Provides visualisation of the graph in addition to simulate function.

simulate_vis provides visualisation of the graph in addition to simulate function.

simulate_vis(
  input_graph,
  cycle = 1,
  threshold = 0,
  save = FALSE,
  Competing_color = "green",
  mirna_color = "orange",
  Upregulation = "red",
  Downregulation = "blue",
  title = "GRAPH",
  layout = "kk"
)

Arguments

input_graph	The graph object that processed in previous steps.
cycle	Optimal iteration number for gaining steady-state.
threshold	absolute minimum amount of change required to be considered as up/down regulated element
save	provides to save graph output
Competing_color	The color of competing elements on the graph with "green" default.
mirna_color	The color of miRNAs on the graph with "orange" default.
Upregulation	The color of Upregulated elements on the graph with "red" default.
Downregulation	The color of Downregulated elements on the graph with "blue" default.
title	Title of the given graph.
layout	The layout that will be used for visualisation of the graph.

Value

It gives a graph and the images of states in each iteration until the end of the simulation.

Details

simulate_vis gives the last graph object and each iterations' image.

Examples

# When does the system gain steady-state conditions again?

## new_counts, the dataset that includes the current counts of nodes.

data("minsamp")
data("new_counts")

priming_graph(minsamp, Competing_expression, miRNA_expression)%>%
  update_variables(new_counts)%>%
  simulate_vis()
#> Warning: First column is processed as competing and the second as miRNA.
#> # A tbl_graph: 8 nodes and 7 edges
#> #
#> # A rooted tree
#> #
#> # Node Data: 8 x 7 (active)
#>   name  type      node_id initial_count count_pre count_current changes_variable
#>   <chr> <chr>       <int>         <dbl>     <dbl>         <dbl> <chr>           
#> 1 Gene1 Competing       1         10000     10000        10063. Up              
#> 2 Gene2 Competing       2         10000     20000        19841. Down            
#> 3 Gene3 Competing       3          5000      5000         5032. Up              
#> 4 Gene4 Competing       4         10000     10000        10063. Up              
#> 5 Gene5 Competing       5          5000      5000         5000  Competing       
#> 6 Gene6 Competing       6         10000     10000        10000  Competing       
#> # … with 2 more rows
#> #
#> # Edge Data: 7 x 20
#>    from    to Competing_name miRNA_name Competing_expre… miRNA_expression dummy
#>   <int> <int> <chr>          <chr>                 <dbl>            <dbl> <dbl>
#> 1     1     7 Gene1          Mir1                  10000             1000     1
#> 2     2     7 Gene2          Mir1                  10000             1000     1
#> 3     3     7 Gene3          Mir1                   5000             1000     1
#> # … with 4 more rows, and 13 more variables: afff_factor <dbl>,
#> #   degg_factor <dbl>, comp_count_list <list>, comp_count_pre <dbl>,
#> #   comp_count_current <dbl>, mirna_count_list <list>, mirna_count_pre <dbl>,
#> #   mirna_count_current <dbl>, mirna_count_per_dep <dbl>, effect_current <dbl>,
#> #   effect_pre <dbl>, effect_list <list>, mirna_count_per_comp <dbl>

priming_graph(minsamp, Competing_expression, miRNA_expression, 
    aff_factor = c(seed_type,energy), deg_factor = c(region))%>%
  update_variables(new_counts)%>%
  simulate_vis(cycle = 12)
#> Warning: First column is processed as competing and the second as miRNA.
#> # A tbl_graph: 8 nodes and 7 edges
#> #
#> # A rooted tree
#> #
#> # Node Data: 8 x 7 (active)
#>   name  type      node_id initial_count count_pre count_current changes_variable
#>   <chr> <chr>       <int>         <dbl>     <dbl>         <dbl> <chr>           
#> 1 Gene1 Competing       1         10000    10064.        10064. Competing       
#> 2 Gene2 Competing       2         10000    19997.        19997. Competing       
#> 3 Gene3 Competing       3          5000     5023.         5023. Competing       
#> 4 Gene4 Competing       4         10000    10029.        10029. Competing       
#> 5 Gene5 Competing       5          5000     5000.         5000. Competing       
#> 6 Gene6 Competing       6         10000    10000.        10000. Competing       
#> # … with 2 more rows
#> #
#> # Edge Data: 7 x 23
#>    from    to Competing_name miRNA_name Competing_expre… miRNA_expression
#>   <int> <int> <chr>          <chr>                 <dbl>            <dbl>
#> 1     1     7 Gene1          Mir1                  10000             1000
#> 2     2     7 Gene2          Mir1                  10000             1000
#> 3     3     7 Gene3          Mir1                   5000             1000
#> # … with 4 more rows, and 17 more variables: seed_type <dbl>, energy <dbl>,
#> #   region <dbl>, dummy <dbl>, afff_factor <dbl>, degg_factor <dbl>,
#> #   comp_count_list <list>, comp_count_pre <dbl>, comp_count_current <dbl>,
#> #   mirna_count_list <list>, mirna_count_pre <dbl>, mirna_count_current <dbl>,
#> #   mirna_count_per_dep <dbl>, effect_current <dbl>, effect_pre <dbl>,
#> #   effect_list <list>, mirna_count_per_comp <dbl>