Examples used in this vignette will use the GlobalPatterns dataset from phyloseq. Or, in some cases, the enterotype dataset.

library(phyloseq)
data(GlobalPatterns)


Abundance

abundance_heatmap

Create a ggplot object heatmap of abundances of each taxa for each sample.


Usage

abundance_heatmap(phyloseq_obj, classification = NULL, treatment, subset = NULL, 
  transformation = 'none', colors = 'default')


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.
classification Column name as a string in the tax_table for the factor

to conglomerate by. transformation | Transformation to be used on the data. “none”, “relative_abundance”, “log”, “log10”, “log1p”, “log2”, “asn”, “atanh”, “boxcox”, “exp”, “identity”, “logit”, “probability”, “probit”, “reciprocal”, “reverse” and “sqrt”. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

abundance_heatmap(GlobalPatterns, classification = 'Phylum',
  treatment = "SampleType", transformation = 'log2')
## Warning in conglomerate_taxa(phyloseq_obj, classification, hierarchical =
## FALSE): trees cannot be preserved after taxa conglomeration



abundance_lines

Create a ggplot object line graph of abundances across samples


Usage

abundance_lines(phyloseq_obj, classification, treatment, subset = NULL, 
  relative_abundance = FALSE, points = TRUE, colors = 'default')


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
classification Column name as a string 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. relative_abundance | If TRUE, transforms the abundance data into relative abundance by sample. points | Whether or not to display point on the lines for each data-point. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

abundance_lines(GlobalPatterns, classification = 'Phylum', 
  treatment = "SampleType", subset = c('Soil', 'Mock', 'Skin', 'Ocean'), 
  relative_abundance = TRUE)



phylogeny_profile

Creates a ggplot object of the stacked bar plots of a classification level in the tax_data, for each sample.


Usage

phylogeny_profile(phyloseq_obj, classification, treatment, subset = NULL,
  merge = TRUE, relative_abundance = FALSE, colors = 'default')


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
classification Column name as a string 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. merge | If FALSE, will show separation of individuals within each classification. relative_abundance | If TRUE, transforms the abundance data into relative abundance by sample. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

phylogeny_profile(GlobalPatterns, classification = 'Phylum', 
  treatment = "SampleType", merge = TRUE, 
  relative_abundance = TRUE)



taxa_abundance_bars

Creates a ggplot object of the side-by-side bar plots of a classification level in the tax_data.


Usage

taxa_abundance_bars(phyloseq_obj, classification = NULL, treatment, subset = NULL, transformation = 'none', colors = 'default')


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
phyloseq_obj A phyloseq-class object.
classification Column name as a string 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. transformation | Transformation to be used on the data. “none”, “mean”, “median”, “sd”, “log”, “log10” colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

filtered_obj <- taxa_filter(GlobalPatterns, frequency = 0.8)
taxa_abundance_bars(filtered_obj, classification = 'Phylum',
  treatment = "SampleType")



taxa_core_graph

Create graph of the core taxa seen in phyloseq-object over a range of abundance and smaple-frequency values.


Usage

taxa_core_graph(phyloseq_obj, treatment = NULL, subset = NULL, 
  frequencies = seq(0.1, 1, 0.1), abundance_thresholds = seq(0.01, 1, 0.01), 
  colors = 'default')


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
classification Column name as a string 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. frequencies | The range of proportions of samples the taxa are found in. abundance_thresholds | The range of minimum relative abundances the taxa are found in for each sample. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

taxa_core_graph(GlobalPatterns, abundance_thresholds = seq(0.01, 0.25, 0.01))



variable_correlation_heatmap

Computes the correlation of numerical variables with taxa and graphs as heatmap.


Usage

variable_correlation_heatmap(phyloseq_obj, variables,
  treatment = NULL, subset = NULL, classification = NULL,
  method = 'spearman', limits = c(-0.8, 0.8),
  colors = 'default', significance_color = 'white', cores = 1)


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
variables Numerical factors within the in the sample_data to correlate with

the abundance data. classification | Column name as a string 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. method | Which correlation method to calculate, “pearson”, “spearman”. limits | The range for the legend, smaller limits will accentuate smaller correlations. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors. significance_color | the color to use for the significance stars. cores numeric value for number of CPU cores to use for the pair-wise permutations. Default (1), 0 uses max cores available. Parallelization not available for systems running MacOS without openMP configuration.

