Incorporate Bioconductor recommendations

.gitignore

@@ -10,4 +10,4 @@ renv/
 renv.lock
 .Rprofile
 .idea
-
+*.Rproj

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: miaViz
 Title: Microbiome Analysis Plotting and Visualization
-Version: 1.15.4
+Version: 1.15.5
 Authors@R: 
     c(person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
              email = "[email protected]",
@@ -47,8 +47,6 @@ Imports:
     dplyr,
     ggnewscale,
     ggrepel,
-    ggplot2,
-    ggraph,
     ggtree,
     methods,
     rlang,
@@ -79,3 +77,6 @@ Remotes:
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.2
 VignetteBuilder: knitr
+URL: https://github.com/microbiome/miaViz
+BugReports: https://github.com/microbiome/miaViz/issues
+

NAMESPACE

@@ -58,9 +58,13 @@ importFrom(BiocParallel,bpstop)
 importFrom(DelayedArray,rowMeans)
 importFrom(DelayedArray,rowSums)
 importFrom(DirichletMultinomial,mixture)
+importFrom(S4Vectors,SimpleList)
 importFrom(S4Vectors,metadata)
+importFrom(S4Vectors,unfactor)
 importFrom(SingleCellExperiment,reducedDim)
 importFrom(SingleCellExperiment,reducedDimNames)
+importFrom(SingleCellExperiment,reducedDims)
+importFrom(SummarizedExperiment,"rowData<-")
 importFrom(SummarizedExperiment,assay)
 importFrom(SummarizedExperiment,colData)
 importFrom(SummarizedExperiment,rowData)
@@ -69,6 +73,7 @@ importFrom(ape,drop.tip)
 importFrom(ape,keep.tip)
 importFrom(ape,rotateConstr)
 importFrom(dplyr,"%>%")
+importFrom(dplyr,across)
 importFrom(dplyr,all_of)
 importFrom(dplyr,arrange)
 importFrom(dplyr,bind_cols)

R/AllGenerics.R

@@ -0,0 +1,131 @@
+# All generic methods are listed here
+
+#' @rdname getNeatOrder
+setGeneric("getNeatOrder", signature = c("x"),
+    function(x, centering = "mean", ...)
+    standardGeneric("getNeatOrder"))
+
+#' @rdname plotAbundance
+setGeneric("plotAbundance", signature = c("x"), function(x, ...)
+    standardGeneric("plotAbundance"))
+
+#' @rdname plotAbundanceDensity
+#' @export
+setGeneric("plotAbundanceDensity", signature = c("x"), function(x, ...)
+    standardGeneric("plotAbundanceDensity"))
+
+#' @rdname plotCCA
+#' @aliases plotRDA
+#' @export
+setGeneric("plotCCA", signature = c("x"), function(x, ...)
+    standardGeneric("plotCCA"))
+
+#' @rdname plotCCA
+#' @aliases plotCCA
+#' @export
+setGeneric("plotRDA", signature = c("x"), function(x, ...)
+    standardGeneric("plotRDA"))
+
+#' @rdname plotColTile
+#' @export
+setGeneric("plotColTile", signature = c("object"),
+    function(object, x, y, ...)
+    standardGeneric("plotColTile"))
+
+#' @rdname plotColTile
+#' @export
+setGeneric("plotRowTile", signature = c("object"),
+    function(object, x, y, ...)
+    standardGeneric("plotRowTile"))
+
+#' @rdname plotDMN
+#' @export
+setGeneric("plotDMNFit", signature = "x",
+    function(x, name = "DMN", type = c("laplace","AIC","BIC"), ...)
+    standardGeneric("plotDMNFit"))
+
+#' @rdname plotGraph
+#' @export
+setGeneric("plotColGraph", signature = c("x","y"),
+    function(x, y, ...)
+    standardGeneric("plotColGraph"))
+
+#' @rdname plotGraph
+#' @export
+setGeneric("plotRowGraph", signature = c("x","y"),
+    function(x, y, ...)
+    standardGeneric("plotRowGraph"))
+
+#' @rdname plotLoadings
+setGeneric("plotLoadings", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotLoadings"))
+
+#' @rdname plotPrevalence
+#' @export
+setGeneric("plotRowPrevalence", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotRowPrevalence"))
+
+#' @rdname plotPrevalence
+#' @export
+setGeneric("plotPrevalentAbundance", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotPrevalentAbundance"))
+
+#' @rdname plotPrevalence
+#' @export
+setGeneric("plotPrevalence", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotPrevalence"))
+
+#' @rdname plotScree
+#' @export
+setGeneric("plotScree", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotScree"))
+
+#' @rdname plotSeries
+#' @export
+setGeneric("plotSeries", signature = c("object"),
+    function(object, ...)
+    standardGeneric("plotSeries"))
+
+#' @rdname plotTree
+setGeneric("plotRowTree", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotRowTree"))
+#' @rdname plotTree
+setGeneric("plotColTree", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotColTree"))
+
+#' @rdname treeData
+setGeneric("rowTreeData", signature = c("x"),
+    function(x, ...)
+    standardGeneric("rowTreeData"))
+
+#' @rdname treeData
+setGeneric("colTreeData", signature = c("x"),
+    function(x, ...)
+    standardGeneric("colTreeData"))
+
+#' @rdname treeData
+setGeneric("rowTreeData<-", signature = c("x"),
+    function(x, tree.name = tree_name, tree_name = "phylo", value)
+    standardGeneric("rowTreeData<-"))
+
+#' @rdname treeData
+setGeneric("colTreeData<-", signature = c("x"),
+    function(x, tree.name = tree_name, tree_name = "phylo", value)
+    standardGeneric("colTreeData<-"))
+
+#' @rdname treeData
+setGeneric("combineTreeData", signature = c("x"),
+    function(x, other.fields = other_fields, other_fields = list())
+    standardGeneric("combineTreeData"))
+
+#' @rdname treeData
+setGeneric("combineTreeData", signature = c("x"),
+    function(x, other.fields = other_fields, other_fields = list())
+    standardGeneric("combineTreeData"))

