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 159 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.
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2 variables correlated to 'RACE'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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2 variables correlated to 'ETHNICITY'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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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', 'HISTOLOGICAL.TYPE', and 'COMPLETENESS.OF.RESECTION'.
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant variables | Associated with | Associated with | ||
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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 | Wilcoxon test | N=0 | ||||
PATHOLOGY M STAGE | Kruskal-Wallis test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
HISTOLOGICAL TYPE | Kruskal-Wallis test | N=0 | ||||
COMPLETENESS OF RESECTION | Kruskal-Wallis test | N=0 | ||||
RACE | Kruskal-Wallis test | N=2 | ||||
ETHNICITY | Wilcoxon test | N=2 | not hispanic or latino | N=2 | hispanic or latino | N=0 |
Time to Death | Duration (Months) | 0-113 (median=13.9) |
censored | N = 90 | |
death | N = 65 | |
Significant variables | N = 0 |
AGE | Mean (SD) | 61.46 (14) |
Significant variables | N = 0 |
AGE | Mean (SD) | 61.46 (14) |
Significant variables | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 64 | |
STAGE II | 36 | |
STAGE III | 2 | |
STAGE IIIA | 32 | |
STAGE IIIB | 5 | |
STAGE IIIC | 6 | |
STAGE IVA | 1 | |
STAGE IVB | 2 | |
Significant variables | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 1.96 (0.96) |
N | ||
1 | 67 | |
2 | 40 | |
3 | 42 | |
4 | 9 | |
Significant variables | N = 0 |
PATHOLOGY.N.STAGE | Labels | N |
class0 | 100 | |
class1 | 3 | |
Significant variables | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 122 | |
M1 | 2 | |
MX | 35 | |
Significant variables | N = 0 |
GENDER | Labels | N |
FEMALE | 61 | |
MALE | 98 | |
Significant variables | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
FIBROLAMELLAR CARCINOMA | 1 | |
HEPATOCELLULAR CARCINOMA | 156 | |
HEPATOCHOLANGIOCARCINOMA (MIXED) | 2 | |
Significant variables | N = 0 |
No variable related to 'COMPLETENESS.OF.RESECTION'.
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 132 | |
R1 | 10 | |
RX | 12 | |
Significant variables | N = 0 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 1 | |
ASIAN | 36 | |
BLACK OR AFRICAN AMERICAN | 8 | |
WHITE | 108 | |
Significant variables | N = 2 |
ETHNICITY | Labels | N |
HISPANIC OR LATINO | 4 | |
NOT HISPANIC OR LATINO | 142 | |
Significant variables | N = 2 | |
Higher in NOT HISPANIC OR LATINO | 2 | |
Higher in HISPANIC OR LATINO | 0 |
W(pos if higher in 'NOT HISPANIC OR LATINO') | wilcoxontestP | Q | AUC | |
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MUTATIONRATE_NONSYNONYMOUS | c("490", "0.01376") | c("490", "0.01376") | 0.0275 | 0.8627 |
MUTATIONRATE_SILENT | c("453", "0.04338") | c("453", "0.04338") | 0.0434 | 0.7975 |
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Expresson data file = LIHC-TP.patients.counts_and_rates.txt
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Clinical data file = LIHC-TP.merged_data.txt
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Number of patients = 159
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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.