@@ -1308,7 +1308,7 @@ setClassUnion("ModellingParamsOrNULL", c("ModellingParams", "NULL"))

 #'     modellingParams <- ModellingParams()

 #'     classified <-

 #'     runTests(measurements, classes, LOOCVparams, modellingParams,

-#'              DataFrame(characteristic = c("dataset", "classification"),

+#'              DataFrame(characteristic = c("Data Set", "Classification"),

 #'                       value = c("Asthma", "Different Means"))

 #'              )

 #'     class(classified)

@@ -7,23 +7,28 @@

 #' or a list of these objects containing the training data.  For a

 #' \code{matrix} and \code{data.frame}, the rows are samples and the columns are features. For a \code{data.frame} or \code{\link{MultiAssayExperiment}} assay

 #' the rows are features and the columns are samples, as is typical in Bioconductor.

-#' @param classes A vector of class labels of class \code{\link{factor}} of the

+#' @param outcomes A vector of class labels of class \code{\link{factor}} of the

 #' same length as the number of samples in \code{measurements} or a character vector of length 1 containing the

 #' column name in \code{measurements} if it is a \code{\link{DataFrame}} or the

 #' column name in \code{colData(measurements)} if \code{measurements} is a \code{\link{MultiAssayExperiment}}. If a column name, that column will be

-#' removed before training.

+#' removed before training. Or a \code{\link{Surv}} object or a character vector of length 2 or 3 specifying the time and event columns in

+#' \code{measurements} for survival outcome.

+#' @param ... Arguments other than measurements and outcomes in the generic.

+#' @param assayName An informative name describing the data (e.g. RNA-seq) table if the input is a data frame or matrix. Not used if input

+#' is \code{MultiAssayExperiment} or other list-like structure because it will already have assay names in the experiment list. This

+#' name will be stored in the characteristics table of the result as Assay Name characteristic.

 #' @param nFeatures The number of features to be used for classification. If this is a single number, the same number of features will be used for all comparisons

-#' or datasets. If a numeric vector these will be optimised over using \code{selectionOptimisation}. If a named vector with the same names of multiple datasets, 

-#' a different number of features will be used for each dataset. If a named list of vectors, the respective number of features will be optimised over. 

+#' or assays. If a numeric vector these will be optimised over using \code{selectionOptimisation}. If a named vector with the same names of multiple assays, 

+#' a different number of features will be used for each assay. If a named list of vectors, the respective number of features will be optimised over. 

 #' Set to NULL or "all" if all features should be used.

-#' @param selectionMethod A character vector of feature selection methods to compare. If a named character vector with names corresponding to different datasets, 

-#' and performing multiview classification, the respective classification methods will be used on each dataset.

+#' @param selectionMethod A character vector of feature selection methods to compare. If a named character vector with names corresponding to different assays, 

+#' and performing multiview classification, the respective classification methods will be used on each assay.

 #' @param selectionOptimisation A character of "Resubstitution", "Nested CV" or "none" specifying the approach used to optimise nFeatures. 

-#' @param classifier A character vector of classification methods to compare. If a named character vector with names corresponding to different datasets, 

-#' and performing multiview classification, the respective classification methods will be used on each dataset.

+#' @param classifier A character vector of classification methods to compare. If a named character vector with names corresponding to different assays, 

+#' and performing multiview classification, the respective classification methods will be used on each assay.

 #' @param multiViewMethod A character vector specifying the multiview method or data integration approach to use.

-#' @param dataCombinations A character vector or list of character vectors proposing the datasets or, in the case of a list, combination of datasets to use

-#' with each element being a vector of datasets to combine.

+#' @param assayCombinations A character vector or list of character vectors proposing the assays or, in the case of a list, combination of assays to use

+#' with each element being a vector of assays to combine.

 #' @param nFolds A numeric specifying the number of folds to use for cross-validation.

 #' @param nRepeats A numeric specifying the the number of repeats or permutations to use for cross-validation.

 #' @param nCores A numeric specifying the number of cores used if the user wants to use parallelisation. 

@@ -36,9 +41,9 @@

 #' 

 #' \code{selectionMethod} can be any of the following implemented approaches -  none, t-test, limma, edgeR, NSC, Bartlett, Levene, DMD, likelihoodRatio, KS or KL.

 #' 

-#' \code{multiViewMethod} can take a few different values. Using \code{merge} will merge or bind the datasets after feature selection. 

-#'  Using \code{prevlidation} will build prevalidated vectors on all the datasets except the clinical data. There must be a dataset called clinical.

-#'  Using \code{pca} will perform pca on each dataset and then merge the top few components with the clinical data. There must be a dataset called clinical.

+#' \code{multiViewMethod} can take a few different values. Using \code{merge} will merge or bind the assays after feature selection. 

+#'  Using \code{prevalidation} will build prevalidated vectors on all the assays except the clinical data. There must be a assay called clinical.

+#'  Using \code{PCA} will perform Principal Components Analysis on each assay and then merge the top few components with the clinical data. There must be a assay called clinical.

 #'

 #' @return An object of class \code{\link{ClassifyResult}}

 #' @export

@@ -55,52 +60,42 @@

 #' performancePlot(result)

 #' 

 #' 

-#' # Compare performance of different datasets. 

-#' # First make a toy example dataset with multiple data types. We'll randomly assign different features to be clinical, gene or protein.

+#' # Compare performance of different assays. 

+#' # First make a toy example assay with multiple data types. We'll randomly assign different features to be clinical, gene or protein.

