Metadata-Version: 2.1
Name: scEasyMode
Version: 1.0.1
Summary: Wrappers for automating single cell workflows in python
Home-page: https://github.com/johnnyUCSF/scEasyMode
Author: Johnny Yu
Author-email: johnny.yu@ucsf.edu
License: UNKNOWN
Description: # ScEasyMode: Wrappers for automating single cell workflows in Python
        
        ## Features
        - Multiseq correction in python using a within-barcode zscore correction
        - Plotting for stacked barplots in your dataset
        - Mouse cell filtering/separation from mixed dataset
        - Scanpy wrapper that simplifies the workflow
        
        ## Installation
        <b> Install using Pip </b>
        ```sh
        pip3 install scEasyMode
        ```
        
        <b> Install using a Conda Environment </b>
        - You may also use Conda to start an environment with ScEasyMode installed inside it. You can install conda from [here.](https://docs.anaconda.com/anaconda/install/)
        - Firstly, clone the repository and create the environment as shown below. Then, activate the environment.
        
        ```sh
        git clone https://github.com/johnnyUCSF/scEasyMode
        cd scEasyMode
        conda env create -f environment.yml
        conda activate sceasymode_env
        ```
        
        - Now start your Jupyter Notebook or Python shell inside the conda environment
        
        ## Usage
        ### Load the modules
        
        ```python
        from scEasyMode import mousefilter
        from scEasyMode import clusterplot
        from scEasyMode import pymulti
        from scEasyMode import sceasy
        ```
        
        ### Demultiplex your samples
        
        ```python
        import pandas as pd
        from scEasyMode import pymulti
        
        # Define parameters
        
        len_10x=16 # Number of bases in cell barcodes
        len_umi=12 # Length of UMI
        len_multi=15, # Number of bases in the HTO barcodes / HashTag O
        
        fastq_r1 = 'path/to/file'
        fastq_r2 = 'path/to/file'
        sample_name = 'test_demultiplexing'
        
        cell_BC_file = 'path/to/cell_barcodes' # Counts Matrix after alignment and pre-processing
        cell_bcs = pd.read_csv(cell_BC_file, sep='\t', header=None)[0].tolist()
        
        multi_BC_file = 'path/to/barcodes' # Barcodes TSV file from 10x or Illumina
        bcsmulti = pd.read_csv(multi_BC_file,sep=',',index_col=1,header=None)
        bcsmulti.columns = ['multi']
        bcsmulti = bcsmulti['multi'].tolist()
        
        pymulti.pymulti(fastq_r1, fastq_r2, bcsmulti=bcsmulti, bcs10x=cell_bcs,
                        len_10x=len_10x, len_multi=len_multi, len_umi=len_umi, split=True,
                        hamming=True, median_only=True, sampname= sample_name,  filter_unmapped_reads=True)
        
        # This function will output multiple graphs
        # It will also store a matrix of the assigned barcodes in the 'pymulti' directory inside the working directory.
        # Note that some reads are unmapped. If you want to retain them, you can do so by specifying filter_unmapped_reads=False.
        ```
        
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
