# scaSectorID¶

The scaSectorID script does the preliminaries of sector identification and stores the outputs using the python tool pickle:

1. Chooses $$k_{max}$$ (the number of significant eigenmodes) by comparison of the $$\tilde{C_{ij}}$$ eigenspectrum to that for the randomized matrices

2. Rotates the top $$k_{max}$$ eigenvectors using independent components analysis

3. 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.

4. 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