 #' # set.seed(51773)

 #' # measurements <- DataFrame(measurements, check.names = FALSE)

-#' # mcols(measurements)$dataset <- c(rep("clinical",20),sample(c("gene", "protein"), ncol(measurements)-20, replace = TRUE))

+#' # mcols(measurements)$assay <- c(rep("clinical",20),sample(c("gene", "protein"), ncol(measurements)-20, replace = TRUE))

 #' # mcols(measurements)$feature <- colnames(measurements)

 #' 

-#' # We'll use different nFeatures for each dataset. We'll also use repeated cross-validation with 5 repeats for speed in the example.

+#' # We'll use different nFeatures for each assay. We'll also use repeated cross-validation with 5 repeats for speed in the example.

 #' # set.seed(51773)

 #' #result <- crossValidate(measurements, classes, nFeatures = c(clinical = 5, gene = 20, protein = 30), classifier = "randomForest", nRepeats = 5)

 #' # performancePlot(result)

 #' 

-#' # Merge different datasets. But we will only do this for two combinations. If dataCombinations is not specified it would attempt all combinations.

+#' # Merge different assays. But we will only do this for two combinations. If assayCombinations is not specified it would attempt all combinations.

 #' # set.seed(51773)

-#' # resultMerge <- crossValidate(measurements, classes, dataCombinations = list(c("clinical", "protein"), c("clinical", "gene")), multiViewMethod = "merge", nRepeats = 5)

+#' # resultMerge <- crossValidate(measurements, classes, assayCombinations = list(c("clinical", "protein"), c("clinical", "gene")), multiViewMethod = "merge", nRepeats = 5)

 #' # performancePlot(resultMerge)

 #' 

 #' 

 #' # performancePlot(c(result, resultMerge))

 #' 

 #' @importFrom survival Surv

-setGeneric("crossValidate", function(measurements,

-                                     classes,

-                                     nFeatures = 20,

-                                     selectionMethod = "t-test",

-                                     selectionOptimisation = "Resubstitution",

-                                     classifier = "randomForest",

-                                     multiViewMethod = "none",

-                                     dataCombinations = NULL,

-                                     nFolds = 5,

-                                     nRepeats = 20,

-                                     nCores = 1,

-                                     characteristicsLabel = NULL)

+setGeneric("crossValidate", function(measurements, outcomes, ...)

     standardGeneric("crossValidate"))

 #' @rdname crossValidate

 #' @export

 setMethod("crossValidate", "DataFrame", 

           function(measurements,

-                   classes, 

+                   outcomes,

+                   assayName = NULL,

                    nFeatures = 20,

                    selectionMethod = "t-test",

                    selectionOptimisation = "Resubstitution",

                    classifier = "randomForest",

                    multiViewMethod = "none",

-                   dataCombinations = NULL,

+                   assayCombinations = NULL,

                    nFolds = 5,

                    nRepeats = 20,

                    nCores = 1,

@@ -108,10 +103,16 @@ setMethod("crossValidate", "DataFrame",

           {

               # Check that data is in the right format

-              splitDataset <- .splitDataAndOutcomes(measurements, classes)

-              measurements <- splitDataset[["measurements"]]

-              classes <- splitDataset[["outcomes"]]

-              checkData(measurements, classes)

+              splitAssay <- .splitDataAndOutcomes(measurements, outcomes)

+              measurements <- splitAssay[["measurements"]]

+              outcomes <- splitAssay[["outcomes"]]

+              

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

+              assayIDs <- unique(mcols(measurements)[, "assay"])

+              if(is.null(assayIDs))

+                assayIDs <- 1

+              

+              checkData(measurements, outcomes)

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

               nFeatures <- cleanNFeatures(nFeatures = nFeatures,

@@ -128,10 +129,6 @@ setMethod("crossValidate", "DataFrame",

               classifier <- cleanClassifier(classifier = classifier,

                                             measurements = measurements)

-

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

-              datasetIDs <- unique(mcols(measurements)[, "dataset"])

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

               ##!!!!! Do something with data combinations

@@ -146,34 +143,34 @@ setMethod("crossValidate", "DataFrame",

               if(multiViewMethod == "none"){

-                  # The below loops over dataset and classifier and allows us to answer

+                  # The below loops over assay and classifier and allows us to answer

                   # the following questions:

                   #

-                  # 1) One dataset using one classifier

-                  # 2) One dataset using multi classifiers

-                  # 3) Multi datasets individually

+                  # 1) One assay using one classifier

+                  # 2) One assay using multi classifiers

+                  # 3) Multi assays individually

                   # We should probably transition this to use grid instead.

-

+                  

                   resClassifier <-

-                      sapply(datasetIDs, function(dataIndex) {

-                          # Loop over datasets

-                          sapply(classifier[[dataIndex]], function(classifierIndex) {

+                      sapply(assayIDs, function(assayIndex) {

+                          # Loop over assays

+                          sapply(classifier[[assayIndex]], function(classifierIndex) {

                               # Loop over classifiers

-                              sapply(selectionMethod[[dataIndex]], function(selectionIndex) {

+                              sapply(selectionMethod[[assayIndex]], function(selectionIndex) {

                                   # Loop over classifiers

                                   set.seed(seed)

                                   measurementsUse <- measurements

-                                  if(!is.null(mcols(measurements))) measurementsUse <- measurements[, mcols(measurements)[, "dataset"] == dataIndex, drop = FALSE]

-                                  

+                                  if(!is.null(assayName)) attr(measurementsUse, "assayName") <- assayName

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

                                   CV(

-                                      measurements = measurementsUse, classes = classes,

-                                      nFeatures = nFeatures[dataIndex],

+                                      measurements = measurementsUse, outcomes = outcomes,

+                                      assayIDs = assayIndex,

+                                      nFeatures = nFeatures[assayIndex],

                                       selectionMethod = selectionIndex,

                                       selectionOptimisation = selectionOptimisation,

                                       classifier = classifierIndex,

                                       multiViewMethod = multiViewMethod,

-                                      dataCombinations = dataIndex,

                                       nFolds = nFolds,

                                       nRepeats = nRepeats,

                                       nCores = nCores,

@@ -201,21 +198,20 @@ setMethod("crossValidate", "DataFrame",

               if(multiViewMethod == "merge"){

-                  # The below loops over different combinations of datasets and merges them together.

