UCEC uterine corpus endometrioid carcinoma

Batch vs clinical traits

Number of clinical traits: 57, number of theoretical DNA methylation batches: 20

Correlation of batch with the center

> table(batchID, center)
       center
batchID A5 AJ AP AW AX B5 BG BK BS D1 DF DI E6 EC EO EY FI H5
   A00U  0  0  9  0 13  0  0  2  0  0  0  0  0  0  0  0  0  0
   A039 18  0 13  0  2  7  6  0  0  0  0  0  0  0  0  0  0  0
   A105  7  0  4  0  1  7 13  1 14  0  0  0  0  0  0  0  0  0
   A10A  1  0  0  0  1  0  3  0  3  0  0  0  0  0  0  0  0  0
   A10N  0  0  0  0  1  7  3  0  1  8  0  0  0  0  0  0  0  0
   A10Q  7  0  4  0  1  7 13  1 14  0  0  0  0  0  0  0  0  0
   A123  4  0  4  0  4 13  4  3  4 11  0  0  0  0  0  0  0  0
   A12K  0  0  0  0  0  0  5  0  1 40  0  1  0  0  0  0  0  0
   A138  0  0 12  0 15  0  0  0  0  0  0  3  0  0  0  0  0  0
   A13K  0  0  0  0  0  2  0  0  0  0  0  0  0  0  0 20  0  0
   A145  0  0  0  0  0  4  0  0  0  0  0  0  2  0  0  1  0  0
   A14H  3  0  1  0  0  5  0  0  2  6  0  0  1  1  0  2  0  0
   A14N  1  0  0  0  0  2  0  0  0  1  0  0  0  0  0  2  0  0
   A161  0  2  0  1  0  0  3  0  0  0  0  1  0  0  3  2  0  0
   A16G  0  0  0  0  0  0  2  1  0  2  0  1  0  2  0  0  0  0
   A17F  0  0  0  0  9  1  1  0  0  4  0  0  0  0  0  0 13  0
   A17H  0  0  0  0  1  0  0  0  0  0  0  0  0  0  0  0  0  0
   A17Z  3  0  0  0  3  0  0  0  0  1  5  0  0  0  5  0  0  1
   A18O  2  5  0  0  3  0  1  0  0  0  1  1  0  0  3  2  1  0
   A19Z  0  6  0  0  0  4  1  0  0  2  0  2  2  0  7  3  0  0

Correlation with clinical traits (complete table is here)

Unknown macro: {csv}

UCEC,DataType,NumberOfNAs,Test,Pvalue
histological_type,factor,69,Pearson's Chi-squared test,9.48E-27
year_of_initial_pathologic_diagnosis,integer,68,Kruskal-Wallis rank sum test,1.18E-17
days_to_form_completion,integer,68,Kruskal-Wallis rank sum test,3.78E-15
prosp_tissue_coll,factor,68,Pearson's Chi-squared test,9.22E-15
retro_tissue_coll,factor,68,Pearson's Chi-squared test,6.46E-14
tumor_grade,factor,73,Pearson's Chi-squared test,9.17E-11
days_to_last_followup,integer,84,Kruskal-Wallis rank sum test,1.15E-10
surgical_approach,factor,72,Pearson's Chi-squared test,5.31E-04
followup_met_assessment_outcome_success_margin_status,factor,126,Pearson's Chi-squared test,1.07E-03
total_pelv_lnr,integer,80,Kruskal-Wallis rank sum test,1.21E-03
first_pathologic_diagnosis_biospecimen_acquisition_method_type,factor,70,Pearson's Chi-squared test,1.53E-03
peritoneal_wash,factor,85,Pearson's Chi-squared test,1.11E-02
vital_status,factor,68,Pearson's Chi-squared test,2.98E-02
days_to_birth,integer,68,Kruskal-Wallis rank sum test,6.14E-02
age_at_initial_pathologic_diagnosis,integer,68,Kruskal-Wallis rank sum test,6.22E-02
person_neoplasm_cancer_status,factor,97,Pearson's Chi-squared test,6.44E-02
total_aor.lnp,integer,137,Kruskal-Wallis rank sum test,6.49E-02
total_aor_lnr,integer,83,Kruskal-Wallis rank sum test,8.01E-02
weight,integer,72,Kruskal-Wallis rank sum test,8.12E-02

