This pipeline uses various statistical tests to identify selected clinical features related to mutation rate.
Testing the association between 2 variables and 11 clinical features across 69 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 'AGE_mutation.rate'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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2 variables correlated to 'HISTOLOGICAL.TYPE'.
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MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
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No variables correlated to 'Time to Death', 'AGE', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'GENDER', 'COMPLETENESS.OF.RESECTION', and 'NUMBER.OF.LYMPH.NODES'.
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=2 | ||||
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 | Wilcoxon test | N=0 | ||||
GENDER | Wilcoxon test | N=0 | ||||
HISTOLOGICAL TYPE | Wilcoxon test | N=2 | rectal mucinous adenocarcinoma | N=2 | rectal adenocarcinoma | N=0 |
COMPLETENESS OF RESECTION | Kruskal-Wallis test | N=0 | ||||
NUMBER OF LYMPH NODES | Spearman correlation test | N=0 |
Time to Death | Duration (Months) | 1-117.1 (median=17) |
censored | N = 54 | |
death | N = 9 | |
Significant variables | N = 0 |
AGE | Mean (SD) | 66.32 (11) |
Significant variables | N = 0 |
AGE | Mean (SD) | 66.32 (11) |
Significant variables | N = 2 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 18 | |
STAGE II | 6 | |
STAGE IIA | 19 | |
STAGE III | 4 | |
STAGE IIIB | 8 | |
STAGE IIIC | 4 | |
STAGE IV | 10 | |
Significant variables | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 2.7 (0.67) |
N | ||
1 | 5 | |
2 | 14 | |
3 | 47 | |
4 | 3 | |
Significant variables | N = 0 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.55 (0.78) |
N | ||
0 | 43 | |
1 | 14 | |
2 | 12 | |
Significant variables | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 59 | |
M1 | 10 | |
Significant variables | N = 0 |
GENDER | Labels | N |
FEMALE | 30 | |
MALE | 39 | |
Significant variables | N = 0 |
HISTOLOGICAL.TYPE | Labels | N |
RECTAL ADENOCARCINOMA | 57 | |
RECTAL MUCINOUS ADENOCARCINOMA | 8 | |
Significant variables | N = 2 | |
Higher in RECTAL MUCINOUS ADENOCARCINOMA | 2 | |
Higher in RECTAL ADENOCARCINOMA | 0 |
No variable related to 'COMPLETENESS.OF.RESECTION'.
COMPLETENESS.OF.RESECTION | Labels | N |
R0 | 59 | |
R1 | 1 | |
R2 | 8 | |
Significant variables | N = 0 |
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Expresson data file = READ-TP.patients.counts_and_rates.txt
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Clinical data file = READ-TP.merged_data.txt
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Number of patients = 69
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Number of variables = 2
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Number of clinical features = 11
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.