py4sci

Previous topic

scaProcessMSA Module

Next topic

scaSectorID Module

This Page

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
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.