This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 354 miRs and 4 clinical features across 188 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one miRs.
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15 miRs correlated to 'Time to Death'.
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HSA-MIR-362 , HSA-MIR-532 , HSA-MIR-100 , HSA-MIR-502 , HSA-MIR-181B-1 , ...
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13 miRs 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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5 miRs correlated to 'GENDER'.
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HSA-MIR-505 , HSA-MIR-107 , HSA-MIR-1226 , HSA-MIR-651 , HSA-MIR-186
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1 miR correlated to 'RACE'.
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HSA-MIR-1304
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=15 | shorter survival | N=11 | longer survival | N=4 |
AGE | Spearman correlation test | N=13 | older | N=9 | younger | N=4 |
GENDER | Wilcoxon test | N=5 | male | N=5 | female | N=0 |
RACE | Kruskal-Wallis test | N=1 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 62 | |
death | N = 102 | |
Significant markers | N = 15 | |
associated with shorter survival | 11 | |
associated with longer survival | 4 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-362 | 1.5 | 4.137e-06 | 0.0015 | 0.663 |
HSA-MIR-532 | 1.47 | 1.594e-05 | 0.0056 | 0.66 |
HSA-MIR-100 | 0.84 | 9.762e-05 | 0.034 | 0.39 |
HSA-MIR-502 | 1.41 | 0.0001185 | 0.042 | 0.638 |
HSA-MIR-181B-1 | 0.77 | 0.000124 | 0.043 | 0.382 |
HSA-MIR-20B | 1.18 | 0.0001469 | 0.051 | 0.623 |
HSA-MIR-660 | 1.41 | 0.0001517 | 0.053 | 0.634 |
HSA-MIR-501 | 1.31 | 0.0002444 | 0.085 | 0.633 |
HSA-MIR-500 | 1.32 | 0.0002583 | 0.089 | 0.633 |
HSA-MIR-188 | 1.35 | 0.0002777 | 0.096 | 0.626 |
AGE | Mean (SD) | 54.9 (16) |
Significant markers | N = 13 | |
pos. correlated | 9 | |
neg. correlated | 4 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-598 | 0.3153 | 1.721e-05 | 0.00609 |
HSA-MIR-766 | -0.2942 | 4.174e-05 | 0.0147 |
HSA-MIR-29B-1 | 0.2923 | 4.688e-05 | 0.0165 |
HSA-MIR-20B | 0.2889 | 5.795e-05 | 0.0203 |
HSA-MIR-363 | 0.277 | 0.0001188 | 0.0416 |
HSA-MIR-29B-2 | 0.271 | 0.0001688 | 0.0589 |
HSA-MIR-181C | -0.268 | 0.0002011 | 0.07 |
HSA-MIR-532 | 0.2644 | 0.0002459 | 0.0853 |
HSA-MIR-22 | 0.2548 | 0.0004167 | 0.144 |
HSA-MIR-500 | 0.2481 | 0.0005977 | 0.206 |
GENDER | Labels | N |
FEMALE | 87 | |
MALE | 101 | |
Significant markers | N = 5 | |
Higher in MALE | 5 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-505 | 5930 | 3.646e-05 | 0.0129 | 0.6749 |
HSA-MIR-107 | 5897 | 5.342e-05 | 0.0189 | 0.6711 |
HSA-MIR-1226 | 4549 | 0.0002545 | 0.0896 | 0.6651 |
HSA-MIR-651 | 2869 | 0.0004099 | 0.144 | 0.652 |
HSA-MIR-186 | 5657 | 0.0006862 | 0.24 | 0.6438 |
RACE | Labels | N |
ASIAN | 2 | |
BLACK OR AFRICAN AMERICAN | 13 | |
WHITE | 171 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1304 | 0.0004229 | 0.149 |
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Expresson data file = LAML-TB.miRseq_RPKM_log2.txt
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Clinical data file = LAML-TB.merged_data.txt
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Number of patients = 188
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Number of miRs = 354
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Number of clinical features = 4
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 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.
In addition to the links below, the full results of the analysis summarized in this report can also be downloaded programmatically using firehose_get, or interactively from either the Broad GDAC website or TCGA Data Coordination Center Portal.