(primary solid tumor cohort)
This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 18242 genes and 5 clinical features across 28 samples, statistically thresholded by Q value < 0.05, no clinical feature related to at least one genes.
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No genes correlated to 'AGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', 'NUMBERPACKYEARSSMOKED', 'STOPPEDSMOKINGYEAR', and 'TOBACCOSMOKINGHISTORYINDICATOR'.
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 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
STOPPEDSMOKINGYEAR | Spearman correlation test | N=0 | ||||
TOBACCOSMOKINGHISTORYINDICATOR | Spearman correlation test | N=0 |
AGE | Mean (SD) | 48.14 (11) |
Significant markers | N = 0 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 6 | |
YES | 22 | |
Significant markers | N = 0 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 17.83 (12) |
Value | N | |
5 | 1 | |
12 | 1 | |
15 | 2 | |
20 | 1 | |
40 | 1 | |
Significant markers | N = 0 |
STOPPEDSMOKINGYEAR | Mean (SD) | 1994.67 (17) |
Value | N | |
1978 | 1 | |
1995 | 1 | |
2011 | 1 | |
Significant markers | N = 0 |
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Expresson data file = CESC-TP.uncv2.mRNAseq_RSEM_normalized_log2.txt
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Clinical data file = CESC-TP.clin.merged.picked.txt
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Number of patients = 28
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Number of genes = 18242
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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 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.