-                  # This allows someone to answer which combinations of the datasets might be most useful.

+                  # The below loops over different combinations of assays and merges them together.

+                  # This allows someone to answer which combinations of the assays might be most useful.

-                  if(is.null(dataCombinations)) dataCombinations <- do.call("c", sapply(seq_along(datasetIDs),function(n)combn(datasetIDs, n, simplify = FALSE)))

+                  if(is.null(assayCombinations)) assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

-                  result <- sapply(dataCombinations, function(dataIndex){

-                      CV(measurements = measurements[, mcols(measurements)$dataset %in% dataIndex],

-                         classes = classes,

-                         nFeatures = nFeatures[dataIndex],

-                         selectionMethod = selectionMethod[dataIndex],

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

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

+                         outcomes = outcomes, assayIDs = assayIndex,

+                         nFeatures = nFeatures[assayIndex],

+                         selectionMethod = selectionMethod[assayIndex],

                          selectionOptimisation = selectionOptimisation,

-                         classifier = classifier[dataIndex],

-                         multiViewMethod = ifelse(length(dataIndex)==1, "none", multiViewMethod),

-                         dataCombinations = dataIndex,

+                         classifier = classifier[assayIndex],

+                         multiViewMethod = ifelse(length(assayIndex) == 1, "none", multiViewMethod),

                          nFolds = nFolds,

                          nRepeats = nRepeats,

                          nCores = nCores,

@@ -229,26 +225,26 @@ setMethod("crossValidate", "DataFrame",

               if(multiViewMethod == "prevalidation"){

-                  # The below loops over different combinations of datasets and combines them together using prevalidation.

-                  # This allows someone to answer which combinations of the datasets might be most useful.

+                  # The below loops over different combinations of assays and combines them together using prevalidation.

+                  # This allows someone to answer which combinations of the assays might be most useful.

-                  if(is.null(dataCombinations)){

-                      dataCombinations <- do.call("c", sapply(seq_along(datasetIDs),function(n)combn(datasetIDs, n, simplify = FALSE)))

-                      dataCombinations <- dataCombinations[sapply(dataCombinations, function(x)"clinical"%in%x, simplify = TRUE)]

-                      if(length(dataCombinations)==0) stop("No dataCombinations with `clinical` data")

+                  if(is.null(assayCombinations))

+                  {

+                      assayCombinations <- do.call("c", sapply(seq_along(assayIDs), function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

+                      assayCombinations <- assayCombinations[sapply(assayCombinations, function(combination) "clinical" %in% combination, simplify = TRUE)]

+                      if(length(assayCombinations) == 0) stop("No assayCombinations with \"clinical\" data")

                   }

-                  result <- sapply(dataCombinations, function(dataIndex){

-                      CV(measurements = measurements[, mcols(measurements)$dataset %in% dataIndex],

-                         classes = classes,

-                         nFeatures = nFeatures[dataIndex],

-                         selectionMethod = selectionMethod[dataIndex],

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

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

+                         outcomes = outcomes, assayIDs = assayIndex,

+                         nFeatures = nFeatures[assayIndex],

+                         selectionMethod = selectionMethod[assayIndex],

                          selectionOptimisation = selectionOptimisation,

-                         classifier = classifier[dataIndex],

-                         multiViewMethod = ifelse(length(dataIndex)==1, "none", multiViewMethod),

-                         dataCombinations = dataIndex,

+                         classifier = classifier[assayIndex],

+                         multiViewMethod = ifelse(length(assayIndex) == 1, "none", multiViewMethod),

                          nFolds = nFolds,

                          nRepeats = nRepeats,

                          nCores = nCores,

@@ -259,30 +255,29 @@ setMethod("crossValidate", "DataFrame",

-              ### Prevalidation to combine data

-              if(multiViewMethod == "pca"){

+              ### Principal Components Analysis to combine data

+              if(multiViewMethod == "PCA"){

-                  # The below loops over different combinations of datasets and combines them together using prevalidation.

-                  # This allows someone to answer which combinations of the datasets might be most useful.

+                  # The below loops over different combinations of assays and combines them together using prevalidation.

+                  # This allows someone to answer which combinations of the assays might be most useful.

