R/projection-dist.r

In theislab/destiny: Creates diffusion maps

#' Projection distance
#'
#' @param dm        A \code{\link{DiffusionMap}} object.
#' @param new_dcs   Diffusion component matrix of which to calculate the distance to the data.
#' @param ...       Passed to \code{\link[proxy:dist]{proxy::dist}} if \code{new_data} was passed.
#' @param new_data  New data points to project into the diffusion map.
#'                  Can be a \link[base]{matrix}, \link[base]{data.frame},
#'                  \link[Biobase:class.ExpressionSet]{ExpressionSet},
#'                  or \link[SingleCellExperiment]{SingleCellExperiment}.
#' @param verbose   If \code{\link{TRUE}}, log additional info to the console.
#'
#' @return A vector of distances each new data point has to the existing data.
#'
#' @examples
#' data(guo_norm)
#' g2_32 <- guo_norm[, guo_norm$num_cells < 64]
#' g64  <- guo_norm[, guo_norm$num_cells == 64]
#' dm <- DiffusionMap(g2_32)
#' d <- projection_dist(dm, new_data = g64)
#'
#' @export
projection_dist <- function(dm, new_dcs = NULL, ..., new_data, verbose = FALSE) {
	if (is.null(new_dcs))
		new_dcs <- dm_predict(dm, new_data, ..., verbose = verbose)
	else if (!missing(new_data))
		stop('only pass one of new_dcs and new_data')

	evs <- eigenvectors(dm)

	nns <- find_knn(evs, 1, query = as.matrix(new_dcs))

	nn_dist <- nns$dist[, 1]

	nn_dist
}