imcRtools: Methods for imaging mass cytometry data analysis

Documented in buildSpatialGraph

#' @title Builds an interaction graph based on the cells' locations
#'
#' @description Function to define cell-cell interactions via distance-based
#' expansion, delaunay triangulation or k nearest neighbor detection.
#'
#' @param object a \code{SingleCellExperiment} or \code{SpatialExperiment}
#' object
#' @param img_id single character indicating the \code{colData(object)} entry
#' containing the unique image identifiers.
#' @param type single character specifying the type of graph to be build.
#' Supported entries are \code{"expansion"} (default) to find interacting
#' cells via distance thresholding; \code{"delaunay"} to find interactions via
#' delaunay triangulation; \code{"knn"} to find the k nearest neighboring
#' cells.
#' @param threshold (when \code{type = "expansion"}) single numeric specifying
#' the maximum distance for considering neighbors
#' @param k (when \code{type = "knn"}) single numeric integer defining the
#' number of nearest neighbors to search for.
#' @param coords character vector of length 2 specifying the names of the
#' \code{colData} (for a \code{SingleCellExperiment} object) or the
#' \code{spatialCoords} entries of the cells' x and y locations.
#' @param name single character specifying the name of the graph.
#' @param directed (when \code{type = "knn"}) should the returned graph be
#' directed? (see details).
#' @param max_dist (when \code{type = "knn"} or \code{type = "delaunay"}) the
#' maximum distance at which to consider neighboring cells. All neighbors
#' within a distance larger than \code{max_dist} will be excluded from graph
#' construction.
#' @param BNPARAM a \code{\link[BiocNeighbors]{BiocNeighborParam}} object
#' defining the algorithm to use.
#' @param BPPARAM a \code{\link[BiocParallel]{BiocParallelParam-class}} object
#' defining how to parallelize computations.
#' @param ... additional parameters passed to the
#' \code{\link[BiocNeighbors]{findNeighbors}} function (\code{type =
#' "expansion"}), the \code{\link[RTriangle]{triangulate}} function
#' (\code{type = "delaunay"}) or the \code{\link[BiocNeighbors]{findKNN}}
#' function (\code{type = "knn"})).
#'
#' @return returns a \code{SpatialExperiment} or \code{SingleCellExperiment}
#' containing the graph in form of a \code{SelfHits} object in
#' \code{colPair(object, name)}. The object is grouped by entries in the 
#' \code{img_id} slot.
#'
#' @section Building an interaction graph:
#' This function defines interacting cells in different ways. They are based
#' on the cells' centroids and do not incorporate cell shape or area.
#'
#' 1. When \code{type = "expansion"}, all cells within the radius
#' \code{threshold} are considered interacting cells.
#'
#' 2. When \code{type = "delaunay"}, interacting cells are found via a delaunay
#' triangulation of the cells' centroids.
#'
#' 3. When \code{type = "knn"}, interacting cells are defined as the \code{k}
#' nearest neighbors in the 2D spatial plane.
#'
#' The \code{directed} parameter only affects graph construction via k nearest
#' neighbor search. For \code{directed = FALSE}, each interaction will be
#' stored as mutual edge (e.g. node 2 is connected to node 10 and vise versa).
#' For \code{type = "expansion"} and \code{type = "delaunay"}, each edge is
#' stored as mutual edge by default.
#'
#' The graph is stored in form of a \code{SelfHits} object in
#' \code{colPair(object, name)}. This object can be regarded as an edgelist
#' and coerced to an \code{igraph} object via
#' \code{graph_from_edgelist(as.matrix(colPair(object, name)))}.
#'
#' @section Choosing the graph construction method:
#' When finding interactions via \code{expansion} or \code{knn}, the
#' \code{\link[BiocNeighbors]{findNeighbors}} or
#' \code{\link[BiocNeighbors]{findKNN}} functions are used, respectively. Both
#' functions accept the \code{BNPARAM} parameter via which the graph
#' construction method can be defined (default
#' \code{\link[BiocNeighbors]{KmknnParam}}). For an overview on the different
#' algorithms, see
#' \code{\link[BiocNeighbors]{BiocNeighborParam}}. Within the
#' \code{BiocNeighborParam} object, \code{distance} can be set to
#' \code{"Euclidean"} (default), \code{"Manhattan"} or \code{"Cosine"}.
#' 
#' @section Ordering of the output object:
#' The \code{buildSpatialGraph} function operates on individual images.
#' Therefore the returned object is grouped by entries in \code{img_id}. 
#' This means all cells of a given image are grouped together in the object.
#' The ordering of cells within each individual image is the same as the ordering
#' of these cells in the input object.
#'
#' @examples
#' path <- system.file("extdata/mockData/steinbock", package = "imcRtools")
#' spe <- read_steinbock(path)
#'
#' # Constructing a graph via expansion
#' spe <- buildSpatialGraph(spe, img_id = "sample_id",
#'                          type = "expansion", threshold = 10)
#' colPair(spe, "expansion_interaction_graph")
#'
#' # Constructing a graph via delaunay triangulation
#' spe <- buildSpatialGraph(spe, img_id = "sample_id",
#'                          type = "delaunay")
#' colPair(spe, "delaunay_interaction_graph")
#'
#' # Constructing a graph via k nearest neighbor search
#' spe <- buildSpatialGraph(spe, img_id = "sample_id",
#'                          type = "knn", k = 5)
#' colPair(spe, "knn_interaction_graph")
#'
#' @seealso
#' \code{\link[BiocNeighbors]{findNeighbors}} for the function finding
#' interactions via expansion
#'
#' \code{\link[BiocNeighbors]{findKNN}} for the function finding interactions
#' via nearest neighbor search
#'
#' \code{\link[RTriangle]{triangulate}} for the function finding interactions
#' via delaunay triangulation
#'
#' \code{\link{plotSpatial}} for visualizing spatial graphs
#'
#' @author Nils Eling (\email{nils.eling@@dqbm.uzh.ch})
#'
#' @importFrom BiocNeighbors findNeighbors findKNN KmknnParam
#' @importFrom SpatialExperiment spatialCoords
#' @importFrom igraph graph_from_adj_list graph_from_edgelist as.undirected
#' simplify as_edgelist as.directed
#' @importFrom RTriangle triangulate pslg
#' @importFrom stats dist
#' @export
buildSpatialGraph <- function(object,
                                img_id,
                                type = c("expansion", "knn", "delaunay"),
                                k = NULL,
                                directed = TRUE,
                                max_dist = NULL,
                                threshold = NULL,
                                coords = c("Pos_X", "Pos_Y"),
                                name = NULL,
                                BNPARAM = KmknnParam(),
                                BPPARAM = SerialParam(),
                                ...){
    type <- match.arg(type)

