This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 8 clinical features across 81 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 4 clinical features related to at least one variables.
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2 variables correlated to 'AGE'.
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MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS
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2 variables correlated to 'NEOPLASM.DISEASESTAGE'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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2 variables correlated to 'PATHOLOGY.T.STAGE'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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1 variable correlated to 'ETHNICITY'.
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MUTATIONRATE_NONSYNONYMOUS
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No variables correlated to 'Time to Death', 'AGE_mutation.rate', 'PATHOLOGY.N.STAGE', and 'GENDER'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant variables | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=2 | older | N=2 | younger | N=0 |
AGE | Linear Regression Analysis | N=0 | ||||
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=2 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=2 | higher stage | N=2 | lower stage | N=0 |
PATHOLOGY N STAGE | Wilcoxon test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
ETHNICITY | Wilcoxon test | N=1 | not hispanic or latino | N=1 | hispanic or latino | N=0 |
Time to Death | Duration (Months) | 4.1-153.6 (median=30.8) |
censored | N = 52 | |
death | N = 28 | |
Significant variables | N = 0 |
AGE | Mean (SD) | 47.19 (16) |
Significant variables | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
AGE | Mean (SD) | 47.19 (16) |
Significant variables | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 8 | |
STAGE II | 33 | |
STAGE III | 18 | |
STAGE IV | 17 | |
Significant variables | N = 2 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.54 (0.99) |
N | ||
1 | 8 | |
2 | 38 | |
3 | 11 | |
4 | 19 | |
Significant variables | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 67 | |
class1 | 10 | |
Significant variables | N = 0 |
GENDER | Labels | N |
FEMALE | 53 | |
MALE | 28 | |
Significant variables | N = 0 |
ETHNICITY | Labels | N |
HISPANIC OR LATINO | 8 | |
NOT HISPANIC OR LATINO | 29 | |
Significant variables | N = 1 | |
Higher in NOT HISPANIC OR LATINO | 1 | |
Higher in HISPANIC OR LATINO | 0 |
W(pos if higher in 'NOT HISPANIC OR LATINO') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
MUTATIONRATE_NONSYNONYMOUS | c("60", "0.0406") | c("60", "0.0406") | 0.0812 | 0.7414 |
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Expresson data file = ACC-TP.patients.counts_and_rates.txt
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Clinical data file = ACC-TP.merged_data.txt
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Number of patients = 81
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Number of variables = 2
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Number of clinical features = 8
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.
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.