Metadata-Version: 2.1
Name: qnorm
Version: 0.2.2
Summary: Quantile normalization
Home-page: https://github.com/Maarten-vd-Sande/qnorm
Author: ['Maarten van der Sande <maartenvandersande@hotmail.com>']
License: MIT
Description: # qnorm
        [![PyPI version](https://badge.fury.io/py/qnorm.svg)](https://badge.fury.io/py/qnorm)
        [![Anaconda version](https://anaconda.org/conda-forge/qnorm/badges/version.svg)](https://anaconda.org/conda-forge/qnorm)
        ![tests](https://github.com/Maarten-vd-Sande/qnorm/workflows/tests/badge.svg)
        
        quantile normalization made easy.
        
        ## Code example
        
        We recreate the example of [Wikipedia](https://en.wikipedia.org/wiki/Quantile_normalization):
        
        ```python
        import pandas as pd
        import qnorm
        
        df = pd.DataFrame({'C1': {'A': 5, 'B': 2, 'C': 3, 'D': 4},
                           'C2': {'A': 4, 'B': 1, 'C': 4, 'D': 2},
                           'C3': {'A': 3, 'B': 4, 'C': 6, 'D': 8}})
        
        print(qnorm.quantile_normalize(df))
        ```
        
        which is what we expect:
        
        ```
                 C1        C2        C3
        A  5.666667  5.166667  2.000000
        B  2.000000  2.000000  3.000000
        C  3.000000  5.166667  4.666667
        D  4.666667  3.000000  5.666667
        ```
        
        **NOTE**: The function quantile_normalize also accepts numpy arrays. 
        
        ## Command Line Interface (CLI) example
        
        Qnorm also contains a CLI for converting csv/tsv files. The CLI depends on pandas, but this is an optional dependency of qnorm. To make use of the CLI make sure to install pandas in your current environment as well!
        
        
        ```console
        user@comp:~$ qnorm --help
        
        usage: qnorm [-h] [-v] table
        
        Quantile normalize your table
        
        positional arguments:
          table          input csv/tsv file which will be quantile normalized
        
        optional arguments:
          -h, --help     show this help message and exit
          -v, --version  show program's version number and exit
        ```
        
        And again the example of [Wikipedia](https://en.wikipedia.org/wiki/Quantile_normalization):
        
        ```console
        user@comp:~$ cat table.tsv
                C1      C2      C3
        A       5       4       3
        B       2       1       4
        C       3       4       6
        D       4       2       8
        
        user@comp:~$ qnorm table.tsv
                C1      C2      C3
        A       5.666666666666666       5.166666666666666       2.0
        B       2.0     2.0     3.0
        C       3.0     5.166666666666666       4.666666666666666
        D       4.666666666666666       3.0     5.666666666666666
        ```
        
        **NOTE:** the qnorm cli assumes that the first column and the first row are used as descriptors, and are "ignored" in the quantile normalization process. Lines starting with a hashtag "#" are treated as comments and ignored.
        
        ## Installation
        
        ### pip
        
        ```console
        user@comp:~$ pip install qnorm
        ```
        
        ### conda
        
        Installing qnorm from the conda-forge channel can be achieved by adding conda-forge to your channels with:
        
        ```console
        user@comp:~$ conda config --add channels conda-forge
        ```
        
        Once the conda-forge channel has been enabled, qnorm can be installed with:
        
        ```console
        user@comp:~$ conda install qnorm
        ```
        
        ### local
        
        clone the repository
        
        ```console
        user@comp:~$ git clone https://github.com/Maarten-vd-Sande/qnorm
        ```
        
        And install it
        
        ```console
        user@comp:~$ cd qnorm
        user@comp:~$ pip install .
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 1 - Planning
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >3.6
Description-Content-Type: text/markdown