    .valid.buildSpatialGraph.input(object, type, img_id, k, threshold, coords,
                                    name, directed, max_dist)

    name <- ifelse(is.null(name), paste0(type, "_interaction_graph"), name)

    cur_ind <- unique(as.character(colData(object)[[img_id]]))

    cur_meta <- metadata(object)
    metadata(object) <- list()
    
    cur_intmeta <- int_metadata(object)

    cur_out <- bplapply(cur_ind,
                        function(x){

                            cur_obj <- object[,
                                as.character(colData(object)[[img_id]]) == x]

                            # Create coords matrix
                            if (is(cur_obj, "SpatialExperiment")) {
                                cur_coords <- spatialCoords(cur_obj)[,coords]
                            } else {
                                cur_coords <- colData(cur_obj)[,coords]
                            }

                            if (type == "expansion") {
                                cur_graph <- findNeighbors(cur_coords,
                                                    threshold = threshold,
                                                get.distance = FALSE,
                                                            BNPARAM = BNPARAM,
                                                            ...)
                                cur_graph <- graph_from_adj_list(
                                    cur_graph$index)
                            } else if (type == "delaunay") {
                                cur_graph <- triangulate(pslg(P = cur_coords),
                                                    ...)

                                cur_edges <- cur_graph$E

                                if (!is.null(max_dist)) {
                                    cur_dist <- apply(cur_edges, 1, function(y){
                                        dist(cur_coords[y,])
                                    })
                                    cur_edges <- cur_edges[cur_dist <= max_dist,]
                                }

                                cur_graph <- graph_from_edgelist(cur_edges,
                                                            directed = FALSE)
                                cur_graph <- as.directed(cur_graph,
                                                        mode = "mutual")

                            } else {
                                if (is.null(max_dist)) {
                                    cur_graph <- findKNN(cur_coords,
                                                    k = k,
                                                    BNPARAM = BNPARAM,
                                                    get.distance = FALSE,
                                                    ...)
                                    cur_graph <- as.list(as.data.frame(
                                        t(cur_graph$index)))
                                    cur_graph <- graph_from_adj_list(cur_graph)
                                } else {
                                    cur_graph <- findKNN(cur_coords,
                                                        k = k,
                                                        BNPARAM = BNPARAM,
                                                        get.distance = TRUE,
                                                        ...)
                                    cur_graph <- lapply(
                                        seq_len(nrow(cur_graph$index)),
                                            function(i){
                                                cur_graph$index[i,
                                        cur_graph$distance[i,] <= max_dist]
                                            })
                                    cur_graph <- graph_from_adj_list(cur_graph)
                                }

                                if (!directed) {
                                    cur_graph <- as.undirected(cur_graph,
                                                            mode = "collapse")
                                    cur_graph <- as.directed(cur_graph,
                                                            mode = "mutual")
                                }
                            }

                            cur_graph <- simplify(cur_graph)

                            cur_graph <- as_edgelist(cur_graph)
                            cur_graph <- SelfHits(from = cur_graph[,1],
                                                    to = cur_graph[,2],
                                                    nnode = nrow(cur_coords))
                            colPair(cur_obj, name) <- cur_graph

                            return(cur_obj)

                        }, BPPARAM = BPPARAM)

    cur_out <- do.call("cbind", cur_out)

    metadata(cur_out) <- cur_meta
    int_metadata(cur_out) <- cur_intmeta
    
    message("The returned object is ordered by the '", img_id, "' entry.")

    return(cur_out)
}