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Create network topology

Source: R/get_network_topology.R
get_network_topology.Rd

Generates a data frame containing network topology information from either a pre-built graph object or directly from an adjacency matrix.

Usage

get_network_topology(
  graph_obj = NULL,
  graph_obj_list = NULL,
  mat = NULL,
  graph_mat_list = NULL,
  transfrom.method = c("none", "scale", "center", "log2", "log10", "ln", "rrarefy",
    "rrarefy_relative"),
  r.threshold = 0.7,
  p.threshold = 0.05,
  method = c("WGCNA", "SpiecEasi", "SPARCC", "cor"),
  cor.method = c("pearson", "kendall", "spearman"),
  proc = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"),
  SpiecEasi.method = c("mb", "glasso"),
  sparcc_R = 20,
  bootstrap = 100
)

Arguments

graph_obj

An graph object from build_graph_from_mat or build_graph_from_df. The network object to be visualized.

graph_obj_list

A list of graph objects. Optional alternative to graph_obj. Each element is analyzed separately.

mat

Numeric Matrix (default = NULL) The matrix to build graph_obj

graph_mat_list

A list of matrices corresponding to graph_obj_list. Optional. Each element is paired with the graph object at the same position and used for topology analysis separately.

transfrom.method

Character. Data transformation methods applied before correlation analysis. Options include: "none" (raw data), "scale" (z-score standardization), "center" (mean centering only), "log2" (log2 transfrom), "log10" (log10 transfrom), "ln" (natural transfrom ), "rrarefy" (random rarefaction using vegan::rrarefy), "rrarefy_relative" (rarefy then convert to relative abundance).

r.threshold

Numeric. Correlation coefficient threshold; edges are kept only if |r| >= r.threshold.

p.threshold

Numeric. Significance threshold for correlations; edges are kept only if p < p.threshold.

method

Character. Relationship analysis methods. Options include: "WGCNA", "SpiecEasi", "SPARCC" and "cor".

cor.method

Character. Correlation analysis method. Options include "pearson", "kendall", and "spearman".

proc

Character. Correlation p-value adjustment methods. Options include: "holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", and "none".

SpiecEasi.method

Character. Inverse-covariance estimation method passed to SpiecEasi when method = "SpiecEasi". One of "mb" (Meinshausen-Buehlmann, default) or "glasso".

sparcc_R

Integer. Number of bootstrap/permutation replicates for SparCC p-values (when method = "SPARCC"). Default 20.

bootstrap

Numeric (default = 100). Number of bootstrap iterations for stability analysis

Value

A list containing topology output and robustness output for a single network. When graph_obj_list is provided, returns a named list of such results.

Examples

# \donttest{
data(ppi_example)
obj <- build_graph_from_df(
  df              = ppi_example$ppi,
  node_annotation = ppi_example$annotation
)
topo <- get_network_topology(graph_obj = obj, bootstrap = 10)
head(topo$topology)
#> # A tibble: 6 × 3
#>   Topology Target_network Random_nerwork
#>   <chr>             <dbl>          <dbl>
#> 1 Node           100            100     
#> 2 Edge            50             50     
#> 3 Degree           1              1     
#> 4 Distance        59.8            3.43  
#> 5 Diameter       109.             9.2   
#> 6 Density          0.0101         0.0101
# }