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scaCore ModuleΒΆ

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

*.db (the database produced by running

Keyword Arguments:

norm type for dimension-reducing the sca matrix. Options are: ‘spec’ (the spectral norm) or ‘frob’ (frobenius norm). Default: frob


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

>>> ./ PF00071_full.db 
By:Rama Ranganathan, Kim Reynolds

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.