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Network Motif Analysis

Source: R/motifs.R
motif_census.Rd

Analyze recurring subgraph patterns (motifs) in networks and test their statistical significance against null models.

Usage

motif_census(
  x,
  size = 3,
  n_random = 100,
  method = c("configuration", "gnm"),
  directed = NULL,
  seed = NULL
)

# S3 method for class 'cograph_motifs'
print(x, ...)

Arguments

x

A matrix, igraph object, or cograph_network

size

Motif size: 3 (triads) or 4 (tetrads). Default 3.

n_random

Number of random networks for the null model. Must be a whole number of at least 2. Default 100.

method

Null model method: "configuration" (preserves degree) or "gnm" (preserves edge count). Default "configuration".

directed

Logical. Treat as directed? Default auto-detected.

seed

Random seed for reproducibility. Default NULL. When supplied, the caller's RNG state is saved and restored.

...

Passed to methods; currently unused.

Value

A cograph_motifs data frame with one row per motif class and columns:

motif

Motif class name (the 16 MAN codes for directed triads, the four undirected triad classes, or motif_<i> labels for size 4).

count

Observed number of that motif in the network.

null_mean, null_sd

Mean and standard deviation of the count across the n_random null graphs.

z_score

(count - null_mean) / null_sd; NA when the null is degenerate (null_sd = 0) and the observation differs from it.

p_value

Two-sided empirical (add-one corrected) permutation p-value, not a Gaussian approximation.

significant

Logical, p_value < 0.05.

The motif size ("size"), directed flag ("directed"), null-model method ("method"), and number of random networks ("n_random") are stored as attributes. Self-loops and multiple edges are removed before counting.

See also

motifs() for the unified API, extract_motifs() for detailed triad extraction, plot.cograph_motifs() for plotting

Other motifs: extract_motifs(), extract_triads(), get_edge_list(), motifs(), plot.cograph_motif_analysis(), plot.cograph_motifs(), subgraphs(), triad_census()

Examples

# Create a directed network
mat <- matrix(c(
  0, 1, 1, 0,
  0, 0, 1, 1,
  0, 0, 0, 1,
  1, 0, 0, 0
), 4, 4, byrow = TRUE)

# Analyze triadic motifs
m <- motif_census(mat)
print(m)
#> Network Motif Analysis
#> Size: 3-node motifs (directed) | Null: configuration (n=100)
#> 
#>  motif count null_mean  null_sd    z_score p_value significant
#>    003     0      0.00 0.000000  0.0000000       1       FALSE
#>    012     0      0.00 0.000000  0.0000000       1       FALSE
#>    102     0      0.00 0.000000  0.0000000       1       FALSE
#>   021D     0      0.00 0.000000  0.0000000       1       FALSE
#>   021U     0      0.00 0.000000  0.0000000       1       FALSE
#>   021C     0      0.00 0.000000  0.0000000       1       FALSE
#>   111D     0      0.98 1.004837 -0.9752828       1       FALSE
#>   111U     0      0.98 1.004837 -0.9752828       1       FALSE
#>   030T     2      1.02 1.004837  0.9752828       1       FALSE
#>   030C     2      1.02 1.004837  0.9752828       1       FALSE
#>    201     0      0.00 0.000000  0.0000000       1       FALSE
#>   120D     0      0.00 0.000000  0.0000000       1       FALSE
#>   120U     0      0.00 0.000000  0.0000000       1       FALSE
#>   120C     0      0.00 0.000000  0.0000000       1       FALSE
#>    210     0      0.00 0.000000  0.0000000       1       FALSE
#>    300     0      0.00 0.000000  0.0000000       1       FALSE
#> 
#> Over-represented: 0 | Under-represented: 0
plot(m)
#> No motifs to plot. Try show_nonsig = TRUE