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 165 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 2 clinical features related to at least one variables.
-
2 variables correlated to 'AGE'.
-
MUTATIONRATE_NONSYNONYMOUS , MUTATIONRATE_SILENT
-
2 variables correlated to 'GENDER'.
-
MUTATIONRATE_SILENT , MUTATIONRATE_NONSYNONYMOUS
-
No variables correlated to 'Time to Death', 'AGE_mutation.rate', 'NEOPLASM.DISEASESTAGE', 'PATHOLOGY.T.STAGE', 'PATHOLOGY.N.STAGE', 'PATHOLOGY.M.STAGE', 'KARNOFSKY.PERFORMANCE.SCORE', 'NUMBERPACKYEARSSMOKED', 'RACE', and 'ETHNICITY'.
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=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=2 | male | N=2 | female | N=0 |
KARNOFSKY PERFORMANCE SCORE | Spearman correlation test | N=0 | ||||
NUMBERPACKYEARSSMOKED | Spearman correlation test | N=0 | ||||
RACE | Kruskal-Wallis test | N=0 | ||||
ETHNICITY | Wilcoxon test | N=0 |
Time to Death | Duration (Years) | 2-5925 (median=566) |
censored | N = 137 | |
death | N = 5 | |
Significant variables | N = 0 |
AGE | Mean (SD) | 60.11 (12) |
Significant variables | N = 2 | |
pos. correlated | 2 | |
neg. correlated | 0 |
AGE | Mean (SD) | 60.11 (12) |
Significant variables | N = 0 |
NEOPLASM.DISEASESTAGE | Labels | N |
STAGE I | 95 | |
STAGE II | 10 | |
STAGE III | 39 | |
STAGE IV | 10 | |
Significant variables | N = 0 |
PATHOLOGY.T.STAGE | Mean (SD) | 1.65 (0.89) |
N | ||
1 | 103 | |
2 | 17 | |
3 | 44 | |
4 | 1 | |
Significant variables | N = 0 |
PATHOLOGY.N.STAGE | Mean (SD) | 0.51 (0.65) |
N | ||
0 | 28 | |
1 | 17 | |
2 | 4 | |
Significant variables | N = 0 |
PATHOLOGY.M.STAGE | Labels | N |
M0 | 65 | |
M1 | 6 | |
MX | 81 | |
Significant variables | N = 0 |
GENDER | Labels | N |
FEMALE | 50 | |
MALE | 115 | |
Significant variables | N = 2 | |
Higher in MALE | 2 | |
Higher in FEMALE | 0 |
No variable related to 'KARNOFSKY.PERFORMANCE.SCORE'.
KARNOFSKY.PERFORMANCE.SCORE | Mean (SD) | 88.42 (19) |
Significant variables | N = 0 |
NUMBERPACKYEARSSMOKED | Mean (SD) | 32.15 (48) |
Significant variables | N = 0 |
RACE | Labels | N |
AMERICAN INDIAN OR ALASKA NATIVE | 2 | |
ASIAN | 2 | |
BLACK OR AFRICAN AMERICAN | 43 | |
WHITE | 106 | |
Significant variables | N = 0 |
-
Expresson data file = KIRP-TP.patients.counts_and_rates.txt
-
Clinical data file = KIRP-TP.merged_data.txt
-
Number of patients = 165
-
Number of variables = 2
-
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.