 , HSA-MIR-29B-1 , HSA-MIR-20B , HSA-MIR-363
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3 genes correlated to 'GENDER'.
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HSA-MIR-107 , HSA-MIR-1226 , HSA-MIR-505
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=7 | shorter survival | N=6 | longer survival | N=1 |
AGE | Spearman correlation test | N=5 | older | N=4 | younger | N=1 |
GENDER | t test | N=3 | male | N=3 | female | N=0 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 62 | |
death | N = 100 | |
Significant markers | N = 7 | |
associated with shorter survival | 6 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-362 | 1.51 | 2.917e-06 | 0.001 | 0.669 |
HSA-MIR-532 | 1.48 | 9.616e-06 | 0.0034 | 0.668 |
HSA-MIR-181B-1 | 0.77 | 8.478e-05 | 0.03 | 0.378 |
HSA-MIR-502 | 1.43 | 8.721e-05 | 0.031 | 0.644 |
HSA-MIR-660 | 1.43 | 9.931e-05 | 0.035 | 0.643 |
HSA-MIR-501 | 1.33 | 0.000109 | 0.038 | 0.641 |
HSA-MIR-188 | 1.38 | 0.0001184 | 0.041 | 0.635 |
AGE | Mean (SD) | 54.89 (16) |
Significant markers | N = 5 | |
pos. correlated | 4 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
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HSA-MIR-598 | 0.3159 | 1.653e-05 | 0.00585 |
HSA-MIR-766 | -0.2945 | 4.1e-05 | 0.0145 |
HSA-MIR-29B-1 | 0.2923 | 4.689e-05 | 0.0165 |
HSA-MIR-20B | 0.2895 | 5.577e-05 | 0.0196 |
HSA-MIR-363 | 0.2772 | 0.0001175 | 0.0411 |
GENDER | Labels | N |
FEMALE | 87 | |
MALE | 101 | |
Significant markers | N = 3 | |
Higher in MALE | 3 | |
Higher in FEMALE | 0 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-107 | 4.15 | 5.109e-05 | 0.0181 | 0.6711 |
HSA-MIR-1226 | 4.03 | 8.43e-05 | 0.0298 | 0.6651 |
HSA-MIR-505 | 4.02 | 8.681e-05 | 0.0306 | 0.6749 |
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Expresson data file = LAML-TB.miRseq_RPKM_log2.txt
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Clinical data file = LAML-TB.clin.merged.picked.txt
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Number of patients = 188
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Number of genes = 354
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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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.