this function converts the count value of the given node.
update_how(input_graph, node_name, how, knockdown = TRUE)
| input_graph | The graph object that processed in previous step/s. |
|---|---|
| node_name | The name of the node whose count is to be changed. |
| how | The change in terms of fold change. |
| knockdown | specifies gene knockdown with default TRUE |
the graph object.
update_how function calculates the current value of given mirna or gene node on the graph object. User must specify current value as fold change.
data('minsamp') priming_graph(minsamp, Competing_expression, miRNA_expression)%>% update_how('Gene1',3)#> 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 30000 Up #> 2 Gene2 Competing 2 10000 10000 10000 Competing #> 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>priming_graph(minsamp, Competing_expression, miRNA_expression, aff_factor = c(seed_type,energy), deg_factor = region)%>% update_how('Gene1', 3)#> 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 30000 Up #> 2 Gene2 Competing 2 10000 10000 10000 Competing #> 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 22 #> 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 16 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>priming_graph(minsamp, Competing_expression, miRNA_expression, aff_factor = c(seed_type,energy), deg_factor = region)%>% update_how('Gene1', how=0, knockdown= TRUE)#> 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 0 0 Competing #> 2 Gene2 Competing 2 10000 10000 10000 Competing #> 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 22 #> 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 16 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>