-                  if(is.null(dataCombinations)){

-                      dataCombinations <- do.call("c", sapply(seq_along(datasetIDs),function(n)combn(datasetIDs, n, simplify = FALSE)))

-                      dataCombinations <- dataCombinations[sapply(dataCombinations, function(x)"clinical"%in%x, simplify = TRUE)]

-                      if(length(dataCombinations)==0) stop("No dataCombinations with `clinical` data")

+                  if(is.null(assayCombinations)){

+                      assayCombinations <- do.call("c", sapply(seq_along(assayIDs),function(nChoose) combn(assayIDs, nChoose, simplify = FALSE)))

+                      assayCombinations <- assayCombinations[sapply(assayCombinations, function(combination) "clinical" %in% combination, simplify = TRUE)]

+                      if(length(assayCombinations) == 0) stop("No assayCombinations with \"clinical\" data")

                   }

-                  result <- sapply(dataCombinations, function(dataIndex){

-                      CV(measurements = measurements[, mcols(measurements)$dataset %in% dataIndex],

-                         classes = classes,

-                         nFeatures = nFeatures[dataIndex],

-                         selectionMethod = selectionMethod[dataIndex],

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

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

+                         outcomes = outcomes, assayIDs = assayIndex,

+                         nFeatures = nFeatures[assayIndex],

+                         selectionMethod = selectionMethod[assayIndex],

                          selectionOptimisation = selectionOptimisation,

-                         classifier = classifier[dataIndex],

-                         multiViewMethod = ifelse(length(dataIndex)==1, "none", multiViewMethod),

-                         dataCombinations = dataIndex,

+                         classifier = classifier[assayIndex],

+                         multiViewMethod = ifelse(length(assayIndex) == 1, "none", multiViewMethod),

                          nFolds = nFolds,

                          nRepeats = nRepeats,

                          nCores = nCores,

@@ -301,13 +296,13 @@ setMethod("crossValidate", "DataFrame",

 # One or more omics data sets, possibly with clinical data.

 setMethod("crossValidate", "MultiAssayExperiment",

           function(measurements,

-                   classes, 

+                   outcomes, 

                    nFeatures = 20,

                    selectionMethod = "t-test",

                    selectionOptimisation = "Resubstitution",

                    classifier = "randomForest",

                    multiViewMethod = "none",

-                   dataCombinations = NULL,

+                   assayCombinations = NULL,

                    nFolds = 5,

                    nRepeats = 20,

                    nCores = 1,

@@ -321,18 +316,18 @@ setMethod("crossValidate", "MultiAssayExperiment",

                       stop("Data set contains replicates. Please provide remove or average replicate observations and try again.")

               }

-              tablesAndClasses <- .MAEtoWideTable(measurements, targets, classes, restrict = NULL)

-              measurements <- tablesAndClasses[["dataTable"]]

-              classes <- tablesAndClasses[["outcomes"]]

+              tablesAndoutcomes <- .MAEtoWideTable(measurements, targets, outcomes, restrict = NULL)

+              measurements <- tablesAndoutcomes[["dataTable"]]

+              outcomes <- tablesAndoutcomes[["outcomes"]]

               crossValidate(measurements = measurements,

-                            classes = classes, 

+                            outcomes = outcomes, 

                             nFeatures = nFeatures,

                             selectionMethod = selectionMethod,

                             selectionOptimisation = selectionOptimisation,

                             classifier = classifier,

                             multiViewMethod = multiViewMethod,

-                            dataCombinations = dataCombinations,

+                            assayCombinations = assayCombinations,

                             nFolds = nFolds,

                             nRepeats = nRepeats,

                             nCores = nCores,

@@ -343,13 +338,14 @@ setMethod("crossValidate", "MultiAssayExperiment",

 #' @export

 setMethod("crossValidate", "data.frame", # data.frame of numeric measurements.

           function(measurements,

-                   classes, 

+                   outcomes, 

+                   assayName = NULL,

                    nFeatures = 20,

                    selectionMethod = "t-test",

                    selectionOptimisation = "Resubstitution",

                    classifier = "randomForest",

                    multiViewMethod = "none",

-                   dataCombinations = NULL,

+                   assayCombinations = NULL,

                    nFolds = 5,

                    nRepeats = 20,

                    nCores = 1,

@@ -357,13 +353,14 @@ setMethod("crossValidate", "data.frame", # data.frame of numeric measurements.

           {

               measurements <- DataFrame(measurements)

               crossValidate(measurements = measurements,

-                            classes = classes, 

+                            outcomes = outcomes,

+                            assayName = assayName,

                             nFeatures = nFeatures,

                             selectionMethod = selectionMethod,

                             selectionOptimisation = selectionOptimisation,

                             classifier = classifier,

                             multiViewMethod = multiViewMethod,

-                            dataCombinations = dataCombinations,

+                            assayCombinations = assayCombinations,

                             nFolds = nFolds,

                             nRepeats = nRepeats,

                             nCores = nCores,

@@ -374,13 +371,14 @@ setMethod("crossValidate", "data.frame", # data.frame of numeric measurements.

 #' @export

 setMethod("crossValidate", "matrix", # Matrix of numeric measurements.

           function(measurements,

-                   classes, 

+                   outcomes,

+                   assayName = NULL,

                    nFeatures = 20,

                    selectionMethod = "t-test",

                    selectionOptimisation = "Resubstitution",

                    classifier = "randomForest",

                    multiViewMethod = "none",

-                   dataCombinations = NULL,

+                   assayCombinations = NULL,

                    nFolds = 5,

                    nRepeats = 20,

                    nCores = 1,

@@ -388,13 +386,14 @@ setMethod("crossValidate", "matrix", # Matrix of numeric measurements.

           {

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

               crossValidate(measurements = measurements,

-                            classes = classes, 

+                            outcomes = outcomes,

+                            assayName = assayName,

                             nFeatures = nFeatures,

                             selectionMethod = selectionMethod,

                             selectionOptimisation = selectionOptimisation,

                             classifier = classifier,

                             multiViewMethod = multiViewMethod,

-                            dataCombinations = dataCombinations,

+                            assayCombinations = assayCombinations,

                             nFolds = nFolds,

                             nRepeats = nRepeats,

                             nCores = nCores,

@@ -408,13 +407,13 @@ setMethod("crossValidate", "matrix", # Matrix of numeric measurements.

