beta_diversity_through_plots.py – A workflow script for computing beta diversity distance matrices and generating PCoA plots

Description:

This script will perform beta diversity, principal coordinate analysis, and generate a preferences file along with 3D PCoA Plots.

Usage: beta_diversity_through_plots.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --otu_table_fp

The input biom table [REQUIRED]

-m, --mapping_fp

Path to the mapping file [REQUIRED]

-o, --output_dir

The output directory [REQUIRED]

[OPTIONAL]

-t, --tree_fp

Path to the tree file [default: None; REQUIRED for phylogenetic measures]

-p, --parameter_fp

Path to the parameter file, which specifies changes to the default behavior. See http://www.qiime.org/documentation/file_formats.html#qiime-parameters . [if omitted, default values will be used]

--color_by_all_fields

Plots will have coloring for all mapping fields [default: False; only include fields with greater than one value and fewer values than the number of samples]

-f, --force

Force overwrite of existing output directory (note: existing files in output_dir will not be removed) [default: None]

-w, --print_only

Print the commands but don’t call them – useful for debugging [default: False]

-a, --parallel

Run in parallel where available [default: False]

-e, --seqs_per_sample

Depth of coverage for even sampling [default: None]

--suppress_emperor_plots

Do not generate emperor plots [default: False]

-O, --jobs_to_start

Number of jobs to start. NOTE: you must also pass -a to run in parallel, this defines the number of jobs to be started if and only if -a is passed [default: 1]

Output:

This script results in a distance matrix (from beta_diversity.py), a principal coordinates file (from principal_coordinates.py), and folders containing the resulting PCoA plots (accessible through html files).

Example:

Given an OTU table, a phylogenetic tree, an even sampling depth, and a mapping file, perform the following steps: 1. Randomly subsample otu_table.biom to even number of sequences per sample (100 in this case); 2. Compute a weighted and unweighted unifrac distance matrices (can add additional metrics by passing a parameters file via -p); 3. Peform a principal coordinates analysis on the result of Step 2; 4. Generate a 2D and 3D plots for all mapping fields.

beta_diversity_through_plots.py -i otu_table.biom -o bdiv_even100/ -t rep_set.tre -m Fasting_Map.txt -e 100