This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 12 clinical features across 130 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 1 clinical feature related to at least one variables.
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1 variable correlated to 'KARNOFSKY.PERFORMANCE.SCORE'.
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MUTATIONRATE_NONSYNONYMOUS
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No variables correlated to 'Time to Death', 'AGE', 'AGE_mutation.rate', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'NUMBERPACKYEARSSMOKED', 'NUMBER.OF.LYMPH.NODES', and 'RACE'.
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=0 | ||||
AGE | Linear Regression Analysis | N=0 | ||||
NEOPLASM DISEASESTAGE | Kruskal-Wallis test | N=0 | ||||
PATHOLOGY T STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY N STAGE | Spearman correlation test | N=0 | ||||
PATHOLOGY M STAGE | Kruskal-Wallis test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=1 | higher score | N=1 | lower score | N=0 |
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 | ||||
RACE | Kruskal-Wallis test | N=0 |
Time to Death | Duration (Months) | 0.1-140.8 (median=8.9) |
censored | N = 81 | |
death | N = 44 | |
Significant variables | N = 0 |
AGE | Mean (SD) | 67.91 (10) |
Significant variables | N = 0 |
AGE | Mean (SD) | 67.91 (10) |
Significant variables | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 1 | |
STAGE II | 37 | |
STAGE III | 44 | |
STAGE IV | 44 | |
Significant variables | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.87 (0.66) |
N | ||
1 | 1 | |
2 | 30 | |
3 | 65 | |
4 | 17 | |
Significant variables | N = 0 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.68 (0.97) |
N | ||
0 | 74 | |
1 | 13 | |
2 | 24 | |
3 | 6 | |
Significant variables | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 76 | |
M1 | 4 | |
MX | 49 | |
Significant variables | N = 0 |
GENDER | Labels | N |
FEMALE | 32 | |
MALE | 98 | |
Significant variables | N = 0 |
One variable related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 78.38 (16) |
Significant variables | N = 1 | |
pos. correlated | 1 | |
neg. correlated | 0 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
MUTATIONRATE_NONSYNONYMOUS | 0.3288 | 0.0469 | 0.0938 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 37.14 (25) |
Significant variables | N = 0 |
NUMBER.OF.LYMPH.NODES | Mean (SD) | 1.81 (3.8) |
Significant variables | N = 0 |
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Expresson data file = BLCA-TP.patients.counts_and_rates.txt
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Clinical data file = BLCA-TP.merged_data.txt
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Number of patients = 130
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Number of variables = 2
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Number of clinical features = 12
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.