 #' @export

 setMethod("crossValidate", "list",

           function(measurements,

-                   classes, 

+                   outcomes, 

                    nFeatures = 20,

                    selectionMethod = "t-test",

                    selectionOptimisation = "Resubstitution",

                    classifier = "randomForest",

                    multiViewMethod = "none",

-                   dataCombinations = NULL,

+                   assayCombinations = NULL,

                    nFolds = 5,

                    nRepeats = 20,

                    nCores = 1,

@@ -424,12 +423,12 @@ setMethod("crossValidate", "list",

               # Check if the list only contains one data type

               if (measurements |> sapply(class) |> unique() |> length() != 1) {

-                  stop("All datasets must be of the same type (e.g. data.frame, matrix)")

+                  stop("All assays must be of the same type (e.g. data.frame, matrix)")

               }

               # Check data type is valid

               if (!(measurements[[1]] |> class() %in% c("data.frame", "DataFrame", "matrix"))) {

-                  stop("Datasets must be of type data.frame, DataFrame or matrix")

+                  stop("assays must be of type data.frame, DataFrame or matrix")

               }

               # Check the list is named

@@ -437,21 +436,21 @@ setMethod("crossValidate", "list",

                   stop("Measurements must be a named list")

               }

-              # Check same number of samples for all datasets

+              # Check same number of samples for all assays

               if ((df_list |> sapply(dim))[2,] |> unique() |> length() != 1) {

-                  stop("All datasets must have the same number of samples")

+                  stop("All assays must have the same number of samples")

               }

-              # Check the number of classes is the same

+              # Check the number of outcomes is the same

               if ((df_list[[1]] |> dim())[2] != classes |> length()) {

-                  stop("Classes must have same number of samples as measurements")

+                  stop("Outcomes must have same number of samples as measurements")

               }

               df_list <- sapply(measurements, t, simplify = FALSE)

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

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

-                  mcols(meas)$dataset <- nam

+                  mcols(meas)$assay <- nam

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

                   meas

               }, df_list, names(df_list))

@@ -461,13 +460,13 @@ setMethod("crossValidate", "list",

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

               crossValidate(measurements = combined_df,

-                            classes = classes, 

+                            outcomes = outcomes, 

                             nFeatures = nFeatures,

                             selectionMethod = selectionMethod,

                             selectionOptimisation = selectionOptimisation,

                             classifier = classifier,

                             multiViewMethod = multiViewMethod,

-                            dataCombinations = dataCombinations,

+                            assayCombinations = assayCombinations,

                             nFolds = nFolds,

                             nRepeats = nRepeats,

                             nCores = nCores,

@@ -481,13 +480,13 @@ setMethod("crossValidate", "list",

 cleanNFeatures <- function(nFeatures, measurements){

     #### Clean up

     if(!is.null(mcols(measurements)))

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

+      obsFeatures <- unlist(as.list(table(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))

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

-    #if(is.null(names(nFeatures)) & length(nFeatures) > 1) stop("nFeatures needs to be a named numeric vector or list with the same names as the datasets.")

-    if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(nFeatures))) stop("nFeatures needs to be a named numeric vector or list with the same names as the datasets.")

+    #if(is.null(names(nFeatures)) & length(nFeatures) > 1) stop("nFeatures needs to be a named numeric vector or list with the same names as the assays.")

+    if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(nFeatures))) stop("nFeatures needs to be a named numeric vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(nFeatures)) & is(nFeatures, "numeric")) nFeatures <- as.list(pmin(obsFeatures, nFeatures[names(obsFeatures)]))

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(nFeatures)) & is(nFeatures, "list")) nFeatures <- mapply(pmin, nFeatures[names(obsFeatures)], obsFeatures, SIMPLIFY = FALSE)

     nFeatures

@@ -498,13 +497,13 @@ cleanNFeatures <- function(nFeatures, measurements){

 cleanSelectionMethod <- function(selectionMethod, measurements){

     #### Clean up

     if(!is.null(mcols(measurements)))

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

+      obsFeatures <- unlist(as.list(table(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)

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

-    #if(is.null(names(selectionMethod)) & length(selectionMethod) > 1) stop("selectionMethod needs to be a named character vector or list with the same names as the datasets.")

-    if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(selectionMethod))) stop("selectionMethod needs to be a named character vector or list with the same names as the datasets.")

+    #if(is.null(names(selectionMethod)) & length(selectionMethod) > 1) stop("selectionMethod needs to be a named character vector or list with the same names as the assays.")

+    if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(selectionMethod))) stop("selectionMethod needs to be a named character vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(selectionMethod)) & is(selectionMethod, "character")) selectionMethod <- as.list(selectionMethod[names(obsFeatures)])

     selectionMethod

 }

@@ -514,13 +513,13 @@ cleanSelectionMethod <- function(selectionMethod, measurements){

 cleanClassifier <- function(classifier, measurements){

     #### Clean up

     if(!is.null(mcols(measurements)))

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

+      obsFeatures <- unlist(as.list(table(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)

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

-    #if(is.null(names(classifier)) & length(classifier) > 1) stop("classifier needs to be a named character vector or list with the same names as the datasets.")

-    if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(classifier))) stop("classifier needs to be a named character vector or list with the same names as the datasets.")

+    #if(is.null(names(classifier)) & length(classifier) > 1) stop("classifier needs to be a named character vector or list with the same names as the assays.")

