This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 524 miRs and 5 clinical features across 132 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one miRs.
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1 miR correlated to 'AGE'.
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HSA-MIR-3065
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No miRs correlated to 'Time to Death', 'GENDER', 'RACE', and 'ETHNICITY'.
Complete statistical result table is provided in Supplement Table 1
Table 1. Get Full Table This table shows the clinical features, statistical methods used, and the number of miRs that are significantly associated with each clinical feature at P value < 0.05 and Q value < 0.3.
| Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
|---|---|---|---|---|---|---|
| Time to Death | Cox regression test | N=0 | ||||
| AGE | Spearman correlation test | N=1 | older | N=1 | younger | N=0 |
| GENDER | Wilcoxon test | N=0 | ||||
| RACE | Kruskal-Wallis test | N=0 | ||||
| ETHNICITY | Wilcoxon test | N=0 |
Table S1. Basic characteristics of clinical feature: 'Time to Death'
| Time to Death | Duration (Months) | 0.1-303.1 (median=16.9) |
| censored | N = 128 | |
| death | N = 4 | |
| Significant markers | N = 0 |
Table S2. Basic characteristics of clinical feature: 'AGE'
| AGE | Mean (SD) | 48.8 (16) |
| Significant markers | N = 1 | |
| pos. correlated | 1 | |
| neg. correlated | 0 |
Table S3. Get Full Table List of one miR significantly correlated to 'AGE' by Spearman correlation test
| SpearmanCorr | corrP | Q | |
|---|---|---|---|
| HSA-MIR-3065 | 0.3129 | 0.0002589 | 0.136 |
Table S4. Basic characteristics of clinical feature: 'GENDER'
| GENDER | Labels | N |
| FEMALE | 75 | |
| MALE | 57 | |
| Significant markers | N = 0 |
Table S5. Basic characteristics of clinical feature: 'RACE'
| RACE | Labels | N |
| AMERICAN INDIAN OR ALASKA NATIVE | 1 | |
| ASIAN | 5 | |
| BLACK OR AFRICAN AMERICAN | 9 | |
| WHITE | 114 | |
| Significant markers | N = 0 |
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Expresson data file = PCPG-TP.miRseq_RPKM_log2.txt
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Clinical data file = PCPG-TP.merged_data.txt
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Number of patients = 132
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Number of miRs = 524
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Number of clinical features = 5
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.