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a package for orthogonal linear separation analysis (OLSA)

Project description

Copyright (c) 2018 Tadahaya Mizuno

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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Description-Content-Type: UNKNOWN
Description: ========
OLSAPY
========

OLSAPY: Orthogonal Linear Separation Analysis in Python
=======================================================
* OLSA is an analysis method of omics data to decompose the complex effects of a perturbagen into basic components.
* OLSAPY is a package for OLSA in python.
* OLSA can be applied to any kinds of omics data such as RNA-seq, proteome, and so on.

Dependency
=======================================================
* python 3.6
* requirements: numpy, pandas, scipy

Setup
=======================================================
::

pip install olsapy

Usage
=======================================================
1. prepare a profile matrix with variables in rows and samples in columns as a csv file
2. import necessary modules as follows:

::

from olsapy import olsa as ol

3. generate a DataClass object as follows:

::

dat = ol.DataClass()

4. load the prepared data file into the generated object as follows:

::

dat.load(<a path for the data file>)

5. run OLSA and obtain a Result object as follows:

::

res = ol.olsa(dat)

6. export each result as csv files as follows:

::

res.export()

7. each result can be extracted as a dataframe if necessary as follows:

::

dataframe = res.rsm()

* a sample code for running OLSA described below:

::

from olsapy import olsa as ol

filein = '<file path>'

#run OLSA simply
dat = ol.DataClass() #generate a DataClass object
dat.load(filein) #load data
res = ol.olsa(dat) #run OLSA and obtain a Result object
res.export() #save data

#run OLSA with some options
df = res.rsm() #.rsm(), etc. extract stored data in a Result object as a dataframe
dat2 = ol.DataClass()
dat2.load_df(df) #load dataframe into a DataClass object
res2 = ol.olsa(dat2,accumulation=0.5) #accumulation determines the vectors subjected to varimax rotation
res2.export(CM=True,TS=False) #results to be exported can be chosen.

Licence
=======================================================
This software is released under the MIT License, see LICENSE.

Authors
=======================================================
Setsuo Kinoshita, Shotaro Maedera, and Tadahaya Mizuno

References
=======================================================
http://www.ilincs.org/ilincs/

Bug Report
=======================================================
If you would like to report any bugs about olsapy, don't hesitate to create an issue on github here, or email me: tadahaya@gmail.com
Keywords: olsa,usspca,profiling,omics,bioinformatics,profile data,transcriptome
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Win32 (MS Windows)
Classifier: Framework :: IPython
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: Microsoft :: Windows :: Windows 10
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics

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1.0.1

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