# scaCore¶

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 single-site position weights and positional conservation values (\(D_i\) and \(D_i^a\))

Compute the dimension-reduced 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**

**Keyword Arguments**`-i`*.db (the database produced by running scaProcessMSA)

`-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 -i 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.