visualize: Wrapper for convenient generation of MetAnnotate ggplot data

In MetAnnotate/metannoviz: Process and visualize output from the MetAnnotate pipeline

Description

Wrapper to generate a ggplot of MetAnnotate data with subset, colours, labels, and so on

Usage

visualize(
  metannotate_data_normalized_list,
  colouring_template_filename = NA,
  top_x = NA,
  percent_mode = "within_sample",
  normalizing_HMM = "auto",
  plot_normalizing_HMM = TRUE,
  dump_raw_data = FALSE,
  ...
)

Arguments

metannotate_data_normalized_list	List output of combine_replicates - replicates must be collapsed
colouring_template_filename	Filename of the colouring template you want to load If the file does not exist, then this function will write a template to that file If 'NA' is entered, then the function will auto-generate colours and continue on
top_x	Numeric vector (length 1) giving the subsetting amount you desire. If top_x >=1, the script will return the "top_x most abundant taxa" for each Dataset/HMM.Family If top_x <1, the script will return "all taxa of (top_x * 100%) abundance or greater for each Dataset/HMM.Family - but see below.
percent_mode	If top_x <1, there are two different methods for keeping the most abundant organisms: "within_sample" – the normalized % abundance relative to rpoB is used "within_HMM" – the percent abundance of that taxon within the specific HMM gene hits is used. You won't notice much of a different between these modes unless one of your HMMs has very few hits and you want to show some of the taxa that were hit. This would be a good time to use 'within_HMM'.
normalizing_HMM	Name of the normalizing HMM (e.g., "rpoB")]; specify 'auto' to attempt auto-detection
plot_normalizing_HMM	Retain the normalizing_HMM in the final ggplot?
dump_raw_data	Return the normalized and subsetted table in lieu of a ggplot
...	Other fine-tuned plotting options controlled by generate_ggplot

Value

A ggplot of MetAnnotate data (or raw data; see above)

MetAnnotate/metannoviz documentation built on Aug. 2, 2020, 3:12 p.m.