@@ -116,7 +116,7 @@ setMethod("crossValidate", "DataFrame",

               }

               # Which data-types or data-views are present?

-              assayIDs <- unique(mcols(measurements)$assay)

+              assayIDs <- unique(S4Vectors::mcols(measurements)$assay)

               if(is.null(assayIDs)) assayIDs <- 1

               # Check that other variables are in the right format and fix

@@ -158,7 +158,7 @@ setMethod("crossValidate", "DataFrame",

                                   # Loop over selectors

                                   set.seed(seed)

                                   measurementsUse <- measurements

-                                  if(assayIndex != 1) measurementsUse <- measurements[, mcols(measurements)[, "assay"] == assayIndex, drop = FALSE]

+                                  if(assayIndex != 1) measurementsUse <- measurements[, S4Vectors::mcols(measurements)[, "assay"] == assayIndex, drop = FALSE]

                                   CV(

                                       measurements = measurementsUse, outcome = outcome,

                                       assayIDs = assayIndex,

@@ -196,7 +196,7 @@ setMethod("crossValidate", "DataFrame",

                   if(!is.list(assayCombinations) && assayCombinations[1] == "all") assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

                   result <- sapply(assayCombinations, function(assayIndex){

-                      CV(measurements = measurements[, mcols(measurements)[["assay"]] %in% assayIndex],

+                      CV(measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIndex],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

                          selectionMethod = selectionMethod[assayIndex],

@@ -229,7 +229,7 @@ setMethod("crossValidate", "DataFrame",

                   }

                   result <- sapply(assayCombinations, function(assayIndex){

-                      CV(measurements = measurements[, mcols(measurements)[["assay"]] %in% assayIndex],

+                      CV(measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIndex],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

                          selectionMethod = selectionMethod[assayIndex],

@@ -263,7 +263,7 @@ setMethod("crossValidate", "DataFrame",

                   result <- sapply(assayCombinations, function(assayIndex){

-                      CV(measurements = measurements[, mcols(measurements)$assay %in% assayIndex],

+                      CV(measurements = measurements[, S4Vectors::mcols(measurements)$assay %in% assayIndex],

                          outcome = outcome, assayIDs = assayIndex,

                          nFeatures = nFeatures[assayIndex],

                          selectionMethod = selectionMethod[assayIndex],

@@ -427,14 +427,14 @@ setMethod("crossValidate", "list",

               df_list <- sapply(measurements, S4Vectors::DataFrame, check.names = FALSE)

               df_list <- mapply(function(meas, nam){

-                  mcols(meas)$assay <- nam

-                  mcols(meas)$feature <- colnames(meas)

+                  S4Vectors::mcols(meas)$assay <- nam

+                  S4Vectors::mcols(meas)$feature <- colnames(meas)

                   meas

               }, df_list, names(df_list))

               combined_df <- do.call("cbind", df_list) 

-              colnames(combined_df) <- mcols(combined_df)$feature

+              colnames(combined_df) <- S4Vectors::mcols(combined_df)$feature

@@ -459,8 +459,8 @@ setMethod("crossValidate", "list",

 ######################################

 cleanNFeatures <- function(nFeatures, measurements){

     #### Clean up

-    if(!is.null(mcols(measurements)$assay))

-      obsFeatures <- unlist(as.list(table(mcols(measurements)[, "assay"])))

+    if(!is.null(S4Vectors::mcols(measurements)$assay))

+      obsFeatures <- unlist(as.list(table(S4Vectors::mcols(measurements)[, "assay"])))

     else obsFeatures <- ncol(measurements)

     if(is.null(nFeatures) || length(nFeatures) == 1 && nFeatures == "all") nFeatures <- as.list(obsFeatures)

     if(is.null(names(nFeatures)) && length(nFeatures) == 1) nFeatures <- as.list(pmin(obsFeatures, nFeatures))

@@ -476,8 +476,8 @@ cleanNFeatures <- function(nFeatures, measurements){

 ######################################

 cleanSelectionMethod <- function(selectionMethod, measurements){

     #### Clean up

-    if(!is.null(mcols(measurements)$assay))

