This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 19042 genes and 4 clinical features across 194 samples, statistically thresholded by P value < 0.05 and Q value < 0.3, 3 clinical features related to at least one genes.
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138 genes correlated to 'AGE'.
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TMEM20 , ANGPTL5 , KIAA1377 , JAKMIP1 , AASS , ...
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15 genes correlated to 'GENDER'.
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DKFZP434L187 , AP2B1 , FAM35A , GLUD1 , CROCC , ...
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4 genes correlated to 'RACE'.
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GRAP , FAM66D__2 , LOC392196 , CPNE7
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No genes correlated to 'Time to Death'
Complete statistical result table is provided in Supplement Table 1
Clinical feature | Statistical test | Significant genes | Associated with | Associated with | ||
---|---|---|---|---|---|---|
Time to Death | Cox regression test | N=0 | ||||
AGE | Spearman correlation test | N=138 | older | N=36 | younger | N=102 |
GENDER | Wilcoxon test | N=15 | male | N=15 | female | N=0 |
RACE | Kruskal-Wallis test | N=4 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 63 | |
death | N = 106 | |
Significant markers | N = 0 |
AGE | Mean (SD) | 55.11 (16) |
Significant markers | N = 138 | |
pos. correlated | 36 | |
neg. correlated | 102 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
TMEM20 | -0.4691 | 5.241e-12 | 9.98e-08 |
ANGPTL5 | -0.4301 | 3.881e-10 | 7.39e-06 |
KIAA1377 | -0.4301 | 3.881e-10 | 7.39e-06 |
JAKMIP1 | -0.4275 | 5.054e-10 | 9.62e-06 |
AASS | -0.4139 | 1.989e-09 | 3.79e-05 |
TBC1D12 | -0.3924 | 1.521e-08 | 0.00029 |
CD96 | 0.3869 | 2.5e-08 | 0.000476 |
CAMK2D | -0.3812 | 4.157e-08 | 0.000791 |
CBLN3 | -0.3809 | 4.265e-08 | 0.000812 |
KHNYN | -0.3809 | 4.265e-08 | 0.000812 |
GENDER | Labels | N |
FEMALE | 89 | |
MALE | 105 | |
Significant markers | N = 15 | |
Higher in MALE | 15 | |
Higher in FEMALE | 0 |
W(pos if higher in 'MALE') | wilcoxontestP | Q | AUC | |
---|---|---|---|---|
DKFZP434L187 | 8773 | 6.895e-26 | 1.31e-21 | 0.9388 |
AP2B1 | 935 | 8.831e-22 | 1.68e-17 | 0.8999 |
FAM35A | 968 | 1.999e-21 | 3.81e-17 | 0.8964 |
GLUD1 | 968 | 1.999e-21 | 3.81e-17 | 0.8964 |
CROCC | 1069 | 2.333e-20 | 4.44e-16 | 0.8856 |
KIF4B | 1238 | 1.227e-18 | 2.34e-14 | 0.8675 |
ATP5J | 6871 | 1.696e-08 | 0.000323 | 0.7353 |
GABPA__1 | 6871 | 1.696e-08 | 0.000323 | 0.7353 |
LOC389791__1 | 6643 | 4.296e-07 | 0.00818 | 0.7109 |
PTGES2__1 | 6643 | 4.296e-07 | 0.00818 | 0.7109 |
RACE | Labels | N |
ASIAN | 2 | |
BLACK OR AFRICAN AMERICAN | 13 | |
WHITE | 177 | |
Significant markers | N = 4 |
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Expresson data file = LAML-TB.meth.by_min_clin_corr.data.txt
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Clinical data file = LAML-TB.merged_data.txt
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Number of patients = 194
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Number of genes = 19042
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Number of clinical features = 4
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 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 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 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.