This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 548 miRs and 8 clinical features across 430 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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101 miRs correlated to 'Time to Death'.
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HSA-MIR-155 , HSA-MIR-10A , HSA-MIR-15B , HSA-MIR-148A , HSA-MIR-196B , ...
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41 miRs correlated to 'AGE'.
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HSA-MIR-34A , HSA-MIR-155 , HSA-MIR-10B , HSA-MIR-25 , HSA-MIR-146A , ...
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19 miRs correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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HSA-MIR-222 , HSA-MIR-148A , HSA-MIR-181A-2 , HSA-MIR-432 , HSA-MIR-135B , ...
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107 miRs correlated to 'HISTOLOGICAL.TYPE'.
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HSA-MIR-1262 , HSA-MIR-186 , HSA-MIR-219-1 , HSA-MIR-592 , HSA-MIR-576 , ...
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56 miRs correlated to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
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HSA-MIR-1274B , HSA-MIR-628 , HSA-MIR-3130-1 , HSA-MIR-9-3 , HSA-MIR-30E , ...
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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=101 | shorter survival | N=96 | longer survival | N=5 |
AGE | Spearman correlation test | N=41 | older | N=32 | younger | N=9 |
GENDER | Wilcoxon test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=19 | higher score | N=1 | lower score | N=18 |
HISTOLOGICAL TYPE | Kruskal-Wallis test | N=107 | ||||
RADIATIONS RADIATION REGIMENINDICATION | Wilcoxon test | N=56 | yes | N=56 | no | N=0 |
RACE | Kruskal-Wallis test | N=1 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 0-211.2 (median=15.8) |
censored | N = 354 | |
death | N = 74 | |
Significant markers | N = 101 | |
associated with shorter survival | 96 | |
associated with longer survival | 5 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-155 | 1.96 | 0 | 0 | 0.805 |
HSA-MIR-10A | 1.34 | 2.802e-12 | 1.5e-09 | 0.729 |
HSA-MIR-15B | 2 | 4.946e-12 | 2.7e-09 | 0.797 |
HSA-MIR-148A | 1.65 | 8.413e-11 | 4.6e-08 | 0.767 |
HSA-MIR-196B | 1.24 | 1.876e-10 | 1e-07 | 0.732 |
HSA-MIR-9-1 | 0.39 | 3.472e-09 | 1.9e-06 | 0.231 |
HSA-MIR-9-2 | 0.39 | 3.511e-09 | 1.9e-06 | 0.231 |
HSA-MIR-346 | 0.62 | 1.534e-08 | 8.3e-06 | 0.37 |
HSA-MIR-23A | 1.96 | 1.585e-08 | 8.6e-06 | 0.733 |
HSA-MIR-142 | 1.67 | 2.849e-08 | 1.5e-05 | 0.727 |
AGE | Mean (SD) | 42.94 (13) |
Significant markers | N = 41 | |
pos. correlated | 32 | |
neg. correlated | 9 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-34A | 0.2988 | 2.677e-10 | 1.47e-07 |
HSA-MIR-155 | 0.2637 | 2.956e-08 | 1.62e-05 |
HSA-MIR-10B | 0.2575 | 6.342e-08 | 3.46e-05 |
HSA-MIR-25 | 0.2575 | 6.349e-08 | 3.46e-05 |
HSA-MIR-146A | 0.255 | 8.534e-08 | 4.64e-05 |
HSA-MIR-10A | 0.2492 | 1.698e-07 | 9.22e-05 |
HSA-MIR-126 | 0.244 | 3.112e-07 | 0.000169 |
HSA-MIR-2115 | 0.2447 | 8.293e-07 | 0.000449 |
HSA-MIR-320B-2 | 0.2333 | 1.033e-06 | 0.000558 |
HSA-MIR-16-1 | 0.2189 | 4.746e-06 | 0.00256 |
GENDER | Labels | N |
FEMALE | 192 | |
MALE | 238 | |
Significant markers | N = 0 |
19 miRs related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 87.74 (12) |
Significant markers | N = 19 | |
pos. correlated | 1 | |
neg. correlated | 18 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-222 | -0.255 | 4.838e-05 | 0.0265 |
HSA-MIR-148A | -0.2524 | 5.828e-05 | 0.0319 |
HSA-MIR-181A-2 | 0.2499 | 6.924e-05 | 0.0378 |
HSA-MIR-432 | -0.2466 | 8.694e-05 | 0.0474 |
HSA-MIR-135B | -0.2634 | 0.0001036 | 0.0564 |
HSA-MIR-493 | -0.2455 | 0.0001178 | 0.064 |
HSA-MIR-187 | -0.2377 | 0.0002279 | 0.124 |
HSA-MIR-103-2 | -0.2318 | 0.0002312 | 0.125 |
HSA-MIR-134 | -0.2294 | 0.0002691 | 0.145 |
HSA-MIR-337 | -0.2287 | 0.0002819 | 0.152 |
HISTOLOGICAL.TYPE | Labels | N |
ASTROCYTOMA | 158 | |
OLIGOASTROCYTOMA | 106 | |
OLIGODENDROGLIOMA | 166 | |
Significant markers | N = 107 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1262 | 1.592e-17 | 8.72e-15 |
HSA-MIR-186 | 4.075e-16 | 2.23e-13 |
HSA-MIR-219-1 | 1.346e-15 | 7.35e-13 |
HSA-MIR-592 | 3.202e-15 | 1.75e-12 |
HSA-MIR-576 | 6.813e-15 | 3.71e-12 |
HSA-MIR-3065 | 1.183e-14 | 6.42e-12 |
HSA-MIR-3074 | 3.451e-13 | 1.87e-10 |
HSA-MIR-505 | 3.619e-13 | 1.96e-10 |
HSA-MIR-301A | 1.691e-12 | 9.13e-10 |
HSA-MIR-21 | 3.645e-12 | 1.96e-09 |
56 miRs related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 88 | |
YES | 342 | |
Significant markers | N = 56 | |
Higher in YES | 56 | |
Higher in NO | 0 |
W(pos if higher in 'YES') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
HSA-MIR-1274B | 7213 | 2.004e-12 | 1.1e-09 | 0.7447 |
HSA-MIR-628 | 8403 | 1.649e-10 | 9.02e-08 | 0.7208 |
HSA-MIR-3130-1 | 7170 | 3.865e-10 | 2.11e-07 | 0.7242 |
HSA-MIR-9-3 | 21069 | 7.01e-09 | 3.82e-06 | 0.7001 |
HSA-MIR-30E | 9290 | 3.064e-08 | 1.67e-05 | 0.6913 |
HSA-MIR-331 | 9333 | 3.877e-08 | 2.11e-05 | 0.6899 |
HSA-MIR-3677 | 9602 | 1.626e-07 | 8.82e-05 | 0.681 |
HSA-MIR-424 | 9640 | 1.981e-07 | 0.000107 | 0.6797 |
HSA-MIR-296 | 9868 | 6.3e-07 | 0.00034 | 0.6721 |
HSA-MIR-500A | 20228 | 6.3e-07 | 0.00034 | 0.6721 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 1 | |
ASIAN | 6 | |
BLACK OR AFRICAN AMERICAN | 14 | |
WHITE | 400 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
---|---|---|
HSA-MIR-1304 | 6.547e-05 | 0.0359 |
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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 = 430
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Number of miRs = 548
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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.