@@ -1,2 +1,10 @@

-^.*\.Rproj$

-^\.Rproj\.user$

+^.*\.Rproj$

+^\.Rproj\.user$

+^_pkgdown\.yml$

+^docs$

+^pkgdown$

+vignettes/introduction.Rmd

+vignettes/performanceEvaluation.Rmd

+vignettes/multiViewMethods.Rmd

+vignettes/incorporateNew.Rmd

+^\.github$

@@ -1,4 +1,8 @@

-.Rproj.user

-.Rhistory

-.RData

-.Ruserdata

+.Rproj.user

+.Rhistory

+.RData

+.Ruserdata

+

+

+# vscode stuff

+.vscode

\ No newline at end of file

@@ -3,10 +3,19 @@ Type: Package

 Title: A framework for cross-validated classification problems, with

        applications to differential variability and differential

        distribution testing

-Version: 3.3.0

-Date: 2022-10-25

-Author: Dario Strbenac, Ellis Patrick, Sourish Iyengar, Harry Robertson, Andy Tran, John Ormerod, Graham Mann, Jean Yang

-Maintainer: Dario Strbenac <dario.strbenac@sydney.edu.au>

+Version: 3.3.3

+Date: 2022-11-17

+Authors@R:

+    c(

+    person(given = "Dario", family = "Strbenac", email = "dario.strbenac@sydney.edu.au", role = c("aut", "cre")),

+    person(given = "Ellis", family = "Patrick", role = "aut"),

+    person(given = "Sourish", family = "Iyengar", role = "aut"),

+    person(given = "Harry", family = "Robertson", role = "aut"),

+    person(given = "Andy", family = "Tran", role = "aut"),

+    person(given = "John", family = "Ormerod", role = "aut"),

+    person(given = "Graham", family = "Mann", role = "aut"),

+    person(given = "Jean", family = "Yang", email = "jean.yang@sydney.edu.au", role = "aut")

+    )

 VignetteBuilder: knitr

 Encoding: UTF-8

 biocViews: Classification, Survival

@@ -86,3 +95,4 @@ Collate:

     'simpleParams.R'

     'subtractFromLocation.R'

     'utilities.R'

+URL: https://sydneybiox.github.io/ClassifyR/

@@ -27,8 +27,6 @@ export(distribution)

 export(edgesToHubNetworks)

 export(featureSetSummary)

 export(finalModel)

-export(generateCrossValParams)

-export(generateModellingParams)

 export(interactorDifferences)

 export(models)

 export(performance)

@@ -87,7 +85,6 @@ exportMethods(selectionPlot)

 exportMethods(show)

 exportMethods(totalPredictions)

 exportMethods(tunedParameters)

-import(BiocParallel)

 import(MultiAssayExperiment)

 import(grid)

 import(methods)

@@ -86,7 +86,7 @@ setMethod("ROCplot", "ClassifyResult", function(results, ...) {

 #' @rdname ROCplot

 #' @export

-setMethod("ROCplot", "list", 

+setMethod("ROCplot", "list",

           function(results, mode = c("merge", "average"), interval = 95,

                    comparison = "auto", lineColours = "auto",

                    lineWidth = 1, fontSizes = c(24, 16, 12, 12, 12), labelPositions = seq(0.0, 1.0, 0.2),

@@ -101,10 +101,18 @@ setMethod("ROCplot", "list",

   if(comparison == "auto")

   {

     if(max(characteristicsCounts) == length(results))

-      comparison <- names(characteristicsCounts)[characteristicsCounts == max(characteristicsCounts)][1]

-    else

+    { # Choose a characteristic which varies the most across the results.

+      candidates <- names(characteristicsCounts)[characteristicsCounts == length(results)]

+      allCharacteristics <- do.call(rbind, lapply(results, function(result) result@characteristics))

+      distinctValues <- by(allCharacteristics[, "value"], allCharacteristics[, "characteristic"], function(values) length(unique(values)))

+      comparison <- names(distinctValues)[which.max(distinctValues)][1]

+    } else {

       stop("No characteristic is present for all results but must be.")

