fast_sparCC

A fast command line interface to find correlations in biom tables with SparCC.


License
BSD-3-Clause
Install
pip install fast_sparCC==0.1.4

Documentation

fast_sparCC

A fast command line interface to find correlations in biom tables with sparCC.

A way to use sparCC on biom formatted tables such as those output by QIIME. Outputs a tab delimited file with pairs of features, correlation values and optional p-values. Includes options for filtering input tables, multiprocessing for bootstraping correlations values to determine significance and p-value adjustment with FDR or Bonferroni correction.

Installation Instructions

Install from pip using the command:

pip install fast_sparCC

Or install fast_sparCC from github by using navigating to the folder of your choice and using these commands:

git clone https://github.com/shafferm/fast_sparCC.git
cd fast_sparCC
python setup.py install

Example usage:

Calculate correlations only:

fast_sparCC.py -i example.biom -o correls.txt --corr_only

Calculate correlations only on table filtered based on Friedman and Alm 2012:

fast_sparCC.py -i example.biom -o correls.txt --corr_only --sparcc_filter

Calculate correlations and p-values based on 1000 bootstraps:

fast_sparCC.py -i example.biom -o correls.txt -b 1000

Calculate correlations and p-values based on 1000 bootstraps and 10 processors:

fast_sparCC.py -i example.biom -o correls.txt -b 1000 --procs 10

API for use in custom python scripts:

Additionally the functions can be imported for use in your own python scripts. This can be used with and without bootstrapping to calculate p-values.

Example usage of sparcc correlations:

#!/usr/local/bin/python2

from biom import load_table
from sparcc_fast import sparcc_correlation

table = load_table("example.biom")
correls = sparcc_correlation(table)
correls.to_csv("correls.txt")

Example usage of sparcc correlations with bootstrapping:

#!/usr/local/bin/python2

from biom import load_table
from sparcc_fast import sparcc_correlation_w_bootstraps

table = load_table("example.biom")
correls = sparcc_correlation_with_bootstraps(table, procs=3, bootstraps=1000)
correls.to_csv("correls.txt")