R/find_node_perturbation.R

In ceRNAnetsim: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA)

#' Calculates average expression changes of all (or specified)
#' nodes except trigger and finds the perturbed node count for
#' all (or specified) nodes in system.
#'
#'
#' @importFrom furrr future_map future_map_dfr
#' @importFrom future plan
#' @importFrom rlang set_names
#' @importFrom igraph gsize degree V 'V<-'
#' @importFrom purrr map_dbl
#' @importFrom tidyr unnest
#'
#' @return It gives a tibble form dataset that includes node names,
#' perturbation efficiency and perturbed count of nodes.
#'
#' @details find_node_perturbation calculates mean expression changes
#' of elements after the change in the network in terms of percentage.
#' It also calculates the number of nodes that have expression changes
#' after the change occur in the network. The outputs of the function
#' are the perturbation efficiency and perturbed count of nodes for
#' each nodes.
#'
#' @param input_graph The graph object that was processed with priming_graph function.
#' @param how The change of count (expression) of the given node in terms of fold change.
#' @param cycle The iteration of simulation.
#' @param limit The minimum fold change which can be taken into account for
#' perturbation calculation on all nodes in terms of percentage.
#' @param fast specifies percentage of affected target in target expression.
#' For example, if fast = 1, the nodes that are affected from miRNA repression
#' activity more than one percent of their expression is determined as subgraph.
#'
#' @examples
#'
#' data('minsamp')
#' data('midsamp')
#'
#'  minsamp%>%
#'   priming_graph(competing_count = Competing_expression, miRNA_count = miRNA_expression)%>%
#'   find_node_perturbation()%>%
#'   select(name, perturbation_efficiency, perturbed_count)
#'
#'
#'  minsamp%>%
#'   priming_graph(competing_count = Competing_expression, miRNA_count = miRNA_expression,
#'     aff_factor = c(energy,seed_type), deg_factor = region)%>%
#'   find_node_perturbation(how = 3, cycle = 4)%>%
#'   select(name, perturbation_efficiency, perturbed_count)
#'
#'  midsamp%>%
#'   priming_graph(competing_count = Gene_expression, miRNA_count = miRNA_expression)%>%
#'   find_node_perturbation(how = 2, cycle= 3, limit=1, fast = 5)%>%
#'   select(name, perturbation_efficiency, perturbed_count)
#'
#'
#' @export

find_node_perturbation <- function(input_graph, how = 2, cycle = 1, limit = 0,
                                   fast = 0) {

  empty_result <- list(dplyr::tibble(perturbation_efficiency = NA, perturbed_count = NA))

  if (fast == 0) {

    result <- input_graph %>% activate(nodes) %>% tidygraph::mutate(eff_count = future_map(V(input_graph)$name,
                                                                                           ~calc_perturbation(input_graph, .x, how, cycle, limit))) %>%
      tibble::as_tibble() %>% unnest(eff_count)
  }

  if (fast != 0) {

    result <- input_graph %>% tidygraph::activate(edges) %>% tidygraph::mutate(fast = ifelse(100 *
                                                                                               effect_current/comp_count_current > fast, TRUE, FALSE)) %>%
      tidygraph::morph(to_subgraph, fast) %>%
      { if( igraph::gsize(crystallise(.)$graph[[1]])==0) stop("Your argument fast removed all nodes. Please try lower fast argument")   else .  }%>%
      tidygraph::activate(nodes) %>%
      tidygraph::mutate(degree = tidygraph::centrality_degree(mode = "all")) %>%
      tidygraph::filter(degree > 0) %>% tidygraph::mutate(eff_count = tidygraph::map_bfs(tidygraph::node_is_center(),
                                                                                         .f = function(graph, node, ...) {
                                                                                           calc_perturbation(graph, V(graph)$name[node], how, cycle,
                                                                                                             limit)
                                                                                         })) %>% tidygraph::unmorph() %>% tidygraph::mutate(len = map_dbl(eff_count,
                                                                                                                                                          length), eff_count = ifelse(len == 0, empty_result, eff_count)) %>%
      tidygraph::as_tibble() %>% unnest() %>% dplyr::select(-degree,
                                                            -len)


  }

  return(result)
}