Examples used in this vignette will use the GlobalPatterns dataset from phyloseq.

library(phyloseq)
data(GlobalPatterns)


co_occurrence_network

Create a ggplot object of the co-occurrence network.


Usage

co_occurrence_network(phyloseq_obj, treatment = NULL, subset = NULL, co_occurrence_table = NULL, classification = NULL, node_colors = 'default', cluster = FALSE, cluster_colors = 'default', buffer = 0.5)


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
treatment Column name as a string, or vector of, in the sample_data.
subset A factor within the treatment. This will remove any samples that to

not contain this factor. This can be a vector of multiple factors to subset on. co_occurrence_table | Table of the co-occurrence of taxa/genes in the phyloseq_obj, computed using co_occurrence(). If no table is given, it will be computed with the phyloseq_obj, using the given treatment and p = 0.05 with a rho cut-off of 0.8. classification | Column name as a string or numeric in the tax_table for the factor to use for node colors. node_colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors. cluster | if TRUE, will use igraph’s cluster_fast_greedy method. Alternatively, you may pass a vector of cluster assignments with order corresponding to the order of the taxa_names in the phyloseq_obj. cluster_colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors. buffer | Amount of space beyond the points to extend the cluster (aesthetic argument).

Examples This sample set is too small to get any meaningful co-occurrence.

filtered_obj <- conglomerate_taxa(GlobalPatterns, "Phylum")
co_occurrence_network(filtered_obj, treatment = "SampleType", 
  classification = 'Phylum')



network_layout_ps

Create an layout_igraph object of the co-occurrence from a phyloseq object. This can be input into the co_occurrence_network function, or used for other network creating scripts. The purpose is to be able to create reproducible and comparable graphics.


Usage

network_layout_ps(phyloseq_obj, treatment = NULL, subset = NULL, co_occurrence_table = NULL, algorithm = 'fr')


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
treatment Column name as a string, or vector of, in the sample_data.
subset A factor within the treatment. This will remove any samples that to

not contain this factor. This can be a vector of multiple factors to subset on. co_occurrence_table | Table of the co-occurrence of taxa/genes in the phyloseq_obj, computed using co_occurrence(). If no table is given, it will be computed with the phyloseq_obj, using the given treatment and p = 0.05 with a rho cut-off of 0.8. algorithm | Supported igraph::layout_ algorithm.

Examples

filtered_obj <- conglomerate_taxa(GlobalPatterns, "Phylum")
network_layout_ps(filtered_obj, treatment = "SampleType", algorithm = 'kk')



network_ps

Create an layout_igraph object of the co-occurrence from a phyloseq object. This can be input into the co_occurrence_network function, or used for other network creating scripts. The purpose is to be able to create reproducible and comparable graphics.


Usage rnetwork_ps(phyloseq_obj, treatment = NULL, subset = NULL, co_occurrence_table = NULL, rho = 0.6)


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
treatment Column name as a string, or vector of, in the sample_data.
subset A factor within the treatment. This will remove any samples that to

not contain this factor. This can be a vector of multiple factors to subset on. co_occurrence_table | Table of the co-occurrence of taxa/genes in the phyloseq_obj, computed using co_occurrence(). If no table is given, it will be computed with the phyloseq_obj, using the given treatment and p = 0.05 with a rho cut-off of 0.8. rho | Cutoffs to use to subset the correlation_table by correlation values.

Examples

filtered_obj <- conglomerate_taxa(GlobalPatterns, "Phylum")
network_ps(filtered_obj, treatment = "SampleType", rho = 0.6)
## IGRAPH 87d181c UN-- 50 165 -- 
## + attr: name (v/c), Kingdom (v/c), Phylum (v/c), Mean Relative
## | Abundance (v/c), Treatment (e/c), rho (e/n), p (e/n), Weight (e/n),
## | Edge (e/c)
## + edges from 87d181c (vertex names):
##  [1] SBR1093       --ZB2           SBR1093       --Synergistetes
##  [3] GOUTA4        --OP8           GOUTA4        --GN04         
##  [5] GOUTA4        --SR1           OP8           --GN04         
##  [7] OP8           --SR1           Hyd24-12      --OP11         
##  [9] GN04          --SR1           ZB2           --Synergistetes
## [11] Euryarchaeota --Chlorobi      Euryarchaeota --Chloroflexi  
## + ... omitted several edges



variable_correlation_network

Create a network ggplot object of the correlation of taxa and sample variables from a phyloseq object. This may also be refered to as a bipartite graph. Function from the phylosmith-package.


Usage

variable_correlation_network(phyloseq_obj, variables, classification = NULL,
 treatment = NULL, subset = NULL, correlation_table = NULL, method = 'spearman',
 rho_threshold = c(-0.01, 0.01), p_threshold = 0.05, colors = 'default',
 negative_positive_colors = c('pink1', 'gray22'))


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
co_occurrence_table Table of the co-occurrence of taxa/genes in the phyloseq_obj, computed using co_occurrence(). If no table is given, it will be computed with the phyloseq_obj, using the given treatment and p = 0.05 with a rho cut-off of 0.8.
variables Numerical factors within the in the sample_data to correlate with the abundance data.
classification Column name as a string or numeric in the tax_table for the factor to conglomerate by.
treatment Column name as a string, or vector of, in the sample_data.
subset A factor within the treatment. This will remove any samples that to

not contain this factor. This can be a vector of multiple factors to subset on. correlation_table | Table of the correlation of taxa/variables in the phyloseq_ob}, computed using variable_correlation. If no table is given, it will be computed with the phyloseq_obj, using the given treatment and p = 0.05. method | Which correlation method to calculate, “pearson”, “spearman”. rho_threshold | Cutoffs to use to subset the correlation_table by correlation values. p_threshold | Cutoffs to use to subset the correlation_table by singnificance values. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors. negative_positive_colors | colors to use for the edges to represent negative and positive correlations. Defaul (‘pink1’, ‘gray22’)

Examples

filtered_obj <- phyloseq::subset_samples(enterotype, 
  !is.na(enterotype@sam_data$ClinicalStatus))
filtered_obj <- taxa_filter(filtered_obj, frequency = 0.65)
variable_correlation_network(filtered_obj, variables = 'Age',
 treatment = "ClinicalStatus", subset = c("healthy", "elderly", "obese"), 
 classification = 'Genus', method = 'spearman')




Schuyler Smith
Ph.D. Bioinformatics and Computational Biology