+    }

   }

+  resultsWithComparison <- sum(sapply(results, function(result) any(result@characteristics[, "characteristic"] == comparison)))

+  if(resultsWithComparison < length(results))

+    stop("Not all results have comparison characteristic ", comparison, ' but need to.')

   ggplot2::theme_set(ggplot2::theme_classic() + ggplot2::theme(panel.border = ggplot2::element_rect(fill = NA)))

   distinctClasses <- levels(actualOutcome(results[[1]]))

@@ -113,6 +113,15 @@ setMethod("calcExternalPerformance", c("Surv", "numeric"),

             .calcPerformance(actualOutcome, predictedOutcome, performanceType = performanceType)[["values"]]

           })

+#' @rdname calcPerformance

+#' @exportMethod calcExternalPerformance

+setMethod("calcExternalPerformance", c("factor", "tabular"), # table has class probabilities per sample.

+          function(actualOutcome, predictedOutcome, performanceType = "AUC")

+          {

+            performanceType <- match.arg(performanceType)

+            .calcPerformance(actualOutcome, predictedOutcome, performanceType = performanceType)[["values"]]

+          })

+

 #' @rdname calcPerformance

 #' @usage NULL

 #' @export

@@ -11,22 +11,26 @@

 #' 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 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. If \code{measurements} is a

-#' \code{\link{MultiAssayExperiment}}, the column name(s) in \code{colData(measurements)} representing the outcome.

+#' \code{\link{MultiAssayExperiment}}, the column name(s) in \code{colData(measurements)} representing the outcome.  If column names

+#' of survival information, time must be in first column and event status in the second.

 #' @param outcomeTrain For the \code{train} function, either a factor vector of classes, a \code{\link{Surv}} object, or

 #' a character string, or vector of such strings, containing column name(s) of column(s)

-#' containing either classes or time and event information about survival.

+#' containing either classes or time and event information about survival. If column names

+#' of survival information, time must be in first column and event status in the second.

 #' @param ... Parameters passed into \code{\link{prepareData}} which control subsetting and filtering of input data.

 #' @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 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 assays, 

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

+#' @param selectionMethod Default: "auto". 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. If \code{"auto"} t-test (two categories) / F-test (three or more categories) ranking

+#' and top \code{nFeatures} optimisation is done. Otherwise, the ranking method is per-feature Cox proportional hazards p-value.

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

-#' @param performanceType Default: \code{"auto"}. If \code{"auto"}, then balanced accuracy for classification or C-index for survival. Any one of the

+#' @param performanceType Default: \code{"auto"}. If \code{"auto"}, then balanced accuracy for classification or C-index for survival. Otherwise, any one of the

 #' options described in \code{\link{calcPerformance}} may otherwise be specified.

-#' @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 classifier Default: \code{"auto"}. 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. If \code{"auto"}, then a random forest is used for a classification

+#' task or Cox proportional hazards model for a survival task.

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

 #' @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. Special value \code{"all"} means all possible subsets of assays.

@@ -108,12 +112,14 @@ setMethod("crossValidate", "DataFrame",

               if(!performanceType %in% c("auto", .ClassifyRenvir[["performanceTypes"]]))

                 stop(paste("performanceType must be one of", paste(c("auto", .ClassifyRenvir[["performanceTypes"]]), collapse = ", "), "but is", performanceType))

+              isCategorical <- is.character(outcome) && (length(outcome) == 1 || length(outcome) == nrow(measurements)) || is.factor(outcome)

               if(performanceType == "auto")

-              {

-                if(is.character(outcome) && (length(outcome) == 1 || length(outcome) == nrow(measurements)) || is.factor(outcome))

-                  performanceType <- "Balanced Accuracy"

-                else performanceType <- "C-index"

-              }

+                if(isCategorical) performanceType <- "Balanced Accuracy" else performanceType <- "C-index"

+              if(length(selectionMethod) == 1 && selectionMethod == "auto")

+                if(isCategorical) selectionMethod <- "t-test" else selectionMethod <- "CoxPH"

+              if(length(classifier) == 1 && classifier == "auto")

+                if(isCategorical) classifier <- "randomForest" else classifier <- "CoxPH"

+              

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

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

@@ -515,18 +521,6 @@ Using an ordinary GLM instead.")

     classifier

 }

-######################################

-######################################

-#' A function to generate a CrossValParams object

-#'

-#' @inheritParams crossValidate

-#'

-#' @return CrossValParams object

-#' @export

-#'

-#' @examples

-#' CVparams <- generateCrossValParams(nRepeats = 20, nFolds = 5, nCores = 8, selectionOptimisation = "none")

