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@@ -56,15 +56,14 @@ gliomaClassifier <- function(data){
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56
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56
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# keep only probes used in the model
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aux <- met[,colnames(met) %in% colnames(model$trainingData),drop = FALSE]
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59
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-
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# This should not happen!
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if(any(apply(aux,2,function(x) all(is.na(x))))) {
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62
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- print("NA columns")
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+ print("Probes has NA for all samples. Setting to 0.5 since model does not accept NA")
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aux[,apply(aux,2,function(x) all(is.na(x)))] <- 0.5
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64
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}
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if(any(apply(aux,2,function(x) any(is.na(x))))) {
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67
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- print("NA values")
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+ print("Probes has NA values for some samples. Setting values a the median of the sample since model does not accept NA ")
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colMedians <- colMedians(aux,na.rm = TRUE)
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x <- which(is.na(aux),arr.ind = TRUE)
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for(l in 1:nrow(x)){
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...
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@@ -74,9 +73,12 @@ gliomaClassifier <- function(data){
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73
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# For missing probes add values to 0.5
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missing_probes <- setdiff(colnames(model$trainingData), colnames(met))
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77
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- missing_probes_matrix <- matrix(rep(0.5, nrow(met) * length(missing_probes)),nrow = nrow(met))
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78
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- colnames(missing_probes_matrix) <- missing_probes
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79
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- aux <- bind_cols(aux,missing_probes_matrix)
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+ if(length(missing_probes) > 0) {
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+ print("Probes are missing. Setting dummy probes to matrix with 0.5 value to all samples.")
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+ missing_probes_matrix <- matrix(rep(0.5, nrow(met) * length(missing_probes)),nrow = nrow(met))
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+ colnames(missing_probes_matrix) <- missing_probes
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+ aux <- bind_cols(aux,missing_probes_matrix)
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81
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+ }
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80
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82
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pred <- predict(model, aux)
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pred.prob <- predict(model, aux, type = "prob")
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