-      obsFeatures <- unlist(as.list(table(mcols(measurements)[, "assay"])))

+    if(!is.null(S4Vectors::mcols(measurements)$assay))

+      obsFeatures <- unlist(as.list(table(S4Vectors::mcols(measurements)[, "assay"])))

     else return(list(selectionMethod))

     if(is.null(names(selectionMethod)) & length(selectionMethod) == 1 & !is.null(names(obsFeatures))) selectionMethod <- sapply(names(obsFeatures), function(x) selectionMethod, simplify = FALSE)

@@ -492,8 +492,8 @@ cleanSelectionMethod <- function(selectionMethod, measurements){

 ######################################

 cleanClassifier <- function(classifier, measurements, nFeatures){

     #### Clean up

-    if(!is.null(mcols(measurements)$assay))

-      obsFeatures <- unlist(as.list(table(mcols(measurements)[, "assay"])))

+    if(!is.null(S4Vectors::mcols(measurements)$assay))

+      obsFeatures <- unlist(as.list(table(S4Vectors::mcols(measurements)[, "assay"])))

     else return(list(classifier))

     if(is.null(names(classifier)) & length(classifier) == 1 & !is.null(names(obsFeatures))) classifier <- sapply(names(obsFeatures), function(x)classifier, simplify = FALSE)

@@ -599,7 +599,7 @@ generateModellingParams <- function(assayIDs,

-    if(length(assayIDs) > 1) obsFeatures <- sum(mcols(measurements)[, "assay"] %in% assayIDs)

+    if(length(assayIDs) > 1) obsFeatures <- sum(S4Vectors::mcols(measurements)[, "assay"] %in% assayIDs)

     else obsFeatures <- ncol(measurements)

@@ -670,7 +670,7 @@ generateMultiviewParams <- function(assayIDs,

         if(length(classifier) > 1) classifier <- classifier[[1]]

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) if(assayID == 1) measurements else measurements[, mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) if(assayID == 1) measurements else measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsAssays <- mapply(generateModellingParams,

@@ -709,7 +709,7 @@ generateMultiviewParams <- function(assayIDs,

     if(multiViewMethod == "prevalidation"){

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) measurements[, mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsAssays <- mapply(generateModellingParams,

@@ -738,7 +738,7 @@ generateMultiviewParams <- function(assayIDs,

     if(multiViewMethod == "prevalidation"){

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) measurements[, mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsAssays <- mapply(generateModellingParams,

@@ -768,7 +768,7 @@ generateMultiviewParams <- function(assayIDs,

     if(multiViewMethod == "PCA"){

         # Split measurements up by assay.

-        assayTrain <- sapply(assayIDs, function(assayID) measurements[, mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+        assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

         # Generate params for each assay. This could be extended to have different selectionMethods for each type

         paramsClinical <-  list(clinical = generateModellingParams(

@@ -906,7 +906,7 @@ train.DataFrame <- function(x, outcomeTrain, classifier = "randomForest", perfor

               outcomeTrain <- measurementsAndOutcome[["outcome"]]

               classifier <- cleanClassifier(classifier = classifier, measurements = measurements)

-              if(assayIDs == "all") assayIDs <- unique(mcols(measurements)[, "assay"])

+              if(assayIDs == "all") assayIDs <- unique(S4Vectors::mcols(measurements)[, "assay"])

               if(is.null(assayIDs)) assayIDs <- 1

               names(assayIDs) <- assayIDs

               names(classifier) <- assayIDs

@@ -919,7 +919,7 @@ train.DataFrame <- function(x, outcomeTrain, classifier = "randomForest", perfor

                               # Loop over classifiers

                                   measurementsUse <- measurements

-                                  if(assayIndex != 1) measurementsUse <- measurements[, mcols(measurements)[, "assay"] == assayIndex, drop = FALSE]

