beta_significance.py – This script runs any of a set of common tests to determine if a sample is statistically significantly different from another sample

Description:

The tests are conducted on each pair of samples present in the input otu table. See the unifrac tutorial online for more details (http://bmf2.colorado.edu/unifrac/tutorial.psp)

Usage: beta_significance.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --input_path

Input otu table in biom format

-o, --output_path

Output results path

-s, --significance_test

Significance test to use, options are ‘unweighted_unifrac’, ‘weighted_unifrac’, ‘weighted_normalized_unifrac’, or ‘p-test’

-t, --tree_path

Path to newick tree file

[OPTIONAL]

-n, --num_iters

Number of monte carlo randomizations [default: 100]

-k, --type_of_test

Type of significance test to perform, options are ‘all_together’, ‘each_pair’ or ‘each_sample’. [default: each_pair]

Output:

The script outputs a tab delimited text file with each pair of samples and a p value representing the probability that a random sample/sequence assignment will result in more dissimilar samples than the actual pair of samples.

Example:

Perform 100 randomizations of sample/sequence assignments, and record the probability that sample 1 is phylogenetically different from sample 2, using the unifrac monte carlo significance test. The test is run for all pairs of samples.

beta_significance.py -i otu_table.biom -t rep_set.tre -s unweighted_unifrac -o unw_sig.txt