+    if(!is.null(names(obsFeatures)) && !all(names(obsFeatures) %in% names(classifier))) stop("classifier needs to be a named character vector or list with the same names as the assays.")

     if(!is.null(names(obsFeatures)) && all(names(obsFeatures) %in% names(classifier)) & is(classifier, "character")) classifier <- as.list(classifier[names(obsFeatures)])

     classifier

 }

@@ -566,13 +565,13 @@ generateCrossValParams <- function(nRepeats, nFolds, nCores, selectionOptimisati

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

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

-checkData <- function(measurements, classes){

+checkData <- function(measurements, outcomes){

     if(is.null(rownames(measurements)))

         stop("'measurements' DataFrame must have sample identifiers as its row names.")

     if(any(is.na(measurements)))

         stop("Some data elements are missing and classifiers don't work with missing data. Consider imputation or filtering.")

-    # !!!  Need to check mcols has dataset NUm

+    # !!!  Need to check mcols has assay NUm

 }

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

@@ -584,19 +583,19 @@ checkData <- function(measurements, classes){

 #' A function to generate a ModellingParams object

 #'

 #' @inheritParams crossValidate

-#' @param datasetIDs A vector of data set identifiers as long at the number of data sets.

+#' @param assayIDs A vector of data set identifiers as long at the number of data sets.

 #'

 #' @return ModellingParams object

 #' @export

 #'

 #' @examples

 #' data(asthma)

-#' # First make a toy example dataset with multiple data types. We'll randomly assign different features to be clinical, gene or protein.

+#' # First make a toy example assay with multiple data types. We'll randomly assign different features to be clinical, gene or protein.

 #' set.seed(51773)

 #' measurements <- DataFrame(measurements, check.names = FALSE) 

-#' mcols(measurements)$dataset <- c(rep("clinical",20),sample(c("gene", "protein"), ncol(measurements)-20, replace = TRUE))

+#' mcols(measurements)$assay <- c(rep("clinical",20),sample(c("gene", "protein"), ncol(measurements)-20, replace = TRUE))

 #' mcols(measurements)$feature <- colnames(measurements)

-#' modellingParams <- generateModellingParams(datasetIDs = c("clinical", "gene", "protein"),

+#' modellingParams <- generateModellingParams(assayIDs = c("clinical", "gene", "protein"),

 #'                                           measurements = measurements, 

 #'                                           nFeatures = list(clinical = 10, gene = 10, protein = 10),

 #'                                           selectionMethod = list(clinical = "t-test", gene = "t-test", protein = "t-test"),

@@ -604,7 +603,7 @@ checkData <- function(measurements, classes){

 #'                                           classifier = "randomForest",

 #'                                           multiViewMethod = "merge")

 #' @import BiocParallel

-generateModellingParams <- function(datasetIDs,

+generateModellingParams <- function(assayIDs,

                                     measurements,

                                     nFeatures,

                                     selectionMethod,

@@ -613,7 +612,7 @@ generateModellingParams <- function(datasetIDs,

                                     multiViewMethod = "none"

 ){

     if(multiViewMethod != "none") {

-        params <- generateMultiviewParams(datasetIDs,

+        params <- generateMultiviewParams(assayIDs,

                                           measurements,

                                           nFeatures,

                                           selectionMethod,

@@ -626,7 +625,7 @@ generateModellingParams <- function(datasetIDs,

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

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

     else obsFeatures <- ncol(measurements)

@@ -705,8 +704,8 @@ generateModellingParams <- function(datasetIDs,

     #

     # if(multiViewMethod == "prevalidation"){

-    #     params$trainParams <- function(measurements, classes) prevalTrainInterface(measurements, classes, params)

-    #     params$trainParams <- function(measurements, classes) prevalTrainInterface(measurements, classes, params)

+    #     params$trainParams <- function(measurements, outcomes) prevalTrainInterface(measurements, outcomes, params)

+    #     params$trainParams <- function(measurements, outcomes) prevalTrainInterface(measurements, outcomes, params)

     # }

     #

@@ -718,7 +717,7 @@ generateModellingParams <- function(datasetIDs,

-generateMultiviewParams <- function(datasetIDs,

+generateMultiviewParams <- function(assayIDs,

                                     measurements,

                                     nFeatures,

                                     selectionMethod,

@@ -730,15 +729,15 @@ generateMultiviewParams <- function(datasetIDs,

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

-        # Split measurements up by dataset.

-        assayTrain <- sapply(datasetIDs, function(x) measurements[,mcols(measurements)[["dataset"]]%in%x], simplify = FALSE)

+        # Split measurements up by assay.

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

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

-        paramsDatasets <- mapply(generateModellingParams,

-                                 nFeatures = nFeatures[datasetIDs],

-                                 selectionMethod = selectionMethod[datasetIDs],

-                                 datasetIDs = datasetIDs,

-                                 measurements = assayTrain[datasetIDs],

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

+        paramsassays <- mapply(generateModellingParams,

+                                 nFeatures = nFeatures[assayIDs],

+                                 selectionMethod = selectionMethod[assayIDs],

+                                 assayIDs = assayIDs,

+                                 measurements = assayTrain[assayIDs],

                                  MoreArgs = list(

                                      selectionOptimisation = selectionOptimisation,

                                      classifier = classifier,

@@ -746,7 +745,7 @@ generateMultiviewParams <- function(datasetIDs,

                                  SIMPLIFY = FALSE)

         # Generate some params for merged model.

-        params <- generateModellingParams(datasetIDs = datasetIDs,

+        params <- generateModellingParams(assayIDs = assayIDs,

                                           measurements = measurements,

                                           nFeatures = nFeatures,

                                           selectionMethod = selectionMethod,

@@ -756,7 +755,7 @@ generateMultiviewParams <- function(datasetIDs,

         # Update selectParams to use

         params@selectParams <- SelectParams(selectMulti,

-                                            params = paramsDatasets,

+                                            params = paramsassays,

                                             characteristics = S4Vectors::DataFrame(characteristic = "Selection Name", value = "merge"),

                                             tuneParams = list(nFeatures = nFeatures[[1]],

                                                               performanceType = "Balanced Error",

@@ -767,16 +766,16 @@ generateMultiviewParams <- function(datasetIDs,

     if(multiViewMethod == "prevalidation"){

-        # Split measurements up by dataset.

