knitr::opts_chunk$set(fig.width=8, fig.height=4, cache = TRUE) library(phylosmith) data(soil_column)
Examples used in this vignette will use the GlobalPatterns
dataset from phyloseq
.
library(phyloseq) data(GlobalPatterns)
Performs a library-size normalization on the phyloseq-object
Usage
library_size(phyloseq_obj)
Arguments
Call | Description
-------------------- | ------------------------------------------------------------
phyloseq_obj
| A phyloseq-class object.
Examples
phyloseq::sample_sums(GlobalPatterns) normalized_obj <- library_size(GlobalPatterns) phyloseq::sample_sums(normalized_obj)
Transforms the the otu_table
count data to relative abundance. Relative abundance sets the count sums for each sample to 1, and then assigns each taxa an abundance equal to its proportion on the total sum (very low abundance taxa may ).
Usage
relative_abundance(phyloseq_obj)
Arguments
Call | Description
-------------------- | ------------------------------------------------------------
phyloseq_obj
| A phyloseq-class object that contains otu_table
count data.
Examples
phyloseq::sample_sums(relative_abundance(GlobalPatterns, 10))
Used to identify which entries in the taxa_table are shared among
treatment-groups. It will return a vector
of taxa names that are all seen
in n
groups.
Usage
common_taxa(phyloseq_obj, treatment = NULL, subset = NULL, n = 'all')
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.
n
| Number of treatment groups that need to share the taxa to be considered a common taxa.
Examples
common_taxa(GlobalPatterns, treatment = 'SampleType', subset = 'Tongue', n = 'all')[1:35]
Filter taxa in phyloseq-object to only include core taxa
Usage
taxa_core(phyloseq_obj, treatment = NULL, subset = NULL, frequency = 0.5, abundance_threshold = 0.01)
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.
frequency
| The proportion of samples the taxa is found in.
abundance_threshold
| The minimum relative abundance the taxa is found in for each sample.
Examples
The soil_column
data has 18,441 OTUs listed in its taxa_table
.
taxa_core(GlobalPatterns, frequency = 0.2, abundance_threshold = 0.01)
Computes the proportion of a taxa classification. This can be done by treatment, sample, or across the dataset.
Usage
taxa_proportions(phyloseq_obj, classification, treatment = NA)
Arguments
Call | Description
-------------------- | ------------------------------------------------------------
phyloseq_obj
| phyloseq_obj
| A phyloseq-class object.
classification
| Column name as a string
or numeric
in the tax_table for the prportions to be reported on.
treatment
| Column name as a string
, or vector
of, in the sample_data
.
Examples
taxa_proportions(GlobalPatterns, 'Phylum', treatment = "SampleType")
taxa_proportions(GlobalPatterns, 'Phylum', treatment = 'Sample')
taxa_proportions(GlobalPatterns, 'Phylum', treatment = NULL)
Identify which taxa are unique to a specific treatment-group. It will return a list
of vector
s of taxa-names that are only seen in each group.
Usage
unique_taxa(phyloseq_obj, treatment, subset = NULL)
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.
Examples
uniques <- unique_taxa(GlobalPatterns, treatment = "SampleType") data.frame(lapply(uniques, "length<-", max(lengths(uniques))))
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