library(cmlmanuscript) data("deg_seset_targetaml") dim(deg.seset)
This document describes analysis for the manuscript "Consensus Machine Learning for Gene Target Selection in Pediatric AML Risk" and utilizes the corresponding package cmlmanuscript
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dim(deg.seset)
table(deg.seset$Risk.group)
We defined binary risk group from risk group as follows.
deg.seset$deg.risk <- ifelse(deg.seset$Risk.group=="Low", 0, ifelse(deg.seset$Risk.group %in% c("Standard","High"),1,"NA")) table(deg.seset$deg.risk) message("table of risk group x binarized risk group") table(deg.seset$deg.risk, deg.seset$Risk.group)
We filtered samples according to available risk group status.
degfilt.se <- deg.seset[,which(deg.seset$deg.risk %in% c(0,1))] # subset on deg risk group available message("dim of filtered se object") dim(degfilt.se)
Next, we checked for confounding from demographic variables (age and sex) among binary risk group. This ensured age and sex do not confound the binary risk group variable.
# summarize gender and age at first diagnosis message("table of gender x binarized risk") table(degfilt.se$Gender,degfilt.se$deg.risk) message("chisq test of gender x binarized risk") chisq.test(table(degfilt.se$Gender,degfilt.se$deg.risk)) # p-value = 0.8044, gender evenly dist degfilt.se$binom.age <- ifelse(degfilt.se$Age.at.Diagnosis.in.Days >= median(degfilt.se$Age.at.Diagnosis.in.Days), "old" ,"young") message("table of binarized age-at-diag x binarized risk") table(degfilt.se$binom.age,degfilt.se$deg.risk) message("chisq results of binarized age-at-diag x binarized risk") chisq.test(table(degfilt.se$binom.age,degfilt.se$deg.risk))
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