LorMe (version 1.1.0)

network_analysis: Conduct Network analysis based on tax summary object

Description

Conduct Network analysis based on tax summary object

Usage

network_analysis(
  taxobj,
  taxlevel,
  reads = FALSE,
  n,
  threshold,
  rel_threshold = 0,
  method = "spearman",
  display = TRUE
)

Value

One list contains nodes information table, adjacency column table, adjacency matrix and 'igraph' object.

Arguments

taxobj

tax summary objects computed by tax_summary.

taxlevel

taxonomy levels used for analysis. Must be one of c("Domain","Phylum","Class","Order","Family","Genus","Species","Base")

reads

Logical,default:FALSE. Taxonomy abundance type used in analysis.FALSE for relative abundance, TRUE for absolute abundance.

n

Numeric. Number of sample size indicating kept asv/otu/gene/taxa appearing. Recommended to set more than half of total sample size.

threshold

Numeric.Threshold of absolute correlation value (r value for pearson method and rho value for spearman method).

rel_threshold

Numeric.Threshold of relative abundance included in the network analysis.Default:0

method

Character, default: "spearman". A character indicating which correlation coefficient method to be computed. One of "pearson" or "spearman"

display

Logical, default:TRUE. If display a preview plot of network based on igraph. FALSE for the first attempt is recommended in case of too many vertices and edges.

Details

1. We had optimized the correlation algorithm to achieve a faster running speed. It takes less than 2 minute to calculate dataframe correlation and p value which more than 400 samples and 10000 OTUs for computer with dual Core i5 processor. However, too many vertices(>2000) or links(>10000) may slow the statistical process and visualization,so we recommend that in your first attempt,set display paramter as F to have a preview. Then you can adjust your n/threshold/method paramter to generate a suitable visualization network

2. We display a preview plot so as to adjusting your network. Generally a global figure (like we show in examples) with less than 1000 vertices and 5000 edges/links is recommended. Further more,we recommend you to output the statistics and adjacency table and use software like cytoscape or gephi for better visualization.

Examples

{
  ### Data preparation ###
  data("Two_group")
  set.seed(999)
  ## Analysis
  network_results <- network_analysis(
    taxobj = Two_group,
    taxlevel = "Genus",
    n = 10,
    threshold = 0.8
  )

  # Nodes information table
  network_nodes <- network_results$Nodes_info
  head(network_nodes)

  # Adjacency table
  network_adjacency <- network_results$Adjacency_column_table
  head(network_adjacency)

  # Complete adjacency matrix
  network_matrix <- network_results$Adjacency_matrix
  print(network_matrix[1:10, 1:10])

  # igraph object
  igraph_object <- network_results$Igraph_object
  network_stat(igraph_object)  # In case you want to see statistics again
  # or do other analysis based on igraph.
}