Metadata-Version: 2.1
Name: brie
Version: 2.0.3
Summary: BRIE: Bayesian regression for isoform estimate
Home-page: https://brie.readthedocs.io
Author: Yuanhua Huang
Author-email: yuanhua@hku.hk
License: Apache-2.0
Description: |PyPI| |Docs| |Build Status|
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/brie.svg
            :target: https://pypi.org/project/brie
        .. |Docs| image:: https://readthedocs.org/projects/brie/badge/?version=latest
           :target: https://brie.readthedocs.io
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        BRIE: Bayesian Regression for Isoform Estimate
        ==============================================
        
        About BRIE
        ----------
        
        Welcome to the new BRIE2 (Bayesian regression for isoform estimate, v2), a 
        scalable Bayesian method to robustly identify splicing phenotypes in single 
        cells RNA-seq designs and accurately estimate isoform proportions and its 
        uncertainty.
        
        BRIE2 supports isoform quantification for different needs:
        
        1. cell features: informative prior is learned from shared cell processes. It 
           also allows to effectively detect splicing phenotypes by using Evidence Lower
           Bound gain, an approximate of Bayes factor.
           
        2. gene features: informative prior is learned from shared gene regulatory 
           features, e.g., sequences and RNA protein binding
        
        3. no feature: use zero-mean logit-normal as uninformative prior, namely
           merely data deriven
           
        Note, `BRIE1 CLI`_ is still available in this version but changed to `brie1` 
        and `brie1-diff`.
        
        .. _BRIE1 CLI: https://brie.readthedocs.io/en/latest/brie1.html
        
        Installation
        ============
        
        BRIE2 is available through PyPI_. To install, type the following command 
        line, and add ``-U`` for upgrading:
        
        .. code-block:: bash
        
          pip install -U brie
        
        Alternatively, you can install from this GitHub repository for latest (often 
        development) version by following command line
        
        .. code-block:: bash
        
          pip install -U git+https://github.com/huangyh09/brie
        
        In either case, add ``--user`` if you don't have the write permission for your 
        Python environment.
        
        For more instructions, see the installation_ manual.
        
        .. _PyPI: https://pypi.org/project/brie
        .. _installation: https://brie.readthedocs.io/en/latest/install.html
        
        
        Manual and examples
        ===================
        
        The full manual is at https://brie.readthedocs.io 
        More examples and tutorials are coming soon.
        
        In brief, you need to run `brie-count` first, which will return a count matrix
        and hdf5 file for AnnData. Then you can use `brie-quant` to perform 
        quantification in different settings. Type command line ``brie-count -h`` and 
        ``brie-quant -h`` to see the full arguments.
        
        
        References
        ==========
        
        Yuanhua Huang and Guido Sanguinetti. `BRIE: transcriptome-wide splicing 
        quantification in single cells 
        <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1248-5>`_. 
        \ **Genome Biology**\, 2017; 18(1):123.
        
Keywords: RNA splicing,Bayesian regression,single cell RNA-seq,variantional inference
Platform: UNKNOWN
Requires-Python: >=3.5
Provides-Extra: docs
