This pipeline uses various statistical tests to identify miRs whose expression levels correlated to selected clinical features.
Testing the association between 469 genes and 5 clinical features across 25 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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1 gene correlated to 'LYMPH.NODE.METASTASIS'.
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HSA-MIR-1537
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No genes correlated to 'AGE', 'GENDER', 'DISTANT.METASTASIS', and 'NEOPLASM.DISEASESTAGE'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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AGE | Spearman correlation test | N=0 | ||||
GENDER | t test | N=0 | ||||
DISTANT METASTASIS | ANOVA test | N=0 | ||||
LYMPH NODE METASTASIS | ANOVA test | N=1 | ||||
NEOPLASM DISEASESTAGE | ANOVA test | N=0 |
AGE | Mean (SD) | 52.32 (15) |
Significant markers | N = 0 |
GENDER | Labels | N |
FEMALE | 11 | |
MALE | 14 | |
Significant markers | N = 0 |
DISTANT.METASTASIS | Labels | N |
M0 | 2 | |
M1 | 2 | |
MX | 3 | |
Significant markers | N = 0 |
LYMPH.NODE.METASTASIS | Labels | N |
N0 | 13 | |
N1 | 2 | |
NX | 10 | |
Significant markers | N = 1 |
ANOVA_P | Q | |
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HSA-MIR-1537 | 0.0001046 | 0.049 |
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Expresson data file = KICH-TP.miRseq_RPKM_log2.txt
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Clinical data file = KICH-TP.clin.merged.picked.txt
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Number of patients = 25
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Number of genes = 469
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Number of clinical features = 5
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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.