MUSICC: A marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome.
Project description
MUSiCC Documentation
MUSiCC is a marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome, developed and maintained by the Borenstein group at the University of Washington.
Availability
MUSiCC is available through the following sources:
As a Python module from GitHub or PyPI (see installation instructions below)
As an online tool at: http://elbo.gs.washington.edu/software_musicc.html.
License
MUSiCC is distributed under a BSD license and can be readily incorporated into custom analysis tools.
Installation Instructions
Prerequisites for installing:
In order for MUSiCC to run successfully, the following Python modules should be pre-installed on your system:
Numpy >= 1.6.1 (http://www.numpy.org/)
Scipy >= 0.9 (http://www.scipy.org/)
Scikit-learn >= 0.15.2 (http://scikit-learn.org/stable/)
Pandas >= 0.14 (http://pandas.pydata.org/)
If you have pip installed, you can install these packages by running the following command:
pip install -U numpy scipy scikit-learn pandas
Installing MUSiCC:
To install MUSiCC, download the package from https://github.com/omanor/MUSiCC/archive/1.0.tar.gz
After downloading MUSiCC, you’ll need to unzip the file. If you’ve downloaded the release version, do this with the following command:
tar -xzf MUSiCC-1.0.tar.gz
You’ll then change into the new MUSiCC directory as follows:
cd MUSiCC-1.0
and install using the following command:
python setup.py install
ALTERNATIVELY, you can install MUSiCC directly from PyPI by running:
pip install -U MUSiCC
Testing the software package
After downloading and installing the software, we recommend testing it by running the following command:
test_MUSiCC.py
This will invoke a series of tests. A correct output should end with:
Ran 3 tests in X.XXXXs
OK
MUSiCC API via the command line
The MUSiCC module handles all calculations internally. MUSiCC offers an interface to the MUSiCC functionality via the command line and the run_MUSiCC script.
Usage:
run_MUSiCC.py input_file [options]
Required arguments:
- input_file
Input abundance file to correct
Optional arguments:
- -h, –help
show help message and exit
- -o OUTPUT_FILE, –out OUTPUT_FILE
Output destination for corrected abundance (default: MUSiCC.tab)
- -if {tab,csv}, –input_format {tab,csv}
Option indicating the format of the input file (default: tab)
- -of {tab,csv}, –output_format {tab,csv}
Option indicating the format of the output file (default: tab)
- -n, –normalize
Apply MUSiCC normalization (default: false)
- -c {use_generic, learn_model}, –correct {use_generic,learn_model}
Correct abundance per-sample using MUSiCC (default: false)
- -perf, –performance
Calculate model performance on various gene sets (may add to running time) (default: false)
- -v, –verbose
Increase verbosity of module (default: false)
MUSiCC API via python script
MUSiCC can also be used directly inside a python script. Passing variables and flags to the MUSiCC script is done by creating a dictionary and passing it to the function correct_and_normalize, as shown below.
Usage:
>>> from musicc.core import correct_and_normalize >>> musicc_args = {'input_file': 'test_musicc/lib/python3.3/site-packages/musicc/examples/simulated_ko_relative_abundance.tab', 'output_file': 'MUSiCC.tab','input_format': 'tab', 'output_format': 'tab', 'musicc_inter': True, 'musicc_intra': 'learn_model','compute_scores': True, 'verbose': True} >>> correct_and_normalize(musicc_args)
Required arguments:
- input_file
Input abundance file to correct
Optional arguments:
- output_file
Output destination for corrected abundance (default: MUSiCC.tab)
- input_format {‘tab’,’csv’}
Option indicating the format of the input file (default: ‘tab’)
- output_format {‘tab’,’csv’}
Option indicating the format of the output file (default: ‘tab’)
- musicc_inter {True, False}
Apply MUSiCC normalization (default: False)
- musicc_intra {‘use_generic’, ‘learn_model’, ‘None’}
Correct abundance per-sample using MUSiCC (default: ‘None’)
- compute_scores {True, False}
Calculate model performance on various gene sets (may add to running time) (default: False)
- verbose {True, False}
Increase verbosity of module (default: False)
Examples
In the musicc/examples directory, the file simulated_ko_relative_abundance.tab contains simulated KO abundance measurements of 20 samples described in the MUSiCC manuscript. Using this file as input for MUSiCC results in the following files:
simulated_ko_MUSiCC_Normalized.tab (only normalization)
simulated_ko_MUSiCC_Normalized_Corrected_use_generic.tab (normalize and correct using the generic model learned from HMP)
simulated_ko_MUSiCC_Normalized_Corrected_learn_model.tab (normalize and correct learning a new model for each sample)
The commands used were the following (via command line):
run_MUSiCC.py musicc/examples/simulated_ko_relative_abundance.tab -n -perf -v -o musicc/examples/simulated_ko_MUSiCC_Normalized.tab
run_MUSiCC.py musicc/examples/simulated_ko_relative_abundance.tab -n -c use_generic -perf -v -o musicc/examples/simulated_ko_MUSiCC_Normalized_Corrected_use_generic.tab
run_MUSiCC.py musicc/examples/simulated_ko_relative_abundance.tab -n -c learn_model -perf -v -o musicc/examples/simulated_ko_MUSiCC_Normalized_Corrected_learn_model.tab
Citing Information
If you use the MUSiCC software, please cite the following paper:
MUSiCC: A marker genes based framework for metagenomic normalization and accurate profiling of gene abundances in the microbiome. Ohad Manor and Elhanan Borenstein. Genome Biology
Question forum
For MUSiCC announcements and questions, including notification of new releases, you can visit the MUSiCC users forum at: https://groups.google.com/forum/#!forum/musicc-users
HISTORY
1.0 (2014-10-07)
Initial release
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