Metadata-Version: 2.1
Name: singlecelldata
Version: 1.0.0
Summary: A python package for managing single-cell RNA-seq datasets.
Home-page: https://github.com/edwinv87/singlecell
Author: Edwin Vans
Author-email: vans.edw@gmail.com
License: UNKNOWN
Description: # SingleCell
        
        SingleCell is a python class available in the singlecelldata package for managing single-cell RNA-seq data. It contains three pandas dataframes; `data` for holding gene expression values (counts/normalized counts), `genedata` for holding more information about the genes e.g., gene names, and `celldata` which contains more information about cells such as cell types, labels etc. It is similar in concept to R package SingleCellExperiment on Bioconductor <https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html>.
        
        ## Installation
        
        The sinclecelldata package can be easily installed from PYPI using the following command:
        
        `pip install singlecelldata`
        
        Alternatively, the singlecelldata package can also be installed from Anaconda Cloud using the Anaconda package distribution software. From the anaconda prompt run the following command to install singlecelldata package in your environment:
        
        `conda install -c edwinvans singlecelldata`
        
        The user can also checkout the anaconda cloud page for more information <https://anaconda.org/edwinvans/singlecelldata>. The source code is available here on the GitHub repository.
        
        ## Usage
        
        The SingleCell class can be used to create an object which stores single-cell gene expression data and additional data about genes and cells in their respective dataframes. To create a SingleCell object sc, the following python code can be used:
        
        ```python
        import pandas as pd
        from singlecelldata import SingleCell
        
        dataset = 'biase'
        
        data_path = "data/" + dataset + '/' + dataset + "_data.csv"
        celldata_path = "data/" + dataset + '/' + dataset + "_celldata.csv"
        genedata_path = "data/" + dataset + '/' + dataset + "_genedata.csv"
        
        # Create pandas dataframes by reading data from files
        data = pd.read_csv(data_path, index_col=0)
        celldata = pd.read_csv(celldata_path, index_col=0)
        genedata = pd.read_csv(genedata_path, index_col = 0)
        
        # Create a single cell object
        sc = SingleCell(dataset, data, celldata, genedata)
        ```
        
        In the above example, a SingleCell object, sc, was ceated by passing the dataset name and the main data, the cell data and gene data as pandas dataframes. Pandas is a powerpul python library for creating data structures from a variety of sources. Pandas can open and read data from numerous differernt file types such as csv files and creating dataframes from it. This enables the user to create SingleCell objects from different data file types. For more information on pandas see <https://pandas.pydata.org/>
        
        ## Contact
        
        Contact the author on vans.edw@gmail.com to give feedback/suggestions for further improvements and to report issues.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