Batch vs survival

Call:
coxph(formula = survivalObject ~ batchVector)

  n= 369, number of events= 29

                      coef  exp(coef)   se(coef)      z Pr(>|z|)
batchVectorA039  3.652e-01  1.441e+00  8.239e-01  0.443   0.6576
batchVectorA105 -4.042e-01  6.675e-01  1.000e+00 -0.404   0.6862
batchVectorA10A -1.746e+01  2.609e-08  8.054e+03 -0.002   0.9983
batchVectorA10N -1.750e+01  2.510e-08  6.350e+03 -0.003   0.9978
batchVectorA123 -5.327e-02  9.481e-01  9.163e-01 -0.058   0.9536
batchVectorA12K  1.225e+00  3.405e+00  8.810e-01  1.391   0.1643
batchVectorA138 -7.666e-01  4.646e-01  1.225e+00 -0.626   0.5315
batchVectorA13K  1.542e+00  4.675e+00  9.249e-01  1.667   0.0954 .
batchVectorA145 -1.745e+01  2.644e-08  1.376e+04 -0.001   0.9990
batchVectorA14H -1.650e-01  8.479e-01  1.240e+00 -0.133   0.8942
batchVectorA14N -1.745e+01  2.639e-08  1.486e+04 -0.001   0.9991
batchVectorA161  2.565e+00  1.301e+01  1.286e+00  1.994   0.0461 *
batchVectorA16G -1.744e+01  2.657e-08  1.697e+04 -0.001   0.9992
batchVectorA17F  1.342e+00  3.829e+00  8.677e-01  1.547   0.1218
batchVectorA17Z -1.746e+01  2.602e-08  1.347e+04 -0.001   0.9990
batchVectorA18O  1.955e+00  7.066e+00  1.001e+00  1.953   0.0509 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                exp(coef) exp(-coef) lower .95 upper .95
batchVectorA039 1.441e+00  6.940e-01   0.28660     7.244
batchVectorA105 6.675e-01  1.498e+00   0.09396     4.742
batchVectorA10A 2.609e-08  3.832e+07   0.00000       Inf
batchVectorA10N 2.510e-08  3.985e+07   0.00000       Inf
batchVectorA123 9.481e-01  1.055e+00   0.15737     5.712
batchVectorA12K 3.405e+00  2.937e-01   0.60563    19.143
batchVectorA138 4.646e-01  2.153e+00   0.04209     5.127
batchVectorA13K 4.675e+00  2.139e-01   0.76293    28.647
batchVectorA145 2.644e-08  3.783e+07   0.00000       Inf
batchVectorA14H 8.479e-01  1.179e+00   0.07459     9.639
batchVectorA14N 2.639e-08  3.790e+07   0.00000       Inf
batchVectorA161 1.301e+01  7.689e-02   1.04492   161.869
batchVectorA16G 2.657e-08  3.763e+07   0.00000       Inf
batchVectorA17F 3.829e+00  2.612e-01   0.69893    20.972
batchVectorA17Z 2.602e-08  3.844e+07   0.00000       Inf
batchVectorA18O 7.066e+00  1.415e-01   0.99266    50.292

Rsquare= 0.064   (max possible= 0.545 )
Likelihood ratio test= 24.27  on 16 df,   p=0.08375
Wald test            = 18.5  on 16 df,   p=0.2953
Score (logrank) test = 30.57  on 16 df,   p=0.01524

DNA methylation

27k, M value, didn't split into red and green. Had to remove two arrays that had NA value for unmethylated or methylated probe intensities (TCGA-A5-A0VQ-01A-11D-A10Q-05,TCGA-BS-A0UF-01A-11D-A10Q-05). Ended up with 115 arrays total.