R/deprecate.R

@@ -0,0 +1,42 @@
+#' These functions will be deprecated. Please use other functions instead.
+#'
+#' @param x -
+#'
+#' @param ... -
+#'
+#' @name deprecate
+NULL
+
+#' @rdname deprecate
+#' @export
+setGeneric("plotTaxaPrevalence", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotTaxaPrevalence"))
+
+#' @rdname deprecate
+#' @export
+setMethod("plotTaxaPrevalence", signature = c(x = "ANY"), function(x, ...){
+    .Deprecated(
+        old ="plotTaxaPrevalence", new = "plotRowPrevalence",
+        msg = paste0("The 'plotTaxaPrevalence' function is ",
+            "deprecated. Use 'plotRowPrevalence' instead."))
+    plotRowPrevalence(x, ...)
+    }
+)
+
+#' @rdname deprecate
+#' @export
+setGeneric("plotFeaturePrevalence", signature = c("x"),
+    function(x, ...)
+    standardGeneric("plotFeaturePrevalence"))
+
+#' @rdname deprecate
+#' @export
+setMethod("plotFeaturePrevalence", signature = c(x = "ANY"), function(x, ...){
+    .Deprecated(
+        old ="plotFeaturePrevalence", new = "plotRowPrevalence",
+        msg = paste0("The 'plotFeaturePrevalence' function is ",
+            "deprecated. Use 'plotRowPrevalence' instead."))
+    plotRowPrevalence(x, ...)
+    }
+)

