(primary blood tumor (peripheral) cohort)
This pipeline uses various statistical tests to identify genes whose promoter methylation levels correlated to selected clinical features.
Testing the association between 16403 genes and 3 clinical features across 169 samples, statistically thresholded by Q value < 0.05, 3 clinical features related to at least one genes.
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2 genes correlated to 'Time to Death'.
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AGRN , C10ORF128
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51 genes correlated to 'AGE'.
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JAKMIP1 , TMEM20 , KIAA1377 , DENND2C , TBC1D12 , ...
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7 genes correlated to 'GENDER'.
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FAM35A , AP2B1 , DKFZP434L187 , KIF4B , CROCC , ...
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=2 | shorter survival | N=1 | longer survival | N=1 |
AGE | Spearman correlation test | N=51 | older | N=7 | younger | N=44 |
GENDER | t test | N=7 | male | N=3 | female | N=4 |
Time to Death | Duration (Months) | 0.9-94.1 (median=12) |
censored | N = 56 | |
death | N = 89 | |
Significant markers | N = 2 | |
associated with shorter survival | 1 | |
associated with longer survival | 1 |
HazardRatio | Wald_P | Q | C_index | |
---|---|---|---|---|
AGRN | 49000001 | 5.388e-08 | 0.00088 | 0.542 |
C10ORF128 | 0.13 | 1.953e-06 | 0.032 | 0.379 |
AGE | Mean (SD) | 55.38 (16) |
Significant markers | N = 51 | |
pos. correlated | 7 | |
neg. correlated | 44 |
SpearmanCorr | corrP | Q | |
---|---|---|---|
JAKMIP1 | -0.4888 | 1.562e-11 | 2.56e-07 |
TMEM20 | -0.4812 | 3.546e-11 | 5.82e-07 |
KIAA1377 | -0.4477 | 1.042e-09 | 1.71e-05 |
DENND2C | -0.4249 | 8.506e-09 | 0.000139 |
TBC1D12 | -0.416 | 1.864e-08 | 0.000306 |
CD96 | 0.4045 | 4.918e-08 | 0.000806 |
CBLN3 | -0.3976 | 8.63e-08 | 0.00141 |
KHNYN | -0.3976 | 8.63e-08 | 0.00141 |
SCN3B | -0.395 | 1.072e-07 | 0.00176 |
AASS | -0.3933 | 1.227e-07 | 0.00201 |
GENDER | Labels | N |
FEMALE | 79 | |
MALE | 90 | |
Significant markers | N = 7 | |
Higher in MALE | 3 | |
Higher in FEMALE | 4 |
T(pos if higher in 'MALE') | ttestP | Q | AUC | |
---|---|---|---|---|
FAM35A | -12.21 | 9.524e-25 | 1.56e-20 | 0.8895 |
AP2B1 | -11.45 | 2.264e-22 | 3.71e-18 | 0.8904 |
DKFZP434L187 | 11.05 | 2.352e-21 | 3.86e-17 | 0.9304 |
KIF4B | -10.96 | 1.176e-20 | 1.93e-16 | 0.8591 |
CROCC | -9.83 | 3.226e-18 | 5.29e-14 | 0.8857 |
LOC389791 | 5.32 | 3.266e-07 | 0.00536 | 0.7158 |
ATP5J | 4.91 | 2.338e-06 | 0.0383 | 0.7331 |
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Expresson data file = LAML-TB.meth.for_correlation.filtered_data.txt
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Clinical data file = LAML-TB.clin.merged.picked.txt
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Number of patients = 169
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Number of genes = 16403
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Number of clinical features = 3
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 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.
This is an experimental feature. The full results of the analysis summarized in this report can be downloaded from the TCGA Data Coordination Center.