Implement mofa2 wrapper for MAE

DESCRIPTION

@@ -21,8 +21,8 @@ License: file LICENSE
 Description: The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.
 Encoding: UTF-8
 Depends: R (>= 4.0)
-Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi
-Suggests: knitr, testthat, Seurat, SeuratObject, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown
+Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk, stringi, MultiAssayExperiment, SummarizedExperiment,
+Suggests: knitr, testthat, Seurat, SeuratObject, ggpubr, foreach, psych, SingleCellExperiment, ggrastr, mvtnorm, GGally, rmarkdown, data.table, tidyverse, BiocStyle, Matrix, markdown, mia
 biocViews: DimensionReduction, Bayesian, Visualization
 URL: https://biofam.github.io/MOFA2/index.html
 BugReports: https://github.com/bioFAM/MOFA2

NAMESPACE

@@ -50,6 +50,7 @@ export(impute)
 export(interpolate_factors)
 export(load_model)
 export(make_example_data)
+export(mofa2)
 export(plot_alignment)
 export(plot_ascii_data)
 export(plot_data_heatmap)
@@ -106,6 +107,7 @@ exportMethods(factors_names)
 exportMethods(features_metadata)
 exportMethods(features_names)
 exportMethods(groups_names)
+exportMethods(mofa2)
 exportMethods(samples_metadata)
 exportMethods(samples_names)
 exportMethods(views_names)
@@ -121,8 +123,12 @@ import(reticulate)
 import(tidyr)
 importFrom(DelayedArray,DelayedArray)
 importFrom(HDF5Array,HDF5ArraySeed)
+importFrom(MultiAssayExperiment,experiments)
 importFrom(RColorBrewer,brewer.pal)
 importFrom(Rtsne,Rtsne)
+importFrom(SummarizedExperiment,assay)
+importFrom(SummarizedExperiment,assayNames)
+importFrom(SummarizedExperiment,assays)
 importFrom(basilisk,BasiliskEnvironment)
 importFrom(corrplot,corrplot)
 importFrom(cowplot,plot_grid)

R/create_mofa_from_mae.R

@@ -0,0 +1,106 @@
+#' Create MOFA from MAE
+#'
+#' \code{mofa2} produces a prepared MOFA2 model from a MAE.
+#'
+#' @param mae a \code{\link[MultiAssayExperiment:MultiAssayExperiment]{MultiAssayExperiment}}
+#' 
+#' @param assay.types List of assays to include in MOFA model (default is "counts").
+#' 
+#' @param groups One of the variable names of coldata from which groups should be derived (default is NULL).
+#' 
+#' @param extract_metadata Boolean specifying whethet metadata should be extracted (default is FALSE).
+#' 
+#' @param ... additional parameters for MOFA, named as the options
+#'   of \code{\link[MOFA2:prepare_mofa]{prepare_mofa}}.
+#' 
+#' @return 
+#' Returns an untrained \code{\link[MOFA2:MOFA]{MOFA}} with specified options
+#' filled in the corresponding slots
+#'
+#' @name create_mofa_from_mae
+#' 
+#' @examples
+#' # Load package and import dataset
+#' library(mia)
+#' data("HintikkaXOData", package = "mia")
+#' mae <- HintikkaXOData
+#' 
+#' # Prepare basic model with selected assays
+#' prep_model <- mofa2(mae, assay.types = c("counts", "nmr", "signals"))
+#' 
+#' # Specify grouping variable and extract metadata
+#' prep_model <- mofa2(mae, assay.types = c("counts", "nmr", "signals"),
+#'                     groups = "Diet", extract_metadata = TRUE)
+#'
+#' # Modify MOFA options with corresponding arguments
+#' prep_model <- mofa2(mae, assay.types = c("counts", "nmr", "signals"),
+#'                     num_factors = 5, stochastic = TRUE)
+NULL
+
+#' @rdname create_mofa_from_mae
+#' @export
+setGeneric("mofa2", signature = c("mae"),
+           function(mae, ...) standardGeneric("mofa2")
+)
+
+#' @rdname create_mofa_from_mae
+#' @export
+setMethod("mofa2", signature = c(mae = "MultiAssayExperiment"),
+          function(mae, assay.types = rep("counts", length(mae)),
+                   groups = NULL, extract_metadata = FALSE, ...){
+            # Select assays of each experiment for MOFA
+            mae <- .select_assays(mae, assay.types)
+            # Create MOFA from selected experiments
+            mdl <- create_mofa_from_MultiAssayExperiment(
+              mae,
+              groups = groups,
+              extract_metadata = extract_metadata
+            )
+            # Make a list of arguments for MOFA
+            mofa_args <- list(
+              object = mdl,
+              data_options = .set_opts(get_default_data_options(mdl), ...),
+              model_options = .set_opts(get_default_model_options(mdl), ...),
+              training_options = .set_opts(get_default_training_options(mdl), ...),
+              mefisto_options = .set_opts(get_default_mefisto_options(mdl), ...)
+            )
+            # Add stochastic options if stochastic is turned on
+            if ( mofa_args[["training_options"]][["stochastic"]] ){
+              mofa_args[["stochastic_options"]] <- .set_opts(get_default_stochastic_options(mdl), ...)
+            }
+            # Prepare MOFA
+            prep_mdl <- do.call("prepare_mofa", mofa_args)
+            return(prep_mdl)
+          }
+)
+
+########################## HELP FUNCTIONS ##########################
+
+# Select assays to be included in MOFA
+#' @importFrom MultiAssayExperiment experiments
+#' @importFrom SummarizedExperiment assay assays assayNames
+.select_assays <- function(mae, assay.types) {
+  # Give corresponding experiment names to assay.types
+  names(assay.types) <- names(experiments(mae))
+  # For every experiment in MAE
+  for ( exp in names(experiments(mae)) ){
+    # Keep only selected assay.type from a given experiment
+    assays(mae[[exp]]) <- list(assay(mae[[exp]], assay.types[[exp]]))
+    # Update assay names
+    assayNames(mae[[exp]]) <- assay.types[[exp]]
+  }
+  return(mae)
+}
+
+# Combine custom options found in ... with default options
+.set_opts <- function(default, ...) {
+  # For every option in a set (data, model, train, ...)
+  for ( opt in names(default) ){
+    # If that option is found among arguments
+    if ( opt %in% names(list(...)) ){
+      # Replace default with value specified in arguments
+      default[[opt]] <- list(...)[[opt]]
+    }
+  }
+  return(default)
+}