Build a graph object from a data frame
Usage
build_graph_from_df(
df,
node_annotation = NULL,
directed = F,
module.method = c("Fast_greedy", "Walktrap", "Edge_betweenness", "Spinglass"),
top_modules = 15,
seed = 1115
)Arguments
- df
Data frame. Edge list with columns
from,to, and optionallyweight. Ifweightis absent, an unweighted graph is constructed.- node_annotation
Data Frame The annotation file of nodes in network
- directed
Logical (default:
FALSE). Whether edges between nodes are directed.- module.method
Character Module analysis methods contains "Fast_greedy", "Walktrap", "Edge_betweenness", "Spinglass"
- top_modules
Integer. Number of top-ranked modules to select.
- seed
Integer (default = 1115). Random seed for reproducibility.
Value
An graph object representing the correlation network. Node/edge attributes include correlation statistics and (optionally) module labels.
Examples
data(ppi_example)
obj <- build_graph_from_df(
df = ppi_example$ppi,
node_annotation = ppi_example$annotation,
directed = FALSE,
module.method = "Fast_greedy",
top_modules = 5
)
obj
#> # A tbl_graph: 100 nodes and 50 edges
#> #
#> # An unrooted forest with 50 trees
#> #
#> # Node Data: 100 × 9 (active)
#> name group modularity modularity2 modularity3 Modularity Degree Segree
#> <chr> <chr> <fct> <fct> <chr> <fct> <dbl> <dbl>
#> 1 C13 C 1 1 1 1 1 1
#> 2 C28 C 1 1 1 1 1 1
#> 3 C2 C 10 10 10 10 1 1
#> 4 D9 D 10 10 10 10 1 1
#> 5 A3 A 11 11 11 11 1 1
#> 6 D38 D 11 11 11 11 1 1
#> 7 B12 B 12 12 12 12 1 1
#> 8 D19 D 12 12 12 12 1 1
#> 9 A1 A 13 13 13 13 1 1
#> 10 D40 D 13 13 13 13 1 1
#> # ℹ 90 more rows
#> # ℹ 1 more variable: Strength <dbl>
#> #
#> # Edge Data: 50 × 4
#> from to weight correlation
#> <int> <int> <dbl> <dbl>
#> 1 9 10 45.2 45.2
#> 2 11 100 50.6 50.6
#> 3 5 6 37.8 37.8
#> # ℹ 47 more rows