-#' @import BiocParallel

 generateCrossValParams <- function(nRepeats, nFolds, nCores, selectionOptimisation){

     seed <- .Random.seed[1]

@@ -549,32 +543,7 @@ generateCrossValParams <- function(nRepeats, nFolds, nCores, selectionOptimisati

     if(!any(tuneMode %in% c("Resubstitution", "Nested CV", "none"))) stop("selectionOptimisation must be Nested CV or Resubstitution or none")

     CrossValParams(permutations = nRepeats, folds = nFolds, parallelParams = BPparam, tuneMode = tuneMode)

 }

-######################################

-######################################

-#' A function to generate a ModellingParams object

-#'

-#' @inheritParams crossValidate

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

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

-#' 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"),

-#'                                           selectionOptimisation = "none",

-#'                                           classifier = "randomForest",

-#'                                           multiViewMethod = "merge")

-#' @import BiocParallel

 generateModellingParams <- function(assayIDs,

                                     measurements,

                                     nFeatures,

@@ -6,9 +6,11 @@ randomForestTrainInterface <- function(measurementsTrain, outcomeTrain, mTryProp

   if(verbose == 3)

     message("Fitting random forest classifier to training data.")

   mtry <- round(mTryProportion * ncol(measurementsTrain)) # Number of features to try.

-      

   # Convert to base data.frame as randomForest doesn't understand DataFrame.

-  ranger::ranger(x = as(measurementsTrain, "data.frame"), y = outcomeTrain, mtry = mtry, importance = "impurity_corrected", ...)

+  fittedModel <- ranger::ranger(x = as(measurementsTrain, "data.frame"), y = outcomeTrain, mtry = mtry, ...)

+  forImportance <- ranger::ranger(x = as(measurementsTrain, "data.frame"), y = outcomeTrain, mtry = mtry, importance = "impurity_corrected", ...)

+  attr(fittedModel, "forImportance") <- forImportance

+  fittedModel

 }

 attr(randomForestTrainInterface, "name") <- "randomForestTrainInterface"

@@ -37,7 +39,8 @@ randomForestPredictInterface <- function(forest, measurementsTest, ..., returnTy

 forestFeatures <- function(forest)

                   {

-                    rankedFeaturesIndices <- order(ranger::importance(forest), decreasing = TRUE)

-                    selectedFeaturesIndices <- which(ranger::importance(forest) > 0)

+                    forImportance <- attr(forest, "forImportance")

+                    rankedFeaturesIndices <- order(ranger::importance(forImportance), decreasing = TRUE)

+                    selectedFeaturesIndices <- which(ranger::importance(forImportance) > 0)

                     list(rankedFeaturesIndices, selectedFeaturesIndices)

                   }

\ No newline at end of file

@@ -5,8 +5,9 @@ rfsrcTrainInterface <- function(measurementsTrain, survivalTrain, mTryProportion

     stop("The package 'randomForestSRC' could not be found. Please install it.")

   if(verbose == 3)

     message("Fitting rfsrc classifier to training data and making predictions on test data.")

-    

-  bindedMeasurements <- cbind(measurementsTrain, event = survivalTrain[, 1], time = survivalTrain[, 2])

+

+  # Surv objects store survival information as a two-column table, time and event, in that order.    

+  bindedMeasurements <- cbind(measurementsTrain, time = survivalTrain[, 1], event = survivalTrain[, 2])

   mtry <- round(mTryProportion * ncol(measurementsTrain)) # Number of features to try.

   randomForestSRC::rfsrc(Surv(time, event) ~ ., data = as.data.frame(bindedMeasurements), mtry = mtry,

                           var.used = "all.trees", importance = TRUE, ...)

@@ -13,7 +13,8 @@

 #' are features.

 #' @param outcome Either a factor vector of classes, a \code{\link{Surv}} object, or

 #' a character string, or vector of such strings, containing column name(s) of column(s)

-#' containing either classes or time and event information about survival.

+#' containing either classes or time and event information about survival. If column names

+#' of survival information, time must be in first column and event status in the second.

 #' @param outcomeColumns If \code{measurements} is a \code{MultiAssayExperiment}, the

 #' names of the column (class) or columns (survival) in the table extracted by \code{colData(data)}

 #' that contain(s) the each individual's outcome to use for prediction.