R/getNeatOrder.R

@@ -1,81 +1,82 @@
 #' Sorting by radial theta angle
-#' 
+#'
 #' @description \code{getNeatOrder} sorts already ordinated data by the radial
 #' theta angle. This method is useful for organizing data points based on their
 #' angular position in a 2D space, typically after an ordination technique such
-#' as PCA or NMDS has been applied. 
-#' 
-#' The function takes in a matrix of ordinated data, optionally 
+#' as PCA or NMDS has been applied.
+#'
+#' The function takes in a matrix of ordinated data, optionally
 #' centers the data using specified methods (\code{mean}, \code{median}, or
 #' \code{NULL}), and then calculates the angle (theta) for each point relative
 #' to the centroid. The data points are then sorted based on these theta values
-#' in ascending order. 
-#' 
-#' One significant application of this sorting method is in plotting heatmaps. 
+#' in ascending order.
+#'
+#' One significant application of this sorting method is in plotting heatmaps.
 #' By using radial theta sorting, the relationships between data points can be
 #' preserved according to the ordination method's spatial configuration, rather
 #' than relying on hierarchical clustering, which may distort these
 #' relationships. This approach allows for a more faithful representation of the
 #' data's intrinsic structure as captured by the ordination process.
-#' 
+#'
 #' @param x A matrix containing the ordinated data to be sorted. Columns should
 #' represent the principal components (PCs) and rows should represent the
 #' entities being analyzed (e.g. features or samples). There should be 2 columns
 #' only representing 2 PCs.
-#' 
+#'
 #' @param centering \code{Character scalar}. Specifies the method to
 #' center the data. Options are \code{"mean"}, \code{"median"}, or \code{NULL}
 #' if your data is already centered. (Default: \code{"mean"})
-#' 
+#'
 #' @param ... Additional arguments passed to other methods.
 #'
 #' @return A \code{character} vector of row indices in the sorted order.
-#' 
-#' @details 
+#'
+#' @details
 #' It's important to note that the
 #' [\pkg{sechm}](https://bioconductor.org/packages/3.18/bioc/vignettes/sechm/inst/doc/sechm.html#row-ordering)
 #' package does actually have the functionality for plotting a heatmap using
-#' this radial theta angle ordering, though only by using an  MDS ordination. 
-#'   
+#' this radial theta angle ordering, though only by using an  MDS ordination.
+#'
 #' That being said, the \code{getNeatOrder} function is more modular and
 #' separate to the plotting, and can be applied to any kind of ordinated data
 #' which can be valuable depending on the use case.
-#' 
-#' [Rajaram & Oono (2010) NeatMap - non-clustering heat map alternatives in R](https://doi.org/10.1186/1471-2105-11-45) outlines this in more detail.
-#' 
+#'
+#' [Rajaram & Oono (2010) NeatMap - non-clustering heat map alternatives in R](https://doi.org/10.1186/1471-2105-11-45)
+#' outlines this in more detail.
+#'
 #' @name getNeatOrder
-#' 
+#'
 #' @examples
 #' # Load the required libraries and dataset
 #' library(mia)
 #' library(scater)
 #' library(ComplexHeatmap)
 #' library(circlize)
 #' data(peerj13075)
-#' 
+#'
 #' # Group data by taxonomic order
 #' tse <- agglomerateByRank(peerj13075, rank = "order", onRankOnly = TRUE)
-#' 
+#'
 #' # Transform the samples into relative abundances using CLR
 #' tse <- transformAssay(
 #'     tse, assay.type = "counts", method="clr", MARGIN = "cols",
 #'     name="clr", pseudocount = TRUE)
-#' 
+#'
 #' # Transform the features (taxa) into zero mean, unit variance
 #' # (standardize transformation)
 #' tse <- transformAssay(
 #'     tse, assay.type="clr", method="standardize", MARGIN = "rows")
-#' 
+#'
 #' # Perform PCA using calculatePCA
 #' res <- calculatePCA(tse, assay.type = "standardize", ncomponents = 10)
-#' 
+#'
 #' # Sort by radial theta and sort the original assay data
 #' sorted_order <- getNeatOrder(res[, c(1,2)], centering = "mean")
 #' tse <- tse[, sorted_order]
-#' 
+#'
 #' # Define the color function and cap the colors at [-5, 5]
 #' col_fun <- colorRamp2(c(-5, 0, 5), c("blue", "white", "red"))
-#' 
+#'
 #' # Create the heatmap
 #' heatmap <- Heatmap(assay(tse, "standardize"),
 #'               name = "NeatMap",
@@ -84,20 +85,14 @@
 #'               cluster_columns = FALSE,  # Do not cluster columns
 #'               show_row_dend = FALSE,
 #'               show_column_dend = FALSE,
-#'               row_names_gp = gpar(fontsize = 4), 
-#'               column_names_gp = gpar(fontsize = 6), 
-#'               heatmap_width = unit(20, "cm"),  
-#'               heatmap_height = unit(15, "cm")  
+#'               row_names_gp = gpar(fontsize = 4),
+#'               column_names_gp = gpar(fontsize = 6),
+#'               heatmap_width = unit(20, "cm"),
+#'               heatmap_height = unit(15, "cm")
 #' )
-#' 
+#'
 NULL
 
-#' @rdname getNeatOrder
-setGeneric("getNeatOrder", signature = c("x"),
-    function(x, centering = "mean", ...)
-        standardGeneric("getNeatOrder"))
-
-
 # Implementation for taking in a raw matrix.
 #' @rdname getNeatOrder
 #' @export
@@ -147,7 +142,7 @@ setMethod("getNeatOrder", signature = c("matrix"),
         center_fun <- switch(centering, "median" = median, "mean" = mean)
         center_vals <- apply(data, 2, center_fun)
         data <- scale(data, center = center_vals, scale = FALSE)
-    } 
+    }
     # Compute the radial theta values using the centered data
     theta <- atan2(data[, 2], data[, 1])
     # Set the names of theta values to the row names of the centered data and