R/priming_graph.R
priming_graph.RdConverts the given dataframe using first variable as competing and the second as miRNA. The function converts the given dataframe using first variable as competing and the second as miRNA. If user defines interaction factors as affinity or degradation, the factors are taken into account.
priming_graph( df, competing_count, miRNA_count, aff_factor = dummy, deg_factor = dummy )
| df | A data frame that includes the miRNA and competing targets. |
|---|---|
| competing_count | The counts (or expression) of competing elements of the dataset. |
| miRNA_count | The counts (or expression) of repressive element (miRNA) of the dataset. |
| aff_factor | The parameter/s of binding between miRNA and targets. |
| deg_factor | The parameter/s for degradation of bound miRNA:target complex. |
the graph object.
priming_graph provides grouping of competing targets and evaluation of targets within the groups taking into account miRNA:target, target:total target, interaction and degradation parameters. The target groups are determined according to miRNAs. If the factors that are important in target interactions are specified as arguments, the factors also are evaluated separately within each group. priming_graph also calculates the miRNA efficiency in steady-state conditions. It is assumed that quantity of competing targets and miRNAs are shown in the steady-state system after the miRNAs exhibit repressive efficiency. Note that the data must not include missing values such as NA or '-'.
#> 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 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)#> 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 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>