-        assayTrain <- sapply(datasetIDs, function(x) measurements[,mcols(measurements)[["dataset"]]%in%x], simplify = FALSE)

+        # Split measurements up by assay.

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

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

-        paramsDatasets <- mapply(generateModellingParams,

-                                 nFeatures = nFeatures[datasetIDs],

-                                 selectionMethod = selectionMethod[datasetIDs],

-                                 datasetIDs = datasetIDs,

-                                 measurements = assayTrain[datasetIDs],

-                                 classifier = classifier[datasetIDs],

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

+        paramsassays <- mapply(generateModellingParams,

+                                 nFeatures = nFeatures[assayIDs],

+                                 selectionMethod = selectionMethod[assayIDs],

+                                 assayIDs = assayIDs,

+                                 measurements = assayTrain[assayIDs],

+                                 classifier = classifier[assayIDs],

                                  MoreArgs = list(

                                      selectionOptimisation = selectionOptimisation,

                                      multiViewMethod = "none"),

@@ -786,8 +785,8 @@ generateMultiviewParams <- function(datasetIDs,

         params <- ModellingParams(

             balancing = "none",

             selectParams = NULL,

-            trainParams = TrainParams(prevalTrainInterface, params = paramsDatasets, characteristics = paramsDatasets$clinical@trainParams@characteristics),

-            predictParams = PredictParams(prevalPredictInterface, characteristics = paramsDatasets$clinical@predictParams@characteristics)

+            trainParams = TrainParams(prevalTrainInterface, params = paramsassays, characteristics = paramsassays$clinical@trainParams@characteristics),

+            predictParams = PredictParams(prevalPredictInterface, characteristics = paramsassays$clinical@predictParams@characteristics)

         )

         return(params)

@@ -795,16 +794,16 @@ generateMultiviewParams <- function(datasetIDs,

     if(multiViewMethod == "prevalidation"){

-        # Split measurements up by dataset.

-        assayTrain <- sapply(datasetIDs, function(x) measurements[,mcols(measurements)[["dataset"]]%in%x], simplify = FALSE)

+        # Split measurements up by assay.

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

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

-        paramsDatasets <- mapply(generateModellingParams,

-                                 nFeatures = nFeatures[datasetIDs],

-                                 selectionMethod = selectionMethod[datasetIDs],

-                                 datasetIDs = datasetIDs,

-                                 measurements = assayTrain[datasetIDs],

-                                 classifier = classifier[datasetIDs],

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

+        paramsassays <- mapply(generateModellingParams,

+                                 nFeatures = nFeatures[assayIDs],

+                                 selectionMethod = selectionMethod[assayIDs],

+                                 assayIDs = assayIDs,

+                                 measurements = assayTrain[assayIDs],

+                                 classifier = classifier[assayIDs],

                                  MoreArgs = list(

                                      selectionOptimisation = selectionOptimisation,

                                      multiViewMethod = "none"),

@@ -814,24 +813,24 @@ generateMultiviewParams <- function(datasetIDs,

         params <- ModellingParams(

             balancing = "none",

             selectParams = NULL,

-            trainParams = TrainParams(prevalTrainInterface, params = paramsDatasets, characteristics = paramsDatasets$clinical@trainParams@characteristics),

-            predictParams = PredictParams(prevalPredictInterface, characteristics = paramsDatasets$clinical@predictParams@characteristics)

+            trainParams = TrainParams(prevalTrainInterface, params = paramsassays, characteristics = paramsassays$clinical@trainParams@characteristics),

+            predictParams = PredictParams(prevalPredictInterface, characteristics = paramsassays$clinical@predictParams@characteristics)

         )

         return(params)

     }

-    if(multiViewMethod == "pca"){

+    if(multiViewMethod == "PCA"){

-        # Split measurements up by dataset.

-        assayTrain <- sapply(datasetIDs, function(x) measurements[,mcols(measurements)[["dataset"]]%in%x], simplify = FALSE)

+        # Split measurements up by assay.

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

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

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

         paramsClinical <-  list(clinical = generateModellingParams(

                                  nFeatures = nFeatures["clinical"],

                                  selectionMethod = selectionMethod["clinical"],

-                                 datasetIDs = "clinical",

+                                 assayIDs = "clinical",

                                  measurements = assayTrain[["clinical"]],

                                  classifier = classifier["clinical"],

                                  selectionOptimisation = selectionOptimisation,

@@ -852,13 +851,14 @@ generateMultiviewParams <- function(datasetIDs,

 CV <- function(measurements,

-               classes,

+               outcomes,

+               assayIDs,

                nFeatures = NULL,

                selectionMethod = "t-test",

                selectionOptimisation = "Resubstitution",

                classifier = "elasticNetGLM",

                multiViewMethod = "none",

-               dataCombinations = NULL,

+               assayCombinations = NULL,

                nFolds = 5,

                nRepeats = 100,

                nCores = 1,

@@ -866,7 +866,7 @@ CV <- function(measurements,

 {

     # Check that data is in the right format

-    checkData(measurements, classes)

+    checkData(measurements, outcomes)

