Update glioma classifier to handle missing probes in EPIC v2. Probabl…

…y will need to update the model later. #599

R/classifiers.R

@@ -72,6 +72,12 @@ gliomaClassifier <- function(data){
             }
         }
 
+        # For missing probes add values to 0.5
+        missing_probes <- setdiff(colnames(model$trainingData), colnames(met))
+        missing_probes_matrix <- matrix(rep(0.5, nrow(met) * length(missing_probes)),nrow = nrow(met))
+        colnames(missing_probes_matrix) <- missing_probes
+        aux <- bind_cols(aux,missing_probes_matrix)
+
         pred <- predict(model, aux)
         pred.prob <- predict(model, aux, type = "prob")
         colnames(pred.prob) <- paste0(i,"_", colnames(pred.prob))

vignettes/classifiers.Rmd

@@ -55,25 +55,25 @@ the same result as the paper.
 
 ```{r, eval = FALSE, message = FALSE, results = "hide"}
 query <- GDCquery(
-  project = "TCGA-GBM",
-  data.category = "DNA methylation",
-  barcode = c("TCGA-06-0122","TCGA-14-1456"),
-  platform = "Illumina Human Methylation 27",
-  legacy = TRUE
+    project = "TCGA-GBM",
+    data.category = "DNA Methylation",
+    barcode = c("TCGA-06-0122","TCGA-14-1456"),
+    platform = "Illumina Human Methylation 27",
+    data.type = "Methylation Beta Value"
 )
 GDCdownload(query)
-data.hg19 <- GDCprepare(query)
+dnam <- GDCprepare(query)
 ```
 
 ```{r, eval = FALSE}
-assay(data.hg19)[1:5,1:2]
+assay(dnam)[1:5,1:2]
 ```
 
 ## Function
 <hr>
 
 ```{r, eval = FALSE}
-classification <- gliomaClassifier(data.hg19)
+classification <- gliomaClassifier(dnam)
 ```
 
 ## Results