```
#' Utilizes the change in expression value/s as triggering.
#'
#' simulate function uses the change in expression value/s as triggering.
#' @import dplyr tidygraph
#' @importFrom purrr pmap
#' @return The graph.
#'
#' @details The steady-state conditions of the system are disturbed
#' after the change in the graph (with update_how or update_variables).
#' In this case, the system tend to be steady state again. The arrangement
#' of competetive profiles of the targets continue until all nodes are
#' updated and steady-state nearly. Note that, If `how` argument is
#' specified as `0`, *simulate()* and *update_how()* functions process
#' the variables to knockdown of specified gene with default `knockdown = TRUE`
#' and knocked down competing RNA is kept at zero. However, if `knockdown= FALSE`
#' argument is applied, competing RNA which has initial expression level of zero
#' is allowed to increase or fluctuate during calculations.
#'
#' @param input_graph The graph object that processed in previous steps.
#' @param cycle Optimal iteration number for gaining steady-state.
#' @param threshold absolute minimum amount of change required to be
#' considered as up/down regulated element
#' @param knockdown specifies gene knockdown with default TRUE
#'
#' @examples
#'
#' data('minsamp')
#' data('new_counts')
#'
#' ## new_counts, the dataset that includes the current counts of nodes.
#'
#' priming_graph(minsamp, Competing_expression, miRNA_expression)%>%
#' update_variables(new_counts)%>%
#' simulate()
#'
#' priming_graph(minsamp, Competing_expression, miRNA_expression,
#' aff_factor = c(seed_type,energy), deg_factor = region)%>%
#' update_variables(new_counts)%>%
#' simulate(cycle = 3)
#'
#' @export
simulate <- function(input_graph, cycle = 1, threshold = 0, knockdown=TRUE) {
if (cycle < 1)
stop("number of cycles should be more than 1", call. = FALSE)
if (knockdown) {
for (i in seq_len(cycle)) {
input_graph <- input_graph %>%
tidygraph::activate(edges) %>%
tidygraph::group_by(to) %>%
tidygraph::mutate(mirna_count_per_comp = mirna_count_current *comp_count_current * afff_factor/sum(comp_count_current *afff_factor),
mirna_count_per_comp = ifelse(is.na(mirna_count_per_comp),0, mirna_count_per_comp)) %>%
tidygraph::ungroup() %>%
tidygraph::mutate(effect_pre = effect_current,
effect_current = mirna_count_per_comp *degg_factor) %>%
tidygraph::group_by(from) %>%
# NEW
tidygraph::mutate(avg_count_current = round((comp_count_pre+comp_count_current)/2, 0)) %>%
tidygraph::mutate(comp_count_pre = ifelse(comp_count_current < 0, 1, comp_count_current),
comp_count_current = ifelse(comp_count_pre ==0, 0, comp_count_pre - (sum(effect_current) - sum(effect_pre))),
comp_count_current = ifelse(comp_count_current < 0, 1, comp_count_current)) %>%
# NEW
tidygraph::mutate(avg_count_pre = avg_count_current,
avg_count_current = round((comp_count_pre+comp_count_current)/2, 0)) %>%
tidygraph::ungroup() %>%
# NEW: this fixes the ossilation issue:
mutate(comp_count_current = ifelse(abs(avg_count_pre - avg_count_current) < 0.001*avg_count_current, avg_count_current, comp_count_current)) %>%
#mutate(comp_count_current = ifelse(avg_count_pre == avg_count_current, avg_count_current, comp_count_current)) %>%
tidygraph::mutate(comp_count_list = pmap(list(comp_count_list,comp_count_current), c),
effect_list = pmap(list(effect_list, effect_current), c),
mirna_count_list = pmap(list(mirna_count_list, mirna_count_current), c)) %>%
tidygraph::activate(nodes) %>%
update_nodes(limit = threshold)
}
return(input_graph %>% activate(edges) %>% select(-avg_count_current, -avg_count_pre) %>% activate(nodes))
}
for (i in seq_len(cycle)) {
input_graph <- input_graph %>%
tidygraph::activate(edges) %>%
tidygraph::group_by(to) %>%
tidygraph::mutate(mirna_count_per_comp = mirna_count_current * comp_count_current * afff_factor/sum(comp_count_current * afff_factor),
mirna_count_per_comp = ifelse(is.na(mirna_count_per_comp), 0, mirna_count_per_comp)) %>%
tidygraph::ungroup() %>%
tidygraph::mutate(effect_pre = effect_current, effect_current = mirna_count_per_comp *degg_factor) %>%
tidygraph::group_by(from) %>%
# NEW
tidygraph::mutate(avg_count_current = round((comp_count_pre+comp_count_current)/2, 0)) %>%
tidygraph::mutate(comp_count_pre = ifelse(comp_count_current <0, 1, comp_count_current),
comp_count_current = comp_count_pre -(sum(effect_current) - sum(effect_pre)),
comp_count_current = ifelse(comp_count_current <0, 1, comp_count_current)) %>%
# NEW
tidygraph::mutate(avg_count_pre = avg_count_current,
avg_count_current = round((comp_count_pre+comp_count_current)/2, 0)) %>%
tidygraph::ungroup() %>%
# NEW: this fixes the ossilation issue:
mutate(comp_count_current = ifelse(abs(avg_count_pre - avg_count_current) < 0.001*avg_count_current, avg_count_current, comp_count_current)) %>%
#mutate(comp_count_current = ifelse(avg_count_pre == avg_count_current, avg_count_current, comp_count_current)) %>%
tidygraph::mutate(comp_count_list = pmap(list(comp_count_list,comp_count_current), c),
effect_list = pmap(list(effect_list, effect_current), c),
mirna_count_list = pmap(list(mirna_count_list, mirna_count_current), c)) %>%
tidygraph::activate(nodes) %>%
update_nodes(limit = threshold)
}
return(input_graph %>% activate(edges) %>% select(-avg_count_current, -avg_count_pre) %>% activate(nodes))
}
```

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.