@@ -4,7 +4,7 @@ coxphRanking <- function(measurementsTrain, survivalTrain, verbose = 3) # Clinic

   pValues <- rep(NA, ncol(measurementsTrain))

   names(pValues) <- colnames(measurementsTrain)

-  

+

   isCat <- sapply(measurementsTrain, class) %in% c("character", "factor")

   if(any(isCat))

   {

@@ -16,7 +16,8 @@

 #' \code{matrix} or \code{\link{DataFrame}}, the rows are samples, and the columns are features.

 #' @param outcomeTrain Either a factor vector of classes, a \code{\link{Surv}} object, or

 #' a character string, or vector of such strings, containing column name(s) of column(s)

-#' containing either classes or time and event information about survival.

+#' containing either classes or time and event information about survival. If column names

+#' of survival information, time must be in first column and event status in the second.

 #' @param measurementsTest Same data type as \code{measurementsTrain}, but only the test

 #' samples.

 #' @param outcomeTest Same data type as \code{outcomeTrain}, but for only the test

@@ -256,12 +257,19 @@ input data. Autmomatically reducing to smaller number.")

     {

       if(is.null(modellingParams@trainParams@getFeatures))

       selectedFeatures <- originalFeatures[selectedFeaturesIndices]

-      else selectedFeatures <- colnames(measurementsTrain)[rankedFeaturesIndices] 

+      else selectedFeatures <- colnames(measurementsTrain)[selectedFeaturesIndices] 

     } else {

       featureColumns <- na.omit(match(c("assay", "feature"), colnames(S4Vectors::mcols(measurementsTrain))))

-      if(is.null(modellingParams@trainParams@getFeatures))

-      selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]

-      else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]

+      if(length(featureColumns) == 1)

+      {

+         if(is.null(modellingParams@trainParams@getFeatures))

+            selectedFeatures <- originalFeatures[selectedFeaturesIndices]

+         else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]            

+      } else {

+                if(is.null(modellingParams@trainParams@getFeatures))

+                  selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]

+                else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]            

+      }

     }

     importanceTable <- S4Vectors::DataFrame(selectedFeatures, performanceChanges)

     if(ncol(importanceTable) == 2) colnames(importanceTable)[1] <- "feature"

@@ -272,6 +280,7 @@ input data. Autmomatically reducing to smaller number.")

   if(!is.null(tuneDetailsSelect)) tuneDetails <- tuneDetailsSelect else tuneDetails <- tuneDetailsTrain

   # Convert back into original, potentially unsafe feature identifiers unless it is a nested cross-validation.

+  

   if(is.null(.iteration) || .iteration != "internal")

   {

     if(!is.null(rankedFeaturesIndices))

@@ -283,9 +292,16 @@ input data. Autmomatically reducing to smaller number.")

         else rankedFeatures <- colnames(measurementsTrain)[rankedFeaturesIndices]            

       } else {

         featureColumns <- na.omit(match(c("assay", "feature"), colnames(S4Vectors::mcols(measurementsTrain))))          

-        if(is.null(modellingParams@trainParams@getFeatures))

-          rankedFeatures <- originalFeatures[rankedFeaturesIndices, ]

-        else rankedFeatures <- S4Vectors::mcols(measurementsTrain)[rankedFeaturesIndices, featureColumns]

+        if(length(featureColumns) == 1)

+        {

+          if(is.null(modellingParams@trainParams@getFeatures))

+            rankedFeatures <- originalFeatures[rankedFeaturesIndices]

+          else rankedFeatures <- S4Vectors::mcols(measurementsTrain)[rankedFeaturesIndices, featureColumns]

+        } else {

+          if(is.null(modellingParams@trainParams@getFeatures))

+            rankedFeatures <- originalFeatures[rankedFeaturesIndices, ]

+          else rankedFeatures <- S4Vectors::mcols(measurementsTrain)[rankedFeaturesIndices, featureColumns] 

+        }

       }

     } else { rankedFeatures <- NULL}

     if(!is.null(selectedFeaturesIndices))

@@ -297,9 +313,16 @@ input data. Autmomatically reducing to smaller number.")

         else selectedFeatures <- colnames(measurementsTrain)[selectedFeaturesIndices]

       } else {

         featureColumns <- na.omit(match(c("assay", "feature"), colnames(S4Vectors::mcols(measurementsTrain))))  

-        if(is.null(modellingParams@trainParams@getFeatures))