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

     nFeatures <- cleanNFeatures(nFeatures = nFeatures,

@@ -877,9 +877,6 @@ CV <- function(measurements,

                                   measurements = measurements)

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

-    datasetIDs <- unique(mcols(measurements)[, "dataset"])

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

-    if(is.null(dataCombinations)) dataCombinations <- datasetIDs

     if(is.null(characteristicsLabel)) characteristicsLabel <- "none"

     # Setup cross-validation parameters including

@@ -890,7 +887,7 @@ CV <- function(measurements,

     )

     # Turn text into TrainParams and TestParams objects

-    modellingParams <- generateModellingParams(datasetIDs = datasetIDs,

+    modellingParams <- generateModellingParams(assayIDs = assayIDs,

                                                measurements = measurements,

                                                nFeatures = nFeatures,

                                                selectionMethod = selectionMethod,

@@ -898,12 +895,12 @@ CV <- function(measurements,

                                                classifier = classifier,

                                                multiViewMethod = multiViewMethod

     )

+    if(assayIDs != 1) assayText <- assayIDs else if(!is.null(attr(measurements, "assayName"))) assayText <- attr(measurements, "assayName") else assayText <- NULL

+    characteristics <- S4Vectors::DataFrame(characteristic = c(if(!is.null(assayText)) "Assay Name" else NULL, "Classifier Name", "Selection Name", "multiViewMethod", "characteristicsLabel"), value = c(if(!is.null(assayText)) paste(assayText, collapse = ", ") else NULL, paste(classifier, collapse = ", "),  paste(selectionMethod, collapse = ", "), multiViewMethod, characteristicsLabel))

-    characteristics = S4Vectors::DataFrame(characteristic = c(if(length(datasetIDs) > 1) "Data Set" else NULL, "Classifier Name", "Selection Name", "multiViewMethod", "characteristicsLabel"), value = c(if(length(datasetIDs) > 1) paste(datasetIDs, collapse = ", ") else NULL, paste(classifier, collapse = ", "),  paste(selectionMethod, collapse = ", "), multiViewMethod, characteristicsLabel))

-

-    classifyResults <- runTests(measurements, classes, crossValParams = crossValParams, modellingParams = modellingParams, characteristics = characteristics)

+    classifyResults <- runTests(measurements, outcomes, crossValParams = crossValParams, modellingParams = modellingParams, characteristics = characteristics)

-    fullResult <- runTest(measurements, classes, measurements, classes, crossValParams = crossValParams, modellingParams = modellingParams, characteristics = characteristics, .iteration = 1)

+    fullResult <- runTest(measurements, outcomes, measurements, outcomes, crossValParams = crossValParams, modellingParams = modellingParams, characteristics = characteristics, .iteration = 1)

     classifyResults@finalModel <- list(fullResult$models)

     classifyResults

@@ -915,7 +912,7 @@ CV <- function(measurements,

-simplifyResults <- function(results, values = c("dataset", "classifier", "selectionMethod", "multiViewMethod")){

+simplifyResults <- function(results, values = c("assay", "classifier", "selectionMethod", "multiViewMethod")){

     ch <- sapply(results, function(x) x@characteristics[x@characteristics$characteristic %in% values, "value"], simplify = TRUE)

     ch <- data.frame(t(ch))

     results[!duplicated(ch)]

@@ -932,4 +929,3 @@ setMethod("predict", "ClassifyResult",

-

@@ -103,7 +103,7 @@ setMethod("distribution", "ClassifyResult",

       allFeaturesText <- allFeatures

     } else if(is(chosenFeatures[[1]], "DataFrame")) {

       allFeatures <- do.call(rbind, chosenFeatures)

-      allFeaturesText <- paste(allFeatures[, "dataset"], allFeatures[, "feature"], sep = ':')

+      allFeaturesText <- paste(allFeatures[, "Original Assay"], allFeatures[, "Original Feature"], sep = ':')

     } else if("Pairs" %in% class(chosenFeatures[[1]])) {

       allFeatures <- do.call(c, unname(chosenFeatures))

       allFeaturesText <- paste(first(allFeatures), second(allFeatures), sep = ', ')

@@ -153,7 +153,7 @@ setMethod("distribution", "ClassifyResult",

     if(isPairs) # Make it DataFrame for counting of the occurrences.

       allFeatures <- as(allFeatures, "DataFrame")

-    summaryTable <- aggregate(list(count = rep(1, nrow(allFeatures))), as.data.frame(allFeatures), length)

+    summaryTable <- aggregate(list(count = rep(1, nrow(allFeatures))), as.data.frame(allFeatures, optional = TRUE), length)

     if(summaryType == "percentage")

     {

@@ -166,9 +166,7 @@ setMethod("distribution", "ClassifyResult",

       pairsSummary <- S4Vectors::Pairs(summaryTable[, "first"], summaryTable[, "second"], summaryTable[, 3])

       colnames(mcols(pairsSummary)) <- colnames(summaryTable[, 3])

       return(pairsSummary)

-    } else { # A table of dataset and feature.

-      if(all(summaryTable[, "dataset"] == "dataset")) # Just return a vector and get rid of unnecessary dataset.

-        summaryTable <- setNames(summaryTable[, 3], summaryTable[, 2])

+    } else { # A table of assay and feature.

       summaryTable

     }

   }

@@ -165,8 +165,8 @@ setMethod("featureSetSummary", "MultiAssayExperiment", # Pick one numeric table

   if(class(featureSets) != "FeatureSetCollection")

     stop("'featureSets' is not of type FeatureSetCollection but must be.")

-  datasetUsed <- measurements[[target]]