This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 512 miRs and 7 clinical features across 32 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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3 miRs correlated to 'Time to Death'.
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HSA-MIR-130B , HSA-MIR-671 , HSA-MIR-616
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1 miR correlated to 'AGE'.
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HSA-MIR-3193
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1 miR correlated to 'PATHOLOGY.T.STAGE'.
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HSA-MIR-25
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No miRs correlated to 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', and 'GENDER'.
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=3 | shorter survival | N=3 | longer survival | N=0 |
AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=1 | lower stage | N=0 |
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | Kruskal-Wallis test | N=0 | ||||
GENDER | Wilcoxon test | N=0 |
Time to Death | Duration (Months) | 0.2-51.8 (median=12.8) |
censored | N = 9 | |
death | N = 23 | |
Significant markers | N = 3 | |
associated with shorter survival | 3 | |
associated with longer survival | 0 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
HSA-MIR-130B | 16 | 4.421e-06 | 0.0023 | 0.823 |
HSA-MIR-671 | 5.8 | 7.28e-05 | 0.037 | 0.773 |
HSA-MIR-616 | 2.6 | 0.0001436 | 0.073 | 0.775 |
AGE | Mean (SD) | 64.53 (8) |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-3193 | 0.7571 | 0.0004332 | 0.222 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 2 | |
STAGE IB | 1 | |
STAGE II | 8 | |
STAGE III | 17 | |
STAGE IV | 4 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.34 (0.83) |
N | ||
1 | 5 | |
2 | 13 | |
3 | 12 | |
4 | 2 | |
Significant markers | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-25 | 0.5907 | 0.0003722 | 0.191 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.52 (0.78) |
N | ||
0 | 19 | |
1 | 5 | |
2 | 5 | |
Significant markers | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 26 | |
M1 | 2 | |
MX | 4 | |
Significant markers | N = 0 |
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Expresson data file = MESO-TP.miRseq_RPKM_log2.txt
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Clinical data file = MESO-TP.merged_data.txt
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Number of patients = 32
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Number of miRs = 512
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Number of clinical features = 7
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 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 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.
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.