This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 546 miRs and 8 clinical features across 397 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 6 clinical features related to at least one miRs.
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67 miRs correlated to 'Time to Death'.
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HSA-MIR-155 , HSA-MIR-10A , HSA-MIR-346 , HSA-MIR-15B , HSA-MIR-196B , ...
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41 miRs correlated to 'AGE'.
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HSA-MIR-34A , HSA-MIR-155 , HSA-MIR-25 , HSA-MIR-10A , HSA-MIR-126 , ...
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8 miRs correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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HSA-MIR-148A , HSA-MIR-222 , HSA-MIR-135B , HSA-MIR-221 , HSA-MIR-432 , ...
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94 miRs correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-1262 , HSA-MIR-592 , HSA-MIR-186 , HSA-MIR-3065 , HSA-MIR-219-1 , ...
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53 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-1274B , HSA-MIR-628 , HSA-MIR-3130-1 , HSA-MIR-30E , HSA-MIR-9-3 , ...
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1 miR correlated to 'RACE'.
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HSA-MIR-1304
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No miRs correlated to 'GENDER', and 'ETHNICITY'.
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=67 | shorter survival | N=62 | longer survival | N=5 |
AGE | Spearman correlation test | N=41 | older | N=33 | younger | N=8 |
GENDER | Wilcoxon test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=8 | higher score | N=0 | lower score | N=8 |
HISTOLOGICAL TYPE | Kruskal-Wallis test | N=94 | ||||
RADIATIONS RADIATION REGIMENINDICATION | Wilcoxon test | N=53 | yes | N=53 | no | N=0 |
RACE | Kruskal-Wallis test | N=1 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 0-211.2 (median=15) |
censored | N = 323 | |
death | N = 70 | |
Significant markers | N = 67 | |
associated with shorter survival | 62 | |
associated with longer survival | 5 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-155 | 1.87 | 4.252e-14 | 2.3e-11 | 0.787 |
HSA-MIR-10A | 1.34 | 3.779e-11 | 2.1e-08 | 0.714 |
HSA-MIR-346 | 0.59 | 4.889e-10 | 2.7e-07 | 0.33 |
HSA-MIR-15B | 1.89 | 8.416e-10 | 4.6e-07 | 0.781 |
HSA-MIR-196B | 1.22 | 4.321e-09 | 2.3e-06 | 0.715 |
HSA-MIR-148A | 1.58 | 1.638e-08 | 8.9e-06 | 0.751 |
HSA-MIR-9-1 | 0.41 | 8.078e-08 | 4.4e-05 | 0.242 |
HSA-MIR-9-2 | 0.41 | 8.185e-08 | 4.4e-05 | 0.242 |
HSA-MIR-23A | 1.86 | 6.298e-07 | 0.00034 | 0.714 |
HSA-MIR-3677 | 1.45 | 6.76e-07 | 0.00036 | 0.75 |
AGE | Mean (SD) | 43.28 (13) |
Significant markers | N = 41 | |
pos. correlated | 33 | |
neg. correlated | 8 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-34A | 0.3236 | 3.944e-11 | 2.15e-08 |
HSA-MIR-155 | 0.2671 | 6.534e-08 | 3.56e-05 |
HSA-MIR-25 | 0.266 | 7.459e-08 | 4.06e-05 |
HSA-MIR-10A | 0.2589 | 1.675e-07 | 9.1e-05 |
HSA-MIR-126 | 0.2579 | 1.876e-07 | 0.000102 |
HSA-MIR-146A | 0.2486 | 5.284e-07 | 0.000286 |
HSA-MIR-10B | 0.2418 | 1.084e-06 | 0.000585 |
HSA-MIR-2115 | 0.2507 | 1.269e-06 | 0.000684 |
HSA-MIR-664 | 0.2356 | 2.873e-06 | 0.00155 |
HSA-MIR-429 | 0.272 | 2.93e-06 | 0.00157 |
GENDER | Labels | N |
FEMALE | 178 | |
MALE | 219 | |
Significant markers | N = 0 |
8 miRs related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 87.99 (12) |
Significant markers | N = 8 | |
pos. correlated | 0 | |
neg. correlated | 8 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-148A | -0.2653 | 4.801e-05 | 0.0262 |
HSA-MIR-222 | -0.2598 | 6.949e-05 | 0.0379 |
HSA-MIR-135B | -0.2619 | 0.0002246 | 0.122 |
HSA-MIR-221 | -0.2359 | 0.0003175 | 0.172 |
HSA-MIR-432 | -0.2332 | 0.0003721 | 0.202 |
HSA-MIR-142 | -0.2308 | 0.0004301 | 0.233 |
HSA-MIR-337 | -0.2288 | 0.0004837 | 0.261 |
HSA-MIR-493 | -0.2306 | 0.0005348 | 0.288 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 142 | |
OLIGOASTROCYTOMA | 102 | |
OLIGODENDROGLIOMA | 153 | |
Significant markers | N = 94 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1262 | 7.847e-15 | 4.28e-12 |
HSA-MIR-592 | 4.233e-14 | 2.31e-11 |
HSA-MIR-186 | 1.164e-13 | 6.33e-11 |
HSA-MIR-3065 | 1.114e-12 | 6.05e-10 |
HSA-MIR-219-1 | 1.453e-12 | 7.87e-10 |
HSA-MIR-3074 | 3.626e-12 | 1.96e-09 |
HSA-MIR-576 | 4.179e-12 | 2.26e-09 |
HSA-MIR-21 | 6.299e-11 | 3.4e-08 |
HSA-MIR-505 | 6.834e-11 | 3.68e-08 |
HSA-MIR-301A | 7.47e-11 | 4.01e-08 |
53 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 88 | |
YES | 309 | |
Significant markers | N = 53 | |
Higher in YES | 53 | |
Higher in NO | 0 |
W(pos if higher in 'YES') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-1274B | 6864 | 3.67e-10 | 2e-07 | 0.7214 |
HSA-MIR-628 | 7698 | 5.296e-10 | 2.89e-07 | 0.7169 |
HSA-MIR-3130-1 | 6674 | 3.247e-09 | 1.77e-06 | 0.7144 |
HSA-MIR-30E | 8534 | 9.834e-08 | 5.34e-05 | 0.6862 |
HSA-MIR-9-3 | 18485 | 2.639e-07 | 0.000143 | 0.6798 |
HSA-MIR-424 | 8776 | 3.876e-07 | 0.00021 | 0.6773 |
HSA-MIR-3677 | 8850 | 5.823e-07 | 0.000314 | 0.6745 |
HSA-MIR-331 | 8852 | 5.886e-07 | 0.000317 | 0.6745 |
HSA-MIR-500A | 18327 | 6.319e-07 | 0.00034 | 0.674 |
HSA-MIR-1262 | 8529 | 1.406e-06 | 0.000755 | 0.6705 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 1 | |
ASIAN | 2 | |
BLACK OR AFRICAN AMERICAN | 14 | |
WHITE | 373 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1304 | 1.219e-05 | 0.00666 |
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Expresson data file = LGG-TP.miRseq_RPKM_log2.txt
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Clinical data file = LGG-TP.merged_data.txt
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Number of patients = 397
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Number of miRs = 546
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Number of clinical features = 8
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.