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
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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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