-          selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]

-        else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]

+        if(length(featureColumns) == 1)

+        {

+          if(is.null(modellingParams@trainParams@getFeatures))

+            selectedFeatures <- originalFeatures[selectedFeaturesIndices]

+          else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]            

+        } else {

+                if(is.null(modellingParams@trainParams@getFeatures))

+                  selectedFeatures <- originalFeatures[selectedFeaturesIndices, ]

+                else selectedFeatures <- S4Vectors::mcols(measurementsTrain)[selectedFeaturesIndices, featureColumns]            

+        }

       }

     } else { selectedFeatures <- NULL}

   } else { # Nested use in feature selection. No feature selection in inner execution, so ignore features. 

@@ -16,7 +16,8 @@

 #' containing either classes or time and event information about survival. If

 #' \code{measurements} is a \code{MultiAssayExperiment}, the names of the column (class) or

 #' columns (survival) in the table extracted by \code{colData(data)} that contain(s) the samples'

-#' outcome to use for prediction.

+#' outcome to use for prediction. If column names of survival information, time must be in first

+#' column and event status in the second.

 #' @param crossValParams An object of class \code{\link{CrossValParams}},

 #' specifying the kind of cross-validation to be done.

 #' @param modellingParams An object of class \code{\link{ModellingParams}},

@@ -13,7 +13,7 @@

 #' a matrix of pre-calculated metrics, for backwards compatibility.

 #' @param classes If \code{results} is a matrix, this is a factor vector of the

 #' same length as the number of columns that \code{results} has.

-#' @param comparison Default: "Classifier Name". The aspect of the experimental

+#' @param comparison Default: "auto". The aspect of the experimental

 #' design to compare. Can be any characteristic that all results share.

 #' @param metric Default: "Sample Error". The sample-wise metric to plot.

 #' @param featureValues If not NULL, can be a named factor or named numeric

@@ -44,6 +44,8 @@

 #' @param legendSize The size of the boxes in the legends.

 #' @param plot Logical. IF \code{TRUE}, a plot is produced on the current

 #' graphics device.

+#' @param ... Parameters not used by the \code{ClassifyResult} method that does

+#' list-packaging but used by the main \code{list} method.

 #' @return A plot is produced and a grob is returned that can be saved to a

 #' graphics device.

 #' @author Dario Strbenac

@@ -82,11 +84,17 @@

 setGeneric("samplesMetricMap", function(results, ...)

 standardGeneric("samplesMetricMap"))

+#' @rdname samplesMetricMap

+#' @export

+setMethod("samplesMetricMap", "ClassifyResult", function(results, ...) {

+    samplesMetricMap(list(assay = results), ...)

+})

+

 #' @rdname samplesMetricMap

 #' @export

 setMethod("samplesMetricMap", "list", 

           function(results,

-                   comparison = "Classifier Name",

+                   comparison = "auto",

                    metric = c("Sample Error", "Sample Accuracy", "Sample C-index"),

                    featureValues = NULL, featureName = NULL,

                    metricColours = list(c("#3F48CC", "#6F75D8", "#9FA3E5", "#CFD1F2", "#FFFFFF"),

@@ -103,6 +111,20 @@ setMethod("samplesMetricMap", "list",

     stop("The package 'gridExtra' could not be found. Please install it.")       

   if(!requireNamespace("gtable", quietly = TRUE))

     stop("The package 'gtable' could not be found. Please install it.")

+  

+  characteristicsCounts <- table(unlist(lapply(results, function(result) result@characteristics[["characteristic"]])))

+  if(comparison == "auto")

+  {

+    if(max(characteristicsCounts) == length(results))

+    { # Choose a characteristic which varies the most across the results.

+      candidates <- names(characteristicsCounts)[characteristicsCounts == length(results)]

+      allCharacteristics <- do.call(rbind, lapply(results, function(result) result@characteristics))

+      distinctValues <- by(allCharacteristics[, "value"], allCharacteristics[, "characteristic"], function(values) length(unique(values)))

+      comparison <- names(distinctValues)[which.max(distinctValues)][1]

+    } else {

+      stop("No characteristic is present for all results but must be.")

