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.. _transform_coordinate_matrices:
.. index:: transform_coordinate_matrices.py
*transform_coordinate_matrices.py* -- Transform 2 coordinate matrices
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**Description:**
This script transforms 2 coordinate matrices (e.g., the output of `principal_coordinates.py <./principal_coordinates.html>`_) 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:** :file:`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.
**Generate monte carlo p-values:**
::
transform_coordinate_matrices.py -r 1000 -i weighted_unifrac_coords.txt,unweighted_unifrac_coords.txt
**Write the transformed procrustes matrices to file:**
::
transform_coordinate_matrices.py -o out/ -i weighted_unifrac_coords.txt,unweighted_unifrac_coords.txt
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