Content Comparison

h5. Batch vs clinical traits

Number of clinical traits: 31

Number of batches based on DNA methylation data: 19

Relationship between batch and the center:
{code:collapse=true}> table(batchID,two)
       two
batchID 02 06 08 12 14 15 16 19 26 27 28 32 41 74 76 81 87
   0186 25  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0199 17  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0218  0 28  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0242  0 53  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0279 34 17 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0287 26 16 15  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0297 13  0 19  0  0  0  0  0  0  0  0  0  0  0  0  0  0
   0314  2  7  6  5  0  0  0  0  0  0  0  0  0  0  0  0  0
   0337  0 10  0 16  0  1  0  0  0  0  0  0  0  0  0  0  0
   0392  0  7  0 10  6  0  5  0  0  0  0  0  0  0  0  0  0
   0521  0  4  0 12 12  3  7  6  3  0  0  0  0  0  0  0  0
   0595  0  4  0  4 13  0  1 10  1  4  9  0  0  0  0  0  0
   0788  5 12  0  0  3  0  0  0  0 13 10  4  0  0  0  0  0
   0915  0  0  0  9  2  0  0  9  0  0  0  5  4  0  0  0  0
   1228  0  1  0  0  2  0  1  3  1  0  0 15  4  0  0  0  0
   1481  0  5  0  3  0  0  0  0  6  0 11  1  0  0  9  0  0
   1697  0 13  0  0  1  1  0 10  1  0  3  2  1  0  5  1  1
   1844  0 10  0  0  5  0  0  1  2  0  1  0  1  6 10  1  0
   2004  0  7  0  0  3  0  0  4  0  0  0  0  0  0  0  0  0{code}
Relationship between batch and clinical variable, significant correlations (entire table can be found [here|^BatchClinicalInfoCorrelationsGBM.txt])
{csv}GBM_clinical,DataType,NumberOfNAs,Test,Pvalue
year_of_initial_pathologic_diagnosis,integer,35,Kruskal-Wallis rank sum test,1.34E-64
pretreatment_history,factor,35,Pearson's Chi-squared test,2.98E-29
histological_type,factor,35,Pearson's Chi-squared test,6.11E-20
initial_pathologic_diagnosis_method,factor,37,Pearson's Chi-squared test,5.24E-15
vital_status,factor,36,Pearson's Chi-squared test,8.36E-15
hormonal_therapy,factor,59,Pearson's Chi-squared test,6.50E-14
targeted_molecular_therapy,factor,65,Pearson's Chi-squared test,5.80E-10
additional_pharmaceutical_therapy,factor,72,Pearson's Chi-squared test,4.19E-05
additional_drug_therapy,factor,73,Pearson's Chi-squared test,5.08E-05
days_to_last_followup,integer,35,Kruskal-Wallis rank sum test,2.90E-04
person_neoplasm_cancer_status,factor,88,Pearson's Chi-squared test,4.81E-04
additional_chemo_therapy,factor,106,Pearson's Chi-squared test,5.18E-03
days_to_death,integer,169,Kruskal-Wallis rank sum test,6.30E-03
days_to_birth,integer,35,Kruskal-Wallis rank sum test,1.13E-02
age_at_initial_pathologic_diagnosis,integer,35,Kruskal-Wallis rank sum test,1.20E-02{csv}

h5. Survival vs batch 

Code for automatic analysis of survival and correlation with clinical traits can be found [here|^SurvivalBasicAnalysis.R]. 

Kaplan Meier Curve and survival by batch:
!KaplanMeierCurveGBM.png|thumbnail!  !SurvivalByBatchGBM.png|thumbnail!
Summary of the cox proportional hazards model can be found [here|^SurvivalBatchSummaryStatisticsGBM.txt], batch shows significant correlation with survival (Likelihood ratio test= 31 on 17 df, p=0.02; Wald test = 28.17  on 17 df, p=0.04297; Score (logrank) test = 29.48  on 17 df, p=0.03035)