(primary blood tumor (peripheral) cohort)
This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 367 genes and 3 clinical features across 187 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-502 , HSA-MIR-660 , HSA-MIR-500 , ...
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6 genes correlated to 'AGE'.
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HSA-MIR-598 , HSA-MIR-766 , HSA-MIR-20B , HSA-MIR-363 , HSA-MIR-29B-2 , ...
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2 genes correlated to 'GENDER'.
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HSA-MIR-505 , HSA-MIR-651
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=7 | shorter survival | N=7 | longer survival | N=0 |
AGE | Spearman correlation test | N=6 | older | N=5 | younger | N=1 |
GENDER | t test | N=2 | male | N=1 | female | N=1 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 62 | |
death | N = 99 | |
Significant markers | N = 7 | |
associated with shorter survival | 7 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-362 | 1.53 | 1.577e-06 | 0.00058 | 0.673 |
HSA-MIR-532 | 1.5 | 7.938e-06 | 0.0029 | 0.671 |
HSA-MIR-502 | 1.46 | 1.188e-05 | 0.0043 | 0.667 |
HSA-MIR-660 | 1.45 | 5.494e-05 | 0.02 | 0.646 |
HSA-MIR-500 | 1.35 | 9.074e-05 | 0.033 | 0.647 |
HSA-MIR-501 | 1.31 | 0.000129 | 0.047 | 0.645 |
HSA-MIR-188 | 1.35 | 0.0001377 | 0.05 | 0.641 |
AGE | Mean (SD) | 55.07 (16) |
Significant markers | N = 6 | |
pos. correlated | 5 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-598 | 0.3199 | 1.011e-05 | 0.00371 |
HSA-MIR-766 | -0.2975 | 3.542e-05 | 0.013 |
HSA-MIR-20B | 0.292 | 5.013e-05 | 0.0183 |
HSA-MIR-363 | 0.2919 | 5.052e-05 | 0.0184 |
HSA-MIR-29B-2 | 0.283 | 8.709e-05 | 0.0316 |
HSA-MIR-29B-1 | 0.2801 | 0.0001032 | 0.0373 |
GENDER | Labels | N |
FEMALE | 86 | |
MALE | 101 | |
Significant markers | N = 2 | |
Higher in MALE | 1 | |
Higher in FEMALE | 1 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-505 | 3.94 | 0.0001204 | 0.0442 | 0.6702 |
HSA-MIR-651 | -3.9 | 0.0001333 | 0.0488 | 0.6538 |
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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 = 187
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Number of genes = 367
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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.