LorMe (version 1.2.1)

network_withdiff: Network Analysis with Differential Species

Description

Meta network analysis integrating differential taxon into a network analysis

Usage

network_withdiff(network_obj, diff_frame, aes_col = NULL, tag_threshold = 5)

Value

A list containing the configured igraph object, vertices coordinates, parameters, and tag statistics.

Arguments

network_obj

Network analysis results generated from network_analysis

diff_frame

Differential analysis results generated from indicator_analysis or Deseq_analysis.

aes_col

A named vector of colors to be used to highlight differential taxon vertices

tag_threshold

Numeric. A threshold for the minimum number of differential taxon to display.

Examples

# \donttest{
{
  # Data preparation
  data("Two_group")
  set.seed(999)
  # Analysis
  network_results <- network_analysis(
    taxobj = Two_group,
    taxlevel = "Genus",
    n = 10,
    threshold = 0.8
  )
  indicator_results <- indicator_analysis(
    taxobj = Two_group,
    taxlevel = "Genus"
  )
  deseq_results <- Deseq_analysis(
    taxobj = Two_group,
    taxlevel = "Genus",
    cutoff = 1,
    control_name = "Control"
  )

  # Visualize
  network_diff_obj <- network_withdiff(
    network_obj = network_results,
    diff_frame = indicator_results
  )
  # Check contained tags for each model
  print(network_diff_obj$tag_statistics$sum_of_tags)
  # Check contained different tags for each model
  print(network_diff_obj$tag_statistics$detailed_tags)

  # Re-visualize
  network_visual_re(
    network_visual_obj = network_diff_obj,
    module_paint = TRUE,
    module_num = c(1, 4)
  )  # Show module with most Treatment indicators

  my_module_palette <- color_scheme(
    c("#83BA9E", "#F49128"),
    5
  )
  network_visual_re(
    network_visual_obj = network_diff_obj,
    module_paint = TRUE,
    module_num = c(1, 4, 6, 3, 8),
    module_palette = my_module_palette
  )  # Show module with most Treatment indicators

  # Available also for DESeq analysis results
  network_diff_obj <- network_withdiff(
    network_obj = network_results,
    diff_frame = deseq_results
  )

  # Parameter adjustment
  network_diff_obj <- network_withdiff(
    network_obj = network_results,
    diff_frame = indicator_results,
    tag_threshold = 20
  )  # The 'tag_threshold' set too high

  network_diff_obj <- network_withdiff(
    network_obj = network_results,
    diff_frame = indicator_results,
    tag_threshold = 10
  )  # Set lower
  # Check contained tags for each model
  print(network_diff_obj$tag_statistics$sum_of_tags)
  # Check contained different tags for each model
  print(network_diff_obj$tag_statistics$detailed_tags)

  network_diff_obj <- network_withdiff(
    network_obj = network_results,
    diff_frame = indicator_results,
    tag_threshold = 1
  )  # Set too low

  # Another example
  data("Three_group")
  network_results <- network_analysis(
    taxobj = Three_group,
    taxlevel = "Genus",
    n = 15,
    threshold = 0.9
  )
  indicator_results <- indicator_analysis(
    taxobj = Three_group,
    taxlevel = "Genus"
  )

  tag_color <- c(
    "CF" = "#F8766D",
    "CF_OF" = "#FFFF00",
    "OF" = "#00BA38",
    "OF_BF" = "#800080",
    "BF" = "#619CFF",
    "CF_BF" = "#00FFFF"
  )
  network_diff_obj <- network_withdiff(
    network_obj = network_results,
    diff_frame = indicator_results,
    aes_col = tag_color,
    tag_threshold = 10
  )

  # Re-visualize
  print(network_diff_obj$tag_statistics$detailed_tags)
  network_visual_re(
    network_visual_obj = network_diff_obj,
    module_paint = TRUE,
    module_num = c(8, 10, 11)
  )  # Show module with most BF indicators
  network_visual_re(
    network_visual_obj = network_diff_obj,
    module_paint = TRUE,
    module_num = c(1, 6, 8)
  )  # Show module with most BF and OF_BF indicators
}
# }