Generates a heatmap of amplicon data by using sample metadata to aggregate samples and taxonomy to aggregate OTUs.
amp_heatmap(data, group_by = "")
(required) Data list as loaded with
(recommended) Group the samples by a categorical variable in the metadata. If
Facet the samples by a categorical variable in the metadata.
(logical) Transform the OTU read counts to be in percent per sample. (default:
The taxonomic level to aggregate the OTUs. (default:
Additional taxonomic level(s) to display, e.g.
The number of taxa to show, or a vector of taxa names. (default:
Converts a specific phylum to class level instead, e.g.
How to show OTUs without taxonomic information. One of the following:
Reorder the x axis by providing a character vector of the x axis values in the desired order, or
Reorder the y axis by providing a character vector of the y axis values in the desired order, or
(logical) Plot the values on the heatmap or not. (default:
The size of the plotted values. (default:
A vector of breaks for the abundance legend, fx
The type of scale used for the coloring of abundances, either
(logical) Whether to color missing values with the lowest color in the scale or not. (default:
Calculate and display either
All values below this value are given the same color. (default:
All values above this value are given the same color.
Sorts the heatmap by the most abundant taxa in a specific sample or group of samples. Provide a sample name or a specific value of the group defined by the
A variable or a specific sample in the metadata to normalise the counts by.
Scale the abundances by a variable in the metadata.
Vector of colors for the colorscale, e.g.
Number of digits to show with the values. (default:
(logical) Return a data frame to print as raw text instead of a ggplot2 object. (default:
Return a 2-column grid plot instead, showing known functional information about the Genus-level OTUs next to the heatmap. When using this feature, make sure that either
A ggplot2 object, or a data frame if
textmap = TRUE.
By default the raw read counts in the abundance matrix are normalised (transformed to percentages) by some plotting functions automatically (for example
amp_timeseries, and more). This means that the relative abundances shown will be calculated based on the remaining taxa after the subset, not including the removed taxa, if any. To circumvent this, set
normalise = TRUE when subsetting with the
amp_subset_samples functions, and then set
normalise = FALSE in the plotting function. This will transform the OTU counts to relative abundances BEFORE the subset, and setting
normalise = FALSE will skip the transformation in the plotting function, see the example below.
data("MiDAS") subsettedData <- amp_subset_samples(MiDAS, Plant %in% c("Aalborg West", "Aalborg East"), normalise = TRUE ) amp_heatmap(subsettedData, group_by = "Plant", tax_aggregate = "Phylum", tax_add = "Genus", normalise = FALSE )
The complete raw data used to generate any ggplot can always be accessed with
ggplot2_object$data when the plot is saved as a ggplot2 object. Additionally, a "textmap" version of the generated heatmap can also be generated by setting
textmap = TRUE to only extract the raw data as shown on the particular heatmap, see examples.
# Load example data data("AalborgWWTPs") # Heatmap grouped by WWTP amp_heatmap(AalborgWWTPs, group_by = "Plant")# Heatmap of 20 most abundant Genera (by mean) grouped by WWTP, split by Year, # values not plotted for visibility, phylum name added and colorscale adjusted manually amp_heatmap(AalborgWWTPs, group_by = "Plant", facet_by = "Year", plot_values = FALSE, tax_show = 20, tax_aggregate = "Genus", tax_add = "Phylum", color_vector = c("white", "red"), plot_colorscale = "sqrt", plot_legendbreaks = c(1, 5, 10) )# Heatmap with known functional information about the Genera shown to the right amp_heatmap(AalborgWWTPs, group_by = "Plant", tax_aggregate = "Genus", plot_functions = TRUE, functions = c("PAO", "GAO", "AOB", "NOB") )# A raw text version of the heatmap can be printed or saved as a data frame with textmap = TRUE: textmap <- amp_heatmap(AalborgWWTPs, group_by = "Plant", tax_aggregate = "Genus", plot_functions = TRUE, functions = c("PAO", "GAO", "AOB", "NOB"), textmap = TRUE ) textmap#> Aalborg East Aalborg West PAO GAO AOB NOB #> Tetrasphaera 5.478941 6.842382 POS NEG NEG NEG #> Trichococcus 7.098638 3.028602 NT NT NT NT #> Candidatus Microthrix 2.769444 6.503052 NEG NEG NEG NEG #> Rhodoferax 3.361297 2.403885 NEG NT NT NT #> Rhodobacter 1.847766 2.967430 NT NT NT NT #> Candidatus Promineofilum 1.317348 3.028949 NEG NEG NEG NEG #> Dechloromonas 1.220824 2.503898 VAR NEG NT NT #> Candidatus Defluviifilum 1.824867 1.502981 NEG NEG NEG NEG #> Propionicimonas 1.229790 1.726184 NT NT NT NT #> Fodinibacter 1.382478 1.530972 NT NT NT NT