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Correlation with clinical traits (complete table is here)
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{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{csv} |
Batch vs survival
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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 .
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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
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