Examples

filtered_obj <- phyloseq::subset_samples(enterotype, 
  !is.na(enterotype@sam_data$ClinicalStatus))
filtered_obj <- taxa_filter(filtered_obj, frequency = 0.65)
variable_correlation_heatmap(filtered_obj, variables = 'Age',
 treatment = "ClinicalStatus", subset = c("healthy", "elderly", "obese"), 
 classification = 'Genus', method = 'spearman', cores = 1, 
 significance_color = 'black')


Diversity


alpha_diversity_graph

Create a ggplot-object box-plot of the alpha-diversity from a phyloseq-object.


Usage

alpha_diversity_graph(phyloseq_obj, index = 'shannon', treatment = NULL, 
  subset = NULL, colors = 'default')


Arguments

Call Description

phyloseq_obj | A phyloseq-class object. index | The diversity index to calculate (‘shannon’, ‘simpson’, ‘invsimpson’) 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. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

alpha_diversity_graph(GlobalPatterns, index = 'shannon', treatment = "SampleType",
  subset = NULL, colors = 'default')



dendrogram_phyloseq

Create a ggplot-object dendrogram of the distance measure from a phyloseq-object.


Usage

dendrogram_phyloseq(phyloseq_obj, treatment = NULL, method = 'bray',
  colors = 'default')


Arguments

Call Description

phyloseq_obj | A phyloseq-class object. treatment | Column name as a string, or vector of, in the sample_data. colors | Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors. method | the distance measurement algorithm to use from “manhattan”, “euclidean”, “canberra”, “bray”, “kulczynski”, “gower”, “morisita”, “horn”, “mountford”, “jaccard”, “raup”, “binomial”, “chao”, “altGower”, “cao”, “mahalanobis”, “clark”

Examples

dendrogram_phyloseq(GlobalPatterns, "SampleType", method = 'bray')
## Registered S3 method overwritten by 'dendextend':
##   method     from 
##   rev.hclust vegan


nmds_phyloseq

Creates a ggplot object of the NMDS ordination of samples.


Usage

nmds_phyloseq(phyloseq_obj, treatment, circle = TRUE, labels = FALSE, 
  colors = 'default', verbose = TRUE)


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
treatment Column name as a string, or vector of, in the sample_data.
circle Whether to add a stat_elipse around eache treatment factor on the graph.
labels Column name as a string or numeric in the sample_data to use to place labels of that factor instead of circle points.
colors Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.
verbose Whether or not to print the vegan::metaMDS stress convergence to console (TRUE) or not (FALSE).

Examples

nmds_phyloseq(GlobalPatterns, c("SampleType"), circle = TRUE, verbose = FALSE)
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse



pcoa_phyloseq

Creates a ggplot object of the NMDS ordination of samples.


Usage

pcoa_phyloseq(phyloseq_obj, treatment, x = 1, y = 2, method = 'bray',
  circle = 0.95, colors = 'default', labels = NULL)


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
treatment Column name as a string, or vector of, in the sample_data.
x the numerical principle compenent to use as the x-axis.
y the numerical principle compenent to use as the y-axis.
method the distance measurement algorithm to use from “manhattan”, “euclidean”, “canberra”, “bray”, “kulczynski”, “gower”, “morisita”, “horn”, “mountford”, “jaccard”, “raup”, “binomial”, “chao”, “altGower”, “cao”, “mahalanobis”, “clark”
circle Whether to add a stat_elipse around eache treatment factor on the graph.
labels Column name as a string or numeric in the sample_data to use to place labels of that factor instead of circle points.
colors Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

pcoa_phyloseq(GlobalPatterns, "SampleType", circle = FALSE)



tsne_phyloseq

Creates a ggplot object of the t-SNE ordination of the samples. t-Distributed Tochastic Neighbor Embedding is a machine learning algorithm of nonlinear dimensionality reduction for high-dimensional data.


Usage

tsne_phyloseq(phyloseq_obj, treatment, perplexity = 10, circle = FALSE, 
  labels = FALSE, colors = 'default')


Arguments

Call Description
phyloseq_obj A phyloseq-class object.
treatment Column name as a string, or vector of, in the sample_data.vector of multiple columns and they will be combined into a new column.
perplexity similar to selecting the number of neighbors to consider in decision making (should not be bigger than 3 * perplexity < nrow(X) - 1, see Rtsne for interpretation)
circle Whether to add a stat_elipse around each treatment factor on the graph.
labels Column name as a string or numeric in the sample_data to use to place labels of that factor instead of circle points.
colors Name of a color set from the RColorBrewer package or a vector palete of R-accepted colors.

Examples

tsne_phyloseq(GlobalPatterns, treatment = "SampleType", perplexity = 5,
  circle = FALSE)




Schuyler Smith
Ph.D. Bioinformatics and Computational Biology