In schuyler-smith/phylosmith: Functions to help analyze data as phyloseq objects

knitr::opts_chunk$set(fig.width=16, fig.height=8, cache = TRUE)
library(phylosmith)

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

Examples

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

network_ps

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

<br>
***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***
```r
filtered_obj <- conglomerate_taxa(GlobalPatterns, "Phylum")
network_ps(filtered_obj, treatment = "SampleType", rho = 0.6)

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')