This pipeline uses various statistical tests to identify miRs whose log2 expression levels correlated to selected clinical features.
Testing the association between 657 miRs and 4 clinical features across 13 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 'PATHOLOGY.T.STAGE'.
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HSA-MIR-3199-2
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No miRs correlated to 'AGE', 'NEOPLASM.DISEASESTAGE', and 'PATHOLOGY.N.STAGE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant miRs | Associated with | Associated with | ||
---|---|---|---|---|---|---|
AGE | Spearman correlation test | N=0 | ||||
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=1 | higher stage | N=0 | lower stage | N=1 |
PATHOLOGY N STAGE | Wilcoxon test | N=0 |
AGE | Mean (SD) | 30.92 (7.4) |
Significant markers | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 2 | |
STAGE IB | 1 | |
STAGE IIA | 1 | |
STAGE IIC | 1 | |
STAGE IIIC | 1 | |
STAGE IS | 7 | |
Significant markers | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 1.69 (0.63) |
N | ||
1 | 5 | |
2 | 7 | |
3 | 1 | |
Significant markers | N = 1 | |
pos. correlated | 0 | |
neg. correlated | 1 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
HSA-MIR-3199-2 | -0.8559 | 0.000382 | 0.25 |
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Expresson data file = TGCT-TP.miRseq_RPKM_log2.txt
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Clinical data file = TGCT-TP.merged_data.txt
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Number of patients = 13
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Number of miRs = 657
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Number of clinical features = 4
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.