scaCore Module

The scaCore script runs the core calculations for SCA, and stores the output using the python tool pickle. These calculations can be divided into two parts:

1. Sequence correlations:

   1. Compute simMat = the global sequence similarity matrix for the alignment
   2. Compute Useq and Uica = the eigenvectors (and independent components) for the following sequence correlation matrices:
      • unweighted (\(U^0\))
      • sequence weights applied (\(U^1\))
      • both sequence and position weights applied (\(U^2\))

2. Positional correlations:

   1. Compute the single-site position weights and positional conservation values (\(D_i\) and \(D_i^a\))
   2. Compute the dimension-reduced SCA correlation matrix \(\tilde{C_{ij}}\), the projected alignment \(tX\), and the projector
   3. Compute Ntrials of the randomized SCA matrix, and the eigenvectors and eigenvalues associated with each

Arguments:

*.db (the database produced by running scaProcessMSA.py).

Keyword Arguments:  

-n	norm type for dimension-reducing the sca matrix. Options are: ‘spec’ (the spectral norm) or ‘frob’ (frobenius norm). Default: frob
-l	lambda parameter for pseudo-counting the alignment. Default: 0.03
--Ntrials, -t	number of randomization trials
--matlab, -m	write out the results of these calculations to a matlab workspace for further analysis

Example:

>>> ./scaCore.py PF00071_full.db 

By:Rama Ranganathan, Kim Reynolds On:8.5.2014

Copyright (C) 2015 Olivier Rivoire, Rama Ranganathan, Kimberly Reynolds This program is free software distributed under the BSD 3-clause license, please see the file LICENSE for details.