+    }

+  }

   resultsWithComparison <- sum(sapply(results, function(result) any(result@characteristics[, "characteristic"] == comparison)))

   if(resultsWithComparison < length(results))

     stop("Not all results have comparison characteristic ", comparison, ' but need to.')

new file mode 100644

@@ -0,0 +1,25 @@

+# ClassifyR: Performance evaluation for multi-view data sets and seamless integration with MultiAssayExperiment and Bioconductor

+

+<img src="man/figures/ClassifyRsticker.png" align="right" width=250 style="margin-left: 10px;">

+

+ClassifyR's performance evaluation focuses on model stability and interpretability. Based on repeated cross-validation, it is possible to evaluate feature selection stability and also per-sample prediction accuracy. Also, multiple omics data assays on the same samples are becoming more popular and ClassifyR supports a range of multi-view methods to evaluate which data view is the most predictive and combine data views to evaluate if multiple views provide superior predictive performance to a single data view.

+

+##  Installation 

+

+The recommended method of installing ClassifyR is by using Bioconductor's BiocManager installer:

+

+```

+library(BiocManager)

+install("ClassifyR", dependencies = TRUE)

+```

+

+The above code will install all packages that provide feature selection or model-building functionality. If only one or two methods are desired then the dependencies option could be omitted and those packages providing functionality installed manually. 

+ 

+##  Website

+

+Please visit [the ClassifyR website](https://sydneybiox.github.io/ClassifyR/) to view the main vignette as well as articles that provide more in-depth explanations for various aspects of the package. Details of performance evaluation, multi-view methods and contributing a wrapper for a new algorithm to the package are provided.

+

+

+## Reference

+

+Strbenac D., Mann, G.J., Ormerod, J.T., and Yang, J. Y. H. (2015) ClassifyR: An R package for performance assessment of classification with applications to transcriptomics, *Bioinformatics*.

new file mode 100644

@@ -0,0 +1,15 @@

+url: https://sydneybiox.github.io/ClassifyR/

+template:

+  bootstrap: 5

+articles:

+- title: Menu

+  contents:

+    - introduction

+    - performanceEvaluation

+    - multiViewMethods

+    - incorporateNew

+    - ClassifyR

+    - DevelopersGuide

+navbar:

+  title: ~

+  bg: dark

new file mode 100644

@@ -0,0 +1,86 @@

+<!DOCTYPE html>

+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">

+<head>

+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">

+<meta charset="utf-8">

+<meta http-equiv="X-UA-Compatible" content="IE=edge">

+<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

+<title>Page not found (404) • ClassifyR</title>

+<script src="https://sydneybiox.github.io/ClassifyR/deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">

+<link href="https://sydneybiox.github.io/ClassifyR/deps/bootstrap-5.1.3/bootstrap.min.css" rel="stylesheet">

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+  <main id="main" class="col-md-9"><div class="page-header">

+      <img src="" class="logo" alt=""><h1>An Introduction to ClassifyR</h1>

+                        <h4 data-toc-skip class="author">Dario Strbenac,

+Ellis Patrick, Graham Mann, Jean Yang, John Ormerod <br> The University

+of Sydney, Australia.</h4>

+            

+      

+      

+      <div class="d-none name"><code>ClassifyR.Rmd</code></div>

+    </div>

+

+    

+    

+<div class="section level2">

+<h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>

+</h2>

+<p>Typically, each feature selection method or classifier originates

+from a different R package, which <strong>ClassifyR</strong> provides a

+wrapper around. By default, only high-performance t-test/F-test and

+random forest are installed. If you intend to compare between numerous

+different modelling methods, you should install all suggested packages

+at once by using the command

+<code>BiocManager::install("ClassifyR", dependencies = TRUE)</code>.

+This will take a few minutes, particularly on Linux, because each

+package will be compiled from source code.</p>

+</div>

+<div class="section level2">

+<h2 id="overview">Overview<a class="anchor" aria-label="anchor" href="#overview"></a>

+</h2>

+<p><strong>ClassifyR</strong> provides a structured pipeline for

+cross-validated classification. Classification is viewed in terms of

+four stages, data transformation, feature selection, classifier

+training, and prediction. The driver functions <em>crossValidate</em>

+and <em>runTests</em> implements varieties of cross-validation. They

+are:</p>

+<ul>

+<li>Permutation of the order of samples followed by k-fold

+cross-validation (runTests only)</li>

+<li>Repeated x% test set cross-validation</li>

+<li>leave-k-out cross-validation</li>

+</ul>

+<p>Driver functions can use parallel processing capabilities in R to

+speed up cross-validations when many CPUs are available. The output of

+the driver functions is a <em>ClassifyResult</em> object which can be

+directly used by the performance evaluation functions. The process of

+classification is summarised by a flowchart.</p>

+<img src="data:image/png;base64,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" style="margin-left: auto;margin-right: auto"><p>Importantly, ClassifyR implements a number of methods for

