This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 502 miRs and 4 clinical features across 113 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one miRs.
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4 miRs correlated to 'Time to Death'.
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HSA-MIR-361 , HSA-MIR-421 , HSA-MIR-17 , HSA-MIR-20A
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2 miRs correlated to 'AGE'.
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HSA-MIR-589 , HSA-MIR-148B
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1 miR correlated to 'GENDER'.
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HSA-MIR-106A
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No miRs correlated to 'RACE'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=4 | shorter survival | N=4 | longer survival | N=0 |
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
GENDER | Wilcoxon test | N=1 | male | N=1 | female | N=0 |
RACE | Kruskal-Wallis test | N=0 |
Time to Death | Duration (Months) | 0.1-143.4 (median=17.8) |
censored | N = 77 | |
death | N = 36 | |
Significant markers | N = 4 | |
associated with shorter survival | 4 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-361 | 2 | 2.353e-05 | 0.012 | 0.716 |
HSA-MIR-421 | 2.1 | 3.255e-05 | 0.016 | 0.732 |
HSA-MIR-17 | 1.89 | 0.0002944 | 0.15 | 0.684 |
HSA-MIR-20A | 1.82 | 0.0004464 | 0.22 | 0.683 |
AGE | Mean (SD) | 61.3 (14) |
Significant markers | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-589 | 0.3336 | 0.0003047 | 0.153 |
HSA-MIR-148B | 0.3316 | 0.0003342 | 0.167 |
GENDER | Labels | N |
FEMALE | 61 | |
MALE | 52 | |
Significant markers | N = 1 | |
Higher in MALE | 1 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
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HSA-MIR-106A | 885 | 5.452e-05 | 0.0274 | 0.721 |
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Expresson data file = SARC-TP.miRseq_RPKM_log2.txt
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Clinical data file = SARC-TP.merged_data.txt
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Number of patients = 113
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Number of miRs = 502
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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.