Adding sorting method

.gitignore

@@ -7,5 +7,7 @@ docs
 /doc/
 /Meta/
 renv/
+renv.lock
 .Rprofile
+.idea
 

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: miaViz
 Title: Microbiome Analysis Plotting and Visualization
-Version: 1.13.2
+Version: 1.13.3
 Authors@R: 
     c(person(given = "Tuomas", family = "Borman", role = c("aut", "cre"),
              email = "[email protected]",
@@ -60,7 +60,9 @@ Suggests:
     patchwork,
     vegan,
     microbiomeDataSets,
-    bluster
+    bluster,
+    ComplexHeatmap,
+    circlize
 Remotes:
     github::microbiome/miaTime
 Roxygen: list(markdown = TRUE)

NAMESPACE

@@ -17,6 +17,7 @@ exportMethods("colTreeData<-")
 exportMethods("rowTreeData<-")
 exportMethods(colTreeData)
 exportMethods(combineTreeData)
+exportMethods(getNeatOrder)
 exportMethods(plotAbundance)
 exportMethods(plotAbundanceDensity)
 exportMethods(plotCCA)

NEWS

@@ -28,3 +28,4 @@ Changes in version 1.11.x
 Changes in version 1.13.x
 + plot*Tree: bugfix, ununique nodes
 + Added confidence.level parameter to plotCCA
++ Added getNeatOrder function

R/getNeatOrder.R

@@ -0,0 +1,165 @@
+#' 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 
+#' centers the data using specified methods (\code{mean}, \code{median}, or
+#' \code{none}), 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. 
+#' 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 A single \code{character} value specifying the method to
+#' center the data. Options are \code{mean}, \code{median}, or \code{none} if
+#' your data is already centered. (default: method = \code{"mean"})
+#' 
+#' @param ... Additional arguments passed to other methods.
+#' @return A \code{character} vector of row names in the sorted order.
+#' 
+#' @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. 
+#'   
+#' 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.
+#' 
+#' @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 = "samples",
+#'     name="clr", pseudocount = TRUE)
+#' 
+#' # Transform the features (taxa) into zero mean, unit variance
+#' # (standardize transformation)
+#' tse <- transformAssay(
+#'     tse, assay.type="clr", method="standardize", MARGIN = "features")
+#' 
+#' # 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",
+#'               col = col_fun,
+#'               cluster_rows = FALSE,  # Do not cluster rows
+#'               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")  
+#' )
+NULL
+
+#' @rdname getNeatOrder
+setGeneric("getNeatOrder", signature = c("x"),
+    function(x, centering = "mean", ...)
+        standardGeneric("getNeatOrder"))
+
+
+# Implementation for taking in a raw matrix.
+#' @rdname getNeatOrder
+#' @export
+setMethod("getNeatOrder", signature = c("matrix"),
+    function(x, centering = "mean", ...){
+        # Check args
+        .check_args(x, centering)
+        # Get the theta values and order them
+        theta_values <- .radial_theta(x, centering)
+        ordering <- .get_sorted_rownames(theta_values)
+        return(ordering)
+    }
+)
+
+# Checks the method arguments.
+.check_args <- function(x, centering) {
+    # Check data is a matrix
+    if (!is.matrix(x)) {
+        stop("Input data must be a matrix.", call. = FALSE)
+    }
+    # Check there is sufficient data
+    if (nrow(x) == 0 || ncol(x) == 0) {
+        stop(
+            "No data to plot. Matrix must have at least one row and one ",
+            "column.", call. = FALSE)
+    }
+    # Check there is sufficient data
+    if (ncol(x) != 2) {
+        stop("Matrix must have only 2 columns.", call. = FALSE)
+    }
+    # Check centering argument
+    centering <- match.arg(centering, c("mean", "median", "none"))
+    # Check for unique row names
+    if (any(duplicated(rownames(x)))) {
+        stop("Row names of the matrix must be unique.", call. = FALSE)
+    }
+    # Check for unique column names
+    if (any(duplicated(colnames(x)))) {
+        stop("Column names of the matrix must be unique.", call. = FALSE)
+    }
+    return(NULL)
+}
+
+# Computes the radial theta values for each row in the data matrix.
+.radial_theta <- function(data, centering) {
+    # Choose the correct centering function based on the method
+    center_fun <- switch(centering, "median" = median, "mean" = mean)
+    # Apply the centering if there's a method present
+    if (!is.null(center_fun)) {
+        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
+    # return the theta values
+    names(theta) <- rownames(data)
+    return(theta)
+}
+
+
+# Sorts the theta values and returns the ordered row names.
+.get_sorted_rownames <- function(theta_values) {
+    sorted_indices <- order(theta_values)
+    rownames <- names(theta_values)[sorted_indices]
+    return(rownames)
+}