This function carries variables from edge to node and should be used after `update_how` or `update_variables` functions
update_nodes(input_graph, once = FALSE, limit = 0)
| input_graph | Processed graph object in previous step. |
|---|---|
| once | The argument is about when the carrying process runs (internal use only) |
| limit | absolute minimum amount of change required to be considered as up/down regulated element |
the graph object.
If the carrying process performs after priming_graph function, the argument must be TRUE. The function helps to visualisation of processed graph object, especially that includes too many nodes.This step makes it easily to follow the processes.
data('minsamp') minsamp %>% priming_graph(Competing_expression, miRNA_expression) %>% update_how('Gene2',2)#> 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 10000 Competing #> 2 Gene2 Competing 2 10000 10000 20000 Up #> 3 Gene3 Competing 3 5000 5000 5000 Competing #> 4 Gene4 Competing 4 10000 10000 10000 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 19 #> 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 12 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>