Metadata-Version: 2.1
Name: secat
Version: 1.0.3
Summary: Size-Exclusion Chromatography Algorithmic Toolkit
Home-page: https://github.com/grosenberger/secat
Author: George Rosenberger
Author-email: gr2578@cumc.columbia.edu
License: UNKNOWN
Description: SECAT: Size-Exclusion Chromatography Algorithmic Toolkit
        ============
        
        *SECAT* is an algorithm for the network-centric data analysis of SEC-SWATH-MS data. The tool is implemented as a multi-step command line application.
        
        Dependencies
        ------------
        
        *SECAT* depends on several Python packages (listed in ``setup.py``) and the ``viper`` R/Bioconductor package, accessed via ``rpy2``. SECAT has been tested on Linux (CentOS 7) and macOS (10.14) operating systems and might run on other versions too.
        
        Please install ``viper`` from [Bioconductor](https://doi.org/doi:10.18129/B9.bioc.viper) prior to SECAT.
        
        Installation
        ------------
        
        We strongly advice to install *SECAT* in a Python [*virtualenv*](https://virtualenv.pypa.io/en/stable/). *SECAT* is compatible with Python 3.7 and higher and installation should require a few minutes with a correctly set-up Python environment.
        
        Install the development version of *SECAT* from GitHub:
        
        ````
        pip install git+https://github.com/grosenberger/secat.git@master
        ````
        
        Install the stable version of *SECAT* from the Python Package Index (PyPI):
        
        ````
        pip install secat
        ````
        
        
        Running SECAT
        -------------
        
        SECAT requires 1-4h running time with a SEC-SWATH-MS data set of two conditions and three replicates each, covering about 5,000 proteins and 80,000 peptides on a typical desktop computer with 4 CPU cores and 16GB RAM.
        
        The exemplary input data (``HeLa-CC.tgz`` and ``Common.tgz`` are required) can be found on Zenodo: [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3515928.svg)](https://doi.org/10.5281/zenodo.3515928)
        
        The data set includes the expected output as SQLite-files. Note: Since the ``PyProphet`` semi-supervised learning step is initialized by a randomized seed, the output might vary slightly from run-to-run with numeric deviations. To completely reproduce the results, the pretrained PyProphet classifier can be applied to as described in the ``secat learn`` step. The Zenodo repository contains all parameters and instructions to reproduce the SECAT analysis results of the other data sets.
        
        *SECAT* consists of the following steps:
        
        
        **1. Data preprocessing**
        
        First, the input quantitative proteomics matrix and parameters are preprocessed to a single file:
        
        ````
        secat preprocess
        --out=hela_string_negative.secat \ # Output filename
        --sec=input/hela_sec_mw.csv \ # SEC annotation file
        --net=../common/9606.protein.links.v11.0.txt.gz \ # Reference PPI network
        --posnet=../common/corum_targets.txt.gz \ # Reference positive interaction network for learning
        --negnet=../common/corum_decoys.txt.gz \ # Reference negative interaction network for learning
        --uniprot=../common/uniprot_9606_20190402.xml.gz \ # Uniprot reference XML file
        --min_interaction_confidence=0 # Minimum interaction confidence
        input/hela_normsw.tsv \ # Input data files
        ````
        
        **2. Signal processing**
        
        Next, the signal processing is conducted in a parallelized fashion:
        
        ````
        secat score --in=hela_string_negative.secat --threads=8
        ````
        
        **3. PPI detection**
        
        The statistical confidence of the PPI is evaluated by machine learning:
        
        ````
        secat learn --in=hela_string_negative.secat --threads=5
        ````
        
        **4. PPI quantification**
        
        Quantitative features are generated for all PPIs and proteins:
        
        ````
        secat quantify --in=hela_string_negative.secat --control_condition=inter
        ````
        
        **5. Export of results**
        
        CSV tables can be exported for import in downstream tools, e.g. Cytoscape:
        
        ````
        secat export --in=hela_string_negative.secat
        ````
        
        **6. Plotting of chromatograms**
        
        PDF reports can be generated for the top (or selected) results:
        
        ````
        secat plot --in=hela_string_negative.secat
        ````
        
        **7. Report of statistics**
        
        Statistics reports can be generated for the top (or selected) results:
        
        ````
        secat statistics --in=hela_string_negative.secat
        ````
        
        **Further options and default parameters**
        
        All options and the default parameters can be displayed by:
        ````
        secat --help
        secat preprocess --help
        secat score --help
        secat learn --help
        secat quantify --help
        secat export --help
        secat plot --help
        secat statistics --help
        ````
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
