Metadata-Version: 2.1
Name: baredSC
Version: 1.1.1
Summary: baredSC: Bayesian Approach to Retreive Expression Distribution of Single Cell
Home-page: https://github.com/lldelisle/baredSC
Author: Lucille Lopez-Delisle, Jean-Baptiste Delisle
Author-email: lucille.delisle@epfl.ch
License: GPLv3
Project-URL: Bug Tracker, https://github.com/lldelisle/baredSC/issues
Project-URL: Documentation, https://baredsc.readthedocs.io/en/v1.1.1
Description: baredSC
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        [![PyPI Version](https://img.shields.io/pypi/v/baredsc.svg?style=plastic)](https://pypi.org/project/baredSC/) [![bioconda-badge](https://img.shields.io/conda/vn/bioconda/baredSC.svg?style=plastic)](https://anaconda.org/bioconda/baredsc) [![DOI](https://zenodo.org/badge/370966963.svg)](https://zenodo.org/badge/latestdoi/370966963)
        
        baredSC (Bayesian Approach to Retreive Expression Distribution of Single Cell) is a tool that uses a Monte-Carlo Markov Chain to estimate a confidence interval on the probability density function (PDF) of expression of one or two genes from single-cell RNA-seq data. It uses the raw counts and the total number of UMI for each cell. The PDF is approximated by a number of 1d or 2d gaussians provided by the user. The likelihood is estimated using the asumption that the raw counts follow a Poisson distribution of parameter equal to the proportion of mRNA for the gene in the cell multiplied by the total number of UMI identified in this cell.
        
        Documentation
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        Visit [our documentation](https://baredsc.readthedocs.io) to see the possible options and follow the tutorials.
        
        Citation
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        If you are using baredSC, please cite our [biorxiv paper](https://doi.org/10.1101/2021.05.26.445740).
        
Platform: UNKNOWN
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.7.*, <4
Description-Content-Type: text/markdown