+                                  if(assayIndex != 1) measurementsUse <- measurements[, S4Vectors::mcols(measurements)[, "assay"] == assayIndex, drop = FALSE]

                                   classifierParams <- .classifierKeywordToParams(classifierForAssay)

                                   if(!is.null(classifierParams$trainParams@tuneParams))

@@ -950,7 +950,7 @@ train.DataFrame <- function(x, outcomeTrain, classifier = "randomForest", perfor

               ### Merging or binding to combine data

               if(multiViewMethod == "merge"){

-                  measurementsUse <- measurements[, mcols(measurements)[["assay"]] %in% assayIDs]

+                  measurementsUse <- measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIDs]

                   model <- .doTrain(measurementsUse, outcomeTrain, NULL, NULL, crossValParams, modellingParams, verbose = 0)[["model"]]

                   class(model) <- c("trainedByClassifyR", class(model))

               }

@@ -959,7 +959,7 @@ train.DataFrame <- function(x, outcomeTrain, classifier = "randomForest", perfor

               ### Prevalidation to combine data

               if(multiViewMethod == "prevalidation"){

                 # Split measurements up by assay.

-                 assayTrain <- sapply(assayIDs, function(assayID) measurements[, mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

+                 assayTrain <- sapply(assayIDs, function(assayID) measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayID], simplify = FALSE)

                # Generate params for each assay. This could be extended to have different selectionMethods for each type

                  paramsAssays <- mapply(generateModellingParams,

@@ -980,10 +980,10 @@ train.DataFrame <- function(x, outcomeTrain, classifier = "randomForest", perfor

               ### Principal Components Analysis to combine data

               if(multiViewMethod == "PCA"){

-                measurementsUse <- measurements[, mcols(measurements)[["assay"]] %in% assayIDs]

+                measurementsUse <- measurements[, S4Vectors::mcols(measurements)[["assay"]] %in% assayIDs]

                 paramsClinical <-  list(clinical = generateModellingParams(

                                         assayIDs = "clinical",

-                                        measurements = measurements[, mcols(measurements)[["assay"]] == "clinical"],

+                                        measurements = measurements[, S4Vectors::mcols(measurements)[["assay"]] == "clinical"],

                                         classifier = classifier["clinical"],

                                         multiViewMethod = "none"))

@@ -1020,8 +1020,8 @@ train.list <- function(x, outcomeTrain, ...)

               df_list <- sapply(x, S4Vectors::DataFrame)

               df_list <- mapply(function(meas, nam){

-                  mcols(meas)$assay <- nam

-                  mcols(meas)$feature <- colnames(meas)

+                  S4Vectors::mcols(meas)$assay <- nam

+                  S4Vectors::mcols(meas)$feature <- colnames(meas)

                   meas

               }, df_list, names(df_list))

@@ -1070,8 +1070,8 @@ predict.trainedByClassifyR <- function(object, newData, ...)

   } else if(is.list(newData) && !is(object, "listOfModels")) # Don't check all those conditions that train function does.

   { # Merge the list of data tables and keep track of assay names in columns' metadata.

     newData <- mapply(function(meas, nam){

-               mcols(meas)$assay <- nam

-               mcols(meas)$feature <- colnames(meas)

+               S4Vectors::mcols(meas)$assay <- nam

+               S4Vectors::mcols(meas)$feature <- colnames(meas)

                meas

                }, newData, names(newData))

     newData <- do.call(cbind, newData)

@@ -37,8 +37,8 @@ pcaTrainInterface <- function(measurements, classes, params, nFeatures, ...)

