Visualize network with custom layouts in different samples

Usage

ggNetView_multi(
  mat,
  group_info,
  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"),
  module.method = c("Fast_greedy", "Walktrap", "Edge_betweenness", "Spinglass"),
  SpiecEasi.method = c("mb", "glasso"),
  sparcc_R = 20,
  node_annotation = NULL,
  top_modules = 15,
  layout = NULL,
  ...,
  layout_nrow = NULL,
  layout_ncol = NULL,
  seed = 1115,
  nrow = NULL,
  ncol = NULL
)

Arguments

mat: Numeric matrix. A numeric matrix with samples in rows and variables in columns.
group_info: DataFrame The group information contains: Sample and Group
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: p.threshold 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".
module.method: Character. Network community detection (module identification) method. Options include "Fast_greedy", "Walktrap", "Edge_betweenness", and "Spinglass".
SpiecEasi.method: Character. Method used in SpiecEasi network inference; options include "mb" and "glasso".
sparcc_R: Integer. Number of bootstrap/permutation replicates for SparCC p-values (when method = "SPARCC"). Default 20.
node_annotation: Data frame. Optional node annotation table, containing metadata such as taxonomy or functional categories.
top_modules: Integer. Number of top-ranked modules to retain for downstream visualization or analysis.
layout: Character string naming the layout passed to ggNetView() (e.g. "gephi", "fr", "circle", "square").
...: Additional arguments passed to ggNetView() (node_*, edge_*, module_label_*, module_outline_*, network_outline_*, layout geometry, ...). Deprecated pre-0.2.0 names (e.g. fill.by, pointsize) are still accepted with a lifecycle warning.
layout_nrow: Integer (default = NULL). Number of layout rows passed to ggNetView when using consensus-module grid layouts.
layout_ncol: Integer (default = NULL). Number of layout columns passed to ggNetView when using consensus-module grid layouts.
seed: Integer (default = 1115). Random seed for reproducibility.
nrow: Integer (default = NULL). Number of rows in the combined patchwork plot.
ncol: Integer (default = NULL). Number of columns in the combined patchwork plot.

Value

A ggplot object representing the network visualization.

Examples

if (FALSE) { # \dontrun{
# `mat` is a numeric matrix (features x samples) and
# `group_info` is a data frame with columns Sample and Group.
p <- ggNetView_multi(
  mat        = mat,
  group_info = group_info,
  method     = "cor",
  layout     = "fr"
)
} # }