scaSectorIDΒΆ
The scaSectorID script does the preliminaries of sector identification and stores the outputs using the python tool pickle:
Chooses \(k_{max}\) (the number of significant eigenmodes) by comparison of the \(\tilde{C_{ij}}\) eigenspectrum to that for the randomized matrices
Rotates the top \(k_{max}\) eigenvectors using independent components analysis
Defines the amino acid positions that significantly contribute to each of the independent components (ICs) by empirically fitting each IC to the t-distribution and selecting positions with greater than a specified cutoff (default: p=0.95) on the CDF.
Assign positions into groups based on the independent component with which it has the greatest degree of co-evolution.
- Key Arguments
- --input, -i
*.db (the database produced by running scaCore)
- --kpos, -k
number of significant eigenmodes for analysis (the default is to automatically choose using the eigenspectrum)
- --cutoff, -p
empirically chosen cutoff for selecting AA positions with a significant contribution to each IC, Default = 0.95
- --matlab, -m
write out the results of this script to a matlab workspace for further analysis
Example:
scaSectorID -i PF00071_full.db
- By
Kim Reynolds
- On
8.19.2014
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.