+classification using different kinds of changes in measurements between

+classes. Most classifiers work with features where the means are

+different. In addition to changes in means (DM),

+<strong>ClassifyR</strong> also allows for classification using

+differential variability (DV; changes in scale) and differential

+distribution (DD; changes in location and/or scale).</p>

+<div class="section level3">

+<h3 id="case-study-diagnosing-asthma">Case Study: Diagnosing Asthma<a class="anchor" aria-label="anchor" href="#case-study-diagnosing-asthma"></a>

+</h3>

+<p>To demonstrate some key features of ClassifyR, a data set consisting

+of the 2000 most variably expressed genes and 190 people will be used to

+quickly obtain results. The journal article corresponding to the data

+set was published in <em>Scientific Reports</em> in 2018 and is titled

+<a href="http://www.nature.com/articles/s41598-018-27189-4" class="external-link">A Nasal

+Brush-based Classifier of Asthma Identified by Machine Learning Analysis

+of Nasal RNA Sequence Data</a>.</p>

+<p>Load the package.</p>

+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html" class="external-link">library</a></span><span class="op">(</span><span class="va"><a href="https://sydneybiox.github.io/ClassifyR/">ClassifyR</a></span><span class="op">)</span></span></code></pre></div>

+<p>A glimpse at the RNA measurements and sample classes.</p>

+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">asthma</span><span class="op">)</span> <span class="co"># Contains measurements and classes variables.</span></span>

+<span><span class="va">measurements</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">5</span>, <span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">]</span></span></code></pre></div>

+<pre><code><span><span class="co">##            HBB BPIFA1  XIST FCGR3B HBA2</span></span>

+<span><span class="co">## Sample 1  9.72  14.06 12.28  11.42 7.83</span></span>

+<span><span class="co">## Sample 2 11.98  13.89  6.35  13.25 9.42</span></span>

+<span><span class="co">## Sample 3 12.15  17.44 10.21   7.87 9.68</span></span>

+<span><span class="co">## Sample 4 10.60  11.87  6.27  14.75 8.96</span></span>

+<span><span class="co">## Sample 5  8.18  15.01 11.21   6.77 6.43</span></span></code></pre>

+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">classes</span><span class="op">)</span></span></code></pre></div>

+<pre><code><span><span class="co">## [1] No  No  No  No  Yes No </span></span>

+<span><span class="co">## Levels: No Yes</span></span></code></pre>

+<p>The numeric matrix variable <em>measurements</em> stores the

+normalised values of the RNA gene abundances for each sample and the

+factor vector <em>classes</em> identifies which class the samples belong

+to. The measurements were normalised using <strong>DESeq2</strong>’s

+<em>varianceStabilizingTransformation</em> function, which produces

+<span class="math inline">\(log_2\)</span>-like data.</p>

+<p>For more complex data sets with multiple kinds of experiments

+(e.g. DNA methylation, copy number, gene expression on the same set of

+samples) a <a href="https://bioconductor.org/packages/release/bioc/html/MultiAssayExperiment.html" class="external-link"><em>MultiAssayExperiment</em></a>

+is recommended for data storage and supported by

+<strong>ClassifyR</strong>’s methods.</p>

+</div>

+</div>

+<div class="section level2">

+<h2 id="quick-start-crossvalidate-function">Quick Start: <em>crossValidate</em> Function<a class="anchor" aria-label="anchor" href="#quick-start-crossvalidate-function"></a>

+</h2>

+<p>The <em>crossValidate</em> function offers a quick and simple way to

+start analysing a dataset in ClassifyR. It is a wrapper for

+<em>runTests</em>, the core model building and testing function of

+ClassifyR. <em>crossValidate</em> must be supplied with

+<em>measurements</em>, a simple tabular data container or a list-like

+structure of such related tabular data on common samples. The classes of

+it may be <em>matrix</em>, <em>data.frame</em>, <em>DataFrame</em>,

+<em>MultiAssayExperiment</em> or <em>list</em> of <em>data.frames</em>.