               ###

               pcaVar <- S4Vectors::DataFrame(pcaVar)

-              mcols(pcaVar)$assay = "PCA"

-              mcols(pcaVar)$feature = colnames(pcaVar)

+              S4Vectors::mcols(pcaVar)$assay = "PCA"

+              S4Vectors::mcols(pcaVar)$feature = colnames(pcaVar)

               fullTrain = cbind(assayTrain[["clinical"]], pcaVar)

@@ -87,7 +87,7 @@ pcaPredictInterface <- function(fullModel, test, ..., returnType = "both", verbo

               fullModel <- fullModel@fullModel[[1]]

               #Split my test data into a list of the different assays

-              assayTest <- sapply(unique(mcols(test)[["assay"]]), function(assay) test[, mcols(test)[["assay"]] %in% assay], simplify = FALSE)

+              assayTest <- sapply(unique(S4Vectors::mcols(test)[["assay"]]), function(assay) test[, S4Vectors::mcols(test)[["assay"]] %in% assay], simplify = FALSE)

               # Pull out my PCA models

               pcaModels <- fullModel$pcaModels

@@ -101,8 +101,8 @@ pcaPredictInterface <- function(fullModel, test, ..., returnType = "both", verbo

               pcaVar <- do.call(cbind, pcaVar)

               pcaVar <- S4Vectors::DataFrame(pcaVar)

-              mcols(pcaVar)$assay = "PCA"

-              mcols(pcaVar)$feature = colnames(pcaVar)

+              S4Vectors::mcols(pcaVar)$assay = "PCA"

+              S4Vectors::mcols(pcaVar)$feature = colnames(pcaVar)

               # Merge my PCA stuff with my clinical data

               fullTest = cbind(assayTest[["clinical"]], pcaVar)

@@ -81,8 +81,8 @@ prevalTrainInterface <- function(measurements, classes, params, ...)

               #fullTrain = cbind(assayTrain[["clinical"]][,selectedFeaturesClinical], prevalidationTrain[rownames(assayTrain[["clinical"]]), , drop = FALSE])

               prevalidationTrain <- S4Vectors::DataFrame(prevalidationTrain)

-              mcols(prevalidationTrain)$assay = "prevalidation"

-              mcols(prevalidationTrain)$feature = colnames(prevalidationTrain)

+              S4Vectors::mcols(prevalidationTrain)$assay = "prevalidation"

+              S4Vectors::mcols(prevalidationTrain)$feature = colnames(prevalidationTrain)

               ###

@@ -142,7 +142,7 @@ prevalTrainInterface <- function(measurements, classes, params, ...)

 prevalPredictInterface <- function(fullModel, test, ..., returnType = "both", verbose = 0)

           {

               fullModel <- fullModel@fullModel[[1]]

-              assayTest <- sapply(unique(mcols(test)[["assay"]]), function(assay) test[, mcols(test)[["assay"]] %in% assay], simplify = FALSE)

+              assayTest <- sapply(unique(S4Vectors::mcols(test)[["assay"]]), function(assay) test[, S4Vectors::mcols(test)[["assay"]] %in% assay], simplify = FALSE)

               prevalidationModels <- fullModel$prevalidationModels

               modelPredictionFunctions <- fullModel$modellingParams

@@ -157,8 +157,8 @@ prevalPredictInterface <- function(fullModel, test, ..., returnType = "both", ve

                   extractPrevalidation()

               prevalidationPredict <- S4Vectors::DataFrame(prevalidationPredict)

-              mcols(prevalidationPredict)$assay = "prevalidation"

-              mcols(prevalidationPredict)$feature = colnames(prevalidationPredict)

+              S4Vectors::mcols(prevalidationPredict)$assay = "prevalidation"

+              S4Vectors::mcols(prevalidationPredict)$feature = colnames(prevalidationPredict)

               fullTest = cbind(assayTest[["clinical"]], prevalidationPredict[rownames(assayTest[["clinical"]]), , drop = FALSE])

@@ -64,7 +64,7 @@ setMethod("prepareData", "DataFrame",

   if(!all(colnames(measurements) == make.names(colnames(measurements))))

   {

     warning("Unsafe feature names in input data. Converted into safe names.")

-    mcols(measurements)$feature <- colnames(measurements) # Save the originals.

+    S4Vectors::mcols(measurements)$feature <- colnames(measurements) # Save the originals.

     colnames(measurements) <- make.names(colnames(measurements)) # Ensure column names are safe names.

   }