transform_coordinate_matrices.py – Transform 2 coordinate matrices

Description:

This script transforms 2 coordinate matrices (e.g., the output of principal_coordinates.py) using procrustes analysis to minimize the distances between corresponding points. Monte Carlo simulations can additionally be performed (-r random trials are run) to estimate the probability of seeing an M^2 value as extreme as the actual M^2.

Usage: transform_coordinate_matrices.py [options]

Input Arguments:

Note

[REQUIRED]

-i, --input_fps

Comma-separated input files

-o, --output_dir

The output directory

[OPTIONAL]

-r, --random_trials

Number of random permutations of matrix2 to perform. [default: (no Monte Carlo analysis performed)]

-d, --num_dimensions

Number of dimensions to include in output matrices [default: 3]

-s, --sample_id_map_fp

Map of original sample ids to new sample ids [default: None]

--store_trial_details

Store PC matrices for individual trials [default: False]

Output:

Two transformed coordinate matrices corresponding to the two input coordinate matrices, and (if -r was specified) a text file summarizing the results of the Monte Carlo simulations.

Write the transformed procrustes matrices to file:

transform_coordinate_matrices.py -i unweighted_unifrac_pc.txt,weighted_unifrac_pc.txt -o procrustes_output

Generate transformed procrustes matrices and monte carlo p-values:

transform_coordinate_matrices.py -i unweighted_unifrac_pc.txt,weighted_unifrac_pc.txt -o mc_procrustes_output -r 1000