+For a dataset with <span class="math inline">\(n\)</span> observations

+and <span class="math inline">\(p\)</span> variables, the

+<em>crossValidate</em> function will accept inputs of the following

+shapes:</p>

+<table class="table">

+<colgroup>

+<col width="25%">

+<col width="37%">

+<col width="37%">

+</colgroup>

+<thead><tr class="header">

+<th>Data Type</th>

+<th align="center"><span class="math inline">\(n \times p\)</span></th>

+<th align="center"><span class="math inline">\(p \times n\)</span></th>

+</tr></thead>

+<tbody>

+<tr class="odd">

+<td><span style="font-family: 'Courier New', monospace;">matrix</span></td>

+<td align="center">✔</td>

+<td align="center"></td>

+</tr>

+<tr class="even">

+<td><span style="font-family: 'Courier New', monospace;">data.frame</span></td>

+<td align="center">✔</td>

+<td align="center"></td>

+</tr>

+<tr class="odd">

+<td><span style="font-family: 'Courier New', monospace;">DataFrame</span></td>

+<td align="center">✔</td>

+<td align="center"></td>

+</tr>

+<tr class="even">

+<td><span style="font-family: 'Courier New', monospace;">MultiAssayExperiment</span></td>

+<td align="center"></td>

+<td align="center">✔</td>

+</tr>

+<tr class="odd">

+<td>

+<span style="font-family: 'Courier New', monospace;">list</span> of

+<span style="font-family: 'Courier New', monospace;">data.frame</span>s</td>

+<td align="center">✔</td>

+<td align="center"></td>

+</tr>

+</tbody>

+</table>

+<p><em>crossValidate</em> must also be supplied with <em>outcome</em>,

+which represents the prediction to be made in a variety of possible

+ways.</p>

+<ul>

+<li>A <em>factor</em> that contains the class label for each

+observation. <em>classes</em> must be of length <span class="math inline">\(n\)</span>.</li>

+<li>A <em>character</em> of length 1 that matches a column name in a

+data frame which holds the classes. The classes will automatically be

+removed before training is done.</li>

+<li>A <em>Surv</em> object of the same length as the number of samples

+in the data which contains information about the time and censoring of

+the samples.</li>

+<li>A <em>character</em> vector of length 2 or 3 that each match a

+column name in a data frame which holds information about the time and

+censoring of the samples. The time-to-event columns will automatically

+be removed before training is done.</li>

+</ul>

+<p>The type of classifier used can be changed with the

+<em>classifier</em> argument. The default is a random forest, which

+seamlessly handles categorical and numerical data. A full list of

+classifiers can be seen by running <em>?crossValidate</em>. A feature

+selection step can be performed before classification using

+<em>nFeatures</em> and <em>selectionMethod</em>, which is a t-test by

+default. Similarly, the number of folds and number of repeats for cross

+validation can be changed with the <em>nFolds</em> and <em>nRepeats</em>

+arguments. If wanted, <em>nCores</em> can be specified to run the cross

+validation in parallel. To perform 5-fold cross-validation of a Support

+Vector Machine with 2 repeats:</p>

+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span><span class="va">result</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/crossValidate.html">crossValidate</a></span><span class="op">(</span><span class="va">measurements</span>, <span class="va">classes</span>, classifier <span class="op">=</span> <span class="st">"SVM"</span>,</span>

+<span>                        nFeatures <span class="op">=</span> <span class="fl">20</span>, nFolds <span class="op">=</span> <span class="fl">5</span>, nRepeats <span class="op">=</span> <span class="fl">2</span>, nCores <span class="op">=</span> <span class="fl">1</span><span class="op">)</span></span></code></pre></div>

+<pre><code><span><span class="co">## Processing sample set 10.</span></span></code></pre>

+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span><span class="fu"><a href="../reference/performancePlot.html">performancePlot</a></span><span class="op">(</span><span class="va">result</span><span class="op">)</span></span></code></pre></div>

+<pre><code><span><span class="co">## Warning in .local(results, ...): Balanced Accuracy not found in all elements of results. Calculating it now.</span></span></code></pre>

+<p><img src="ClassifyR_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>

+<div class="section level3">

+<h3 id="data-integration-with-crossvalidate">Data Integration with crossValidate<a class="anchor" aria-label="anchor" href="#data-integration-with-crossvalidate"></a>