make_network: Make microbiome network (igraph)
In phyloseq: Handling and analysis of high-throughput microbiome census data

Description Usage Arguments Value See Also Examples

A specialized function for creating a network representation of microbiomes, sample-wise or taxa-wise, based on a user-defined ecological distance and (potentially arbitrary) threshold. The graph is ultimately represented using the igraph-package.

1 2	make_network(physeq, type="samples", distance="jaccard", max.dist = 0.4, keep.isolates=FALSE, ...)

`physeq`	(Required). Default `NULL`. A `phyloseq-class` object, or `otu_table-class` object, on which `g` is based. `phyloseq-class` recommended.
`type`	(Optional). Default `"samples"`. Whether the network should be samples or taxa/OTUs. Supported arguments are `"samples"`, `"taxa"`, where `"taxa"` indicates using the OTUs/taxaindices, whether they actually represent species or some other taxonomic rank. NOTE: not all distance methods are supported if `"taxa"` selected for type. For example, the UniFrac distance and DPCoA cannot be calculated for taxa-wise distances, because they use a taxa-wise tree as part of their calculation between samples, and there is no transpose-equivalent for this tree.
`distance`	(Optional). Default `"jaccard"`. Any supported argument to the `method` parameter of the `distance` function is supported here. Some distance methods, like `"unifrac"`, may take a non-trivial amount of time to calculate, in which case you probably want to calculate the distance matrix separately, save, and then provide it as the argument to `distance` instead. See below for alternatives). Alternatively, if you have already calculated the sample-wise distance object, the resulting `dist`-class object can be provided as `distance` instead (see examples). A third alternative is to provide a function that takes a sample-by-taxa matrix (typical vegan orientation) and returns a sample-wise distance matrix.
`max.dist`	(Optional). Default `0.4`. The maximum ecological distance (as defined by `distance`) allowed between two samples to still consider them “connected” by an edge in the graphical model.
`keep.isolates`	(Optional). Default `FALSE`. Logical. Whether to keep isolates (un-connected samples, not microbial isolates) in the graphical model that is returned. Default results in isolates being removed from the object.
`...`	(Optional). Additional parameters passed on to `distance`.

A igraph-class object.

plot_network

# # Example plots with Enterotype Dataset
data(enterotype)
ig <- make_network(enterotype, max.dist=0.3)
plot_network(ig, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.3, label=NULL)
# 
ig1 <- make_network(enterotype, max.dist=0.2)
plot_network(ig1, enterotype, color="SeqTech", shape="Enterotype", line_weight=0.3, label=NULL)
# 
# # Three methods of choosing/providing distance/distance-method
# Provide method name available to distance() function
ig <- make_network(enterotype, max.dist=0.3, distance="jaccard")
# Provide distance object, already computed
jaccdist <- distance(enterotype, "jaccard")
ih <- make_network(enterotype, max.dist=0.3, distance=jaccdist)
# Provide "custom" function.
ii <- make_network(enterotype, max.dist=0.3, distance=function(x){vegan::vegdist(x, "jaccard")})
# The have equal results:		
all.equal(ig, ih)
all.equal(ig, ii)
# 
# Try out making a trivial "network" of the 3-sample esophagus data,
# with weighted-UniFrac as distance
data(esophagus)
ij <- make_network(esophagus, "samples", "unifrac", weighted=TRUE)