network_analysis {LorMe} | R Documentation |
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
)
Arguments
taxobj |
tax summary objects computed by |
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
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 asF
to have a preview. Then you can adjust your n/threshold/method paramter to generate a suitable visualization networkWe 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.
Value
One list contains nodes information table, adjacency column table, adjacency matrix and 'igraph' object.
Note
Replicates should be at least 5,more than 8 is recommend.
In case of too many edges/links or not a global network plot, you can stop the process immediately to provent wasting too much time.
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.
}