Metadata-Version: 2.1
Name: brie
Version: 2.0.6
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|
        
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        BRIE: Bayesian Regression for Isoform Estimate
        ==============================================
        
        About BRIE
        ----------
        
        Welcome to the new BRIE (>=2.0 or BRIE2), Bayesian Regression for Isoform 
        Estimate, a scalable Bayesian method to accurately identify splicing phenotypes 
        in single-cell RNA-seq experiments and quantify isoform proportions and their 
        uncertainty.
        
        BRIE2 supports the analysis of splicing processes at two molecular levels, 
        either between alternative splicing isoforms or between unspliced and spliced 
        RNAs. In either case, it returns cell-by-event or cell-by-gene matrices of PSI 
        value and its 95% confidence interval (quantification) and the statistics for 
        detecting DAS and DMG on each event or gene:
        
        1. **Differential alternative splicing (DAS):** This task is to quantify the 
           proportions of alternative splicing isoforms and to detect DAS between groups
           of cells or along with a continuous covariate, e.g., pseudotime. 
           BRIE2 is designed for two-isoform splicing events with a focus on exon 
           skipping, but in principle also applicable for mutual exclusion, 
           intron-retaining, alternative poly-A site, 3' splice site and 5' splice site.
        
        2. **Differential momentum genes (DMG):** This task is to quantify the 
           proportions of unspliced and spliced RNAs in each gene and each cell. 
           Similar to DAS, the DMG is a principled selection of genes that capture 
           heterogeneity in transcriptional kinetics between cell groups, e.g., cell 
           types, or continuous cell covariates, hence may enhance the RNA velocity 
           analyses by focusing on dynamics informed genes.
        
        
        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 the latest (often 
        development) version with the 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: https://github.com/huangyh09/brie-tutorials
        
        In short, there are two steps for running BRIE2. 
        First, obtain cell-by-gene or cell-by-event count matrices for each isoform. 
        For the exon-skipping event, you can run ``brie-count``, which will return count 
        matrices and hdf5 file for AnnData. 
        For spliced and unspliced matrices, we listed a few options in the manual_.
        
        Then you can use ``brie-quant`` to perform quantification of splicing ratio and 
        detect differential alternative splicing or differential momentum genes. 
        
        Type command line ``brie-count -h`` and ``brie-quant -h`` to see the full 
        arguments.
        
        
        .. _manual: https://brie.readthedocs.io/en/latest/quick_start.html#step1-read-counts
        
        
        References
        ==========
        
        * Yuanhua Huang and Guido Sanguinetti. `BRIE2: computational identification of 
          splicing phenotypes from single-cell transcriptomic experiments
          <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02461-5>`_.
          \ **Genome Biology**\, 2021; 22(1):251.
        
        * 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
