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Analysis of batch vs clinical traits
Number of clinical traits: 23
Number of batches based on the pattern for DNA methylation samples: 3
Correlation between batch and center in AML:
> table(batchID,two)
two
batchID AB
0741 96
0742 73
0743 25
There is only one center from which all patients come from.
Significant trait - batch correlations (all other correlations can be found in a table here)
Unknown macro: {csv} "LAML_clinical_traits","DataType","NumberOfNAs","Test","Pvalue"
"days_to_form_completion","integer",2,"Kruskal-Wallis rank sum test",1.22E-26
"year_of_initial_pathologic_diagnosis","integer",2,"Kruskal-Wallis rank sum test",2.53E-26
"days_to_death","integer",82,"Kruskal-Wallis rank sum test",3.94E-04
"prior_diagnosis","factor",2,"Pearson's Chi-squared test",1.63E-03
"vital_status","factor",2,"Pearson's Chi-squared test",5.25E-03
"age_at_initial_pathologic_diagnosis","integer",2,"Kruskal-Wallis rank sum test",1.66E-02
"days_to_birth","integer",2,"Kruskal-Wallis rank sum test",1.90E-02
"hydroxyurea_administration_prior_registration_clinical_study_indicator","factor",2,"Pearson's Chi-squared test",3.05E-02
"pretreatment_history","factor",2,"Pearson's Chi-squared test",3.05E-02
Survival analysis
> death<-clinical[,4]
> vital<-clinical[,22]
> fup<-clinical[,7]
> x<-cbind(vital,death,fup)
> rownames(x)<-rownames(clinical)
> dim(x[is.na(x[, 2]) & is.na(x[, 3]), ])
[1] 14 3
> mask <- is.na(x[, 2]) & is.na(x[, 3]) #Exclude patients for whom there is no information for days to death or days to the last follow-up, total of 14 patients
> x1 <- x[!mask, ]
> dim(x1)
[1] 188 3
> status <- rep(1, 188) #create censoring indicator
> status[which(is.na(x1[, 2]))] <- 0
> x1[is.na(x1[, 2]), 2] <- x1[which(is.na(x1[, 2]), 2), 3] #Patients that don't have days to death get days to the last follow-up and status is 0
> x2 <- cbind(x1, status)
> k<-match(rownames(x2),meth[,1])
> methK<-meth[k,]
> library(survival)
Loading required package: splines
> surv <- Surv(as.numeric(x2[, 2]), as.numeric(x2[, 4])) #Create survival object
> plot(survfit(surv ~ 1), xlab = "days to death", ylab = "Probability", main = "Kaplan-Meier survival curve \n for TCGA AML (188 patients)")
> plot(survfit(surv ~ methK[, 2]), xlab = "days to death", ylab = "Probability", col = 1:3, main = "TCGA AML survival correlation with batch")
> legend(2000, 0.6, levels(as.factor(methK[, 2])), text.col = 1:3)
> summary(coxph(surv ~ methK[, 2]))
Call:
coxph(formula = surv ~ methK[, 2])
n= 182, number of events= 116
(6 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
methK[, 2]0742 -0.2311 0.7937 0.2139 -1.080 0.279990
methK[, 2]0743 1.1784 3.2492 0.3541 3.328 0.000874 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
methK[, 2]0742 0.7937 1.2599 0.5219 1.207
methK[, 2]0743 3.2492 0.3078 1.6233 6.504
Concordance= 0.563 (se = 0.026 )
Rsquare= 0.06 (max possible= 0.997 )
Likelihood ratio test= 11.17 on 2 df, p=0.003754
Wald test = 14.35 on 2 df, p=0.0007656
Score (logrank) test = 16.2 on 2 df, p=0.0003037
DNA methylation data analysis
