This pipeline uses various statistical tests to identify mRNAs whose expression levels correlated to selected clinical features.
Testing the association between 17814 genes and 5 clinical features across 54 samples, statistically thresholded by Q value < 0.05, 1 clinical feature related to at least one genes.
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144 genes correlated to 'HISTOLOGICAL.TYPE'.
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KLHL34 , OR4S2 , C19ORF12 , TSP50 , KIAA1324 , ...
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No genes correlated to 'Time to Death', 'AGE', 'RADIATIONS.RADIATION.REGIMENINDICATION', and 'NEOADJUVANT.THERAPY'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
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Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=0 | ||||
HISTOLOGICAL TYPE | ANOVA test | N=144 | ||||
RADIATIONS RADIATION REGIMENINDICATION | t test | N=0 | ||||
NEOADJUVANT THERAPY | t test | N=0 |
Time to Death | Duration (Months) | 6-133.2 (median=35.4) |
censored | N = 47 | |
death | N = 7 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 62.94 (12) |
Significant markers | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
ENDOMETRIOID ENDOMETRIAL ADENOCARCINOMA | 41 | |
MIXED SEROUS AND ENDOMETRIOID | 1 | |
SEROUS ENDOMETRIAL ADENOCARCINOMA | 12 | |
Significant markers | N = 144 |
ANOVA_P | Q | |
---|---|---|
KLHL34 | 8.095e-17 | 1.44e-12 |
OR4S2 | 1.38e-14 | 2.46e-10 |
C19ORF12 | 3.022e-14 | 5.38e-10 |
TSP50 | 2.904e-13 | 5.17e-09 |
KIAA1324 | 2.954e-13 | 5.26e-09 |
PNOC | 9.543e-13 | 1.7e-08 |
FOXA2 | 1.393e-12 | 2.48e-08 |
CDH6 | 2.506e-12 | 4.46e-08 |
CLDN6 | 2.018e-11 | 3.59e-07 |
ANXA10 | 8.888e-11 | 1.58e-06 |
No gene related to 'RADIATIONS.RADIATION.REGIMENINDICATION'.
RADIATIONS.RADIATION.REGIMENINDICATION | Labels | N |
NO | 25 | |
YES | 29 | |
Significant markers | N = 0 |
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Expresson data file = UCEC.medianexp.txt
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Clinical data file = UCEC.clin.merged.picked.txt
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Number of patients = 54
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Number of genes = 17814
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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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.