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:
 Sequence correlations:
 Compute simMat = the global sequence similarity matrix for the alignment
 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 (\(U^2\))
 Positional correlations:
 Compute the singlesite position weights and positional conservation values (\(D_i\) and \(D_i^a\))
 Compute the dimensionreduced SCA correlation matrix \(\tilde{C_{ij}}\), the projected alignment \(tX\), and the projector
 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:  


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 3clause license, please see the file LICENSE for details.