R/RcppExports.R

In kharchenkolab/sccore: Core Utilities for Single-Cell RNA-Seq

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

#' convert character vector into a factor with names "values" and "levels"
#'
#' @param vals vector of values to evaluate
#' @return factor with names "values" and "levels"
as_factor <- function(vals) {
    .Call('_sccore_as_factor', PACKAGE = 'sccore', vals)
}

#' Get nearest neighbors method on graph
#'
#' @param adjacency_list igraph adjacency list
#' @param transition_probabilities vector of transition probabilites
#' @param n_verts numeric Number of vertices (default=0)
#' @param n_cores numeric Number of cores to use (default=1)
#' @param min_prob numeric Minimum probability for proximity when calculating hitting time per neighbors (default=1e-3)
#' @param min_visited_verts numeric Minimum number of vertices visted when calculating hitting time per neighbors (default=1000)
#' @param min_prob_lower numeric Probability threshold to continue iteration in depth first search hitting time, dfs_hitting_time() (default=1e-5)
#' @param max_hitting_nn_num numeric Maximum adjacencies for calculating hitting time per neighbor, hitting_time_per_neighbors() (default=0)
#' @param max_commute_nn_num numeric Maximum adjacencies for calculating commute time per neighbor, commute_time_per_node() (default=0)
#' @param verbose boolean Whether to have verbose output (default=TRUE)
#' @return list of commute times based on adjacencies
get_nearest_neighbors <- function(adjacency_list, transition_probabilities, n_verts = 0L, n_cores = 1L, min_prob = 1e-3, min_visited_verts = 1000L, min_prob_lower = 1e-5, max_hitting_nn_num = 0L, max_commute_nn_num = 0L, verbose = TRUE) {
    .Call('_sccore_get_nearest_neighbors', PACKAGE = 'sccore', adjacency_list, transition_probabilities, n_verts, n_cores, min_prob, min_visited_verts, min_prob_lower, max_hitting_nn_num, max_commute_nn_num, verbose)
}

#' Jensen–Shannon distance metric (i.e. the square root of the Jensen–Shannon divergence) between the columns of a dense matrix m
#'
#' @param m Input matrix
#' @return Vectorized version of the lower triangle as an R distance object, stats::dist()
#' @examples
#' ex = matrix(1:9, nrow = 3, ncol = 3)
#' jsDist(ex)
#'
jsDist <- function(m) {
    .Call('_sccore_jsDist', PACKAGE = 'sccore', m)
}

#' Calculates factor-stratified sums for each column
#'
#' @param sY sparse matrix (dgCmatrix)
#' @param rowSel integer factor. Note that the 0-th column will return sums for any NA values; 0 or negative values will be omitted
#' @return Matrix
colSumByFactor <- function(sY, rowSel) {
    .Call('_sccore_colSumByFactor', PACKAGE = 'sccore', sY, rowSel)
}

#' Label propagation
#' 
#' @param edge_verts edge vertices of igraph graph object  
#' @param edge_weights edge weights of igraph graph object 
#' @param vert_labels vector of factor or character labels, named by cell names
#' @param max_n_iters integer Maximal number of iterations (default=10)
#' @param verbose boolean Verbose mode (default=TRUE)
#' @param diffusion_fading numeric Constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=10.0)
#' @param diffusion_fading_const numeric Another constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=0.5)
#' @param tol numeric Absolute tolerance as a stopping criteria (default=5e-3)
#' @param fixed_initial_labels boolean Prohibit changes of initial labels during diffusion (default=FALSE)
#' @return matrix from input graph, with labels propagated
propagate_labels <- function(edge_verts, edge_weights, vert_labels, max_n_iters = 10L, verbose = TRUE, diffusion_fading = 10, diffusion_fading_const = 0.5, tol = 5e-3, fixed_initial_labels = FALSE) {
    .Call('_sccore_propagate_labels', PACKAGE = 'sccore', edge_verts, edge_weights, vert_labels, max_n_iters, verbose, diffusion_fading, diffusion_fading_const, tol, fixed_initial_labels)
}

#' Smooth gene expression, used primarily within conos::correctGenes. Used to smooth gene expression values in order to better represent the graph structure.
#' Use diffusion of expression on graph with the equation dv = exp(-a * (v + b))
#' 
#' @param edge_verts edge vertices of igraph graph object 
#' @param edge_weights edge weights of igraph graph object 
#' @param count_matrix gene count matrix
#' @param is_label_fixed boolean Whether label is fixed
#' @param max_n_iters integer Maximal number of iterations (default=10)
#' @param diffusion_fading numeric Constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=1.0)
#' @param diffusion_fading_const numeric Another constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=0.1)
#' @param tol numeric Absolute tolerance as a stopping criteria (default=1e-3)
#' @param verbose boolean Verbose mode (default=TRUE)
#' @param normalize boolean Whether to normalize values (default=FALSE)
#' @return matrix from input graph, with labels propagated
smooth_count_matrix <- function(edge_verts, edge_weights, count_matrix, is_label_fixed, max_n_iters = 10L, diffusion_fading = 1.0, diffusion_fading_const = 0.1, tol = 1e-3, verbose = TRUE, normalize = FALSE) {
    .Call('_sccore_smooth_count_matrix', PACKAGE = 'sccore', edge_verts, edge_weights, count_matrix, is_label_fixed, max_n_iters, diffusion_fading, diffusion_fading_const, tol, verbose, normalize)
}

#' List of adjacent vertices from igraph object
#' 
#' @param edge_verts edge vertices of igraph graph object 
#' @return list of adjacent vertices
#' @examples
#' \dontrun{
#' edges <- igraph::as_edgelist(conosGraph)
#' adjacentVertices(edges)
#' }
adjacentVertices <- function(edge_verts) {
    .Call('_sccore_adjacentVertices', PACKAGE = 'sccore', edge_verts)
}

#' List of adjacent vertex weights from igraph object
#' 
#' @param edge_verts edge vertices of igraph graph object 
#' @param edge_weights edge weights of igraph graph object  
#' @return list of adjacent vertices
#' @examples
#' \dontrun{
#' edges <- igraph::as_edgelist(conosGraph)
#' edge.weights <- igraph::edge.attributes(conosGraph)$weight
#' adjacent_vertex_weights(edges, edge.weights)
#' }
adjacent_vertex_weights <- function(edge_verts, edge_weights) {
    .Call('_sccore_adjacent_vertex_weights', PACKAGE = 'sccore', edge_verts, edge_weights)
}