Metadata-Version: 2.1
Name: fba
Version: 0.0.6
Summary: Tools for feature barcoding analyses
Home-page: https://github.com/jlduan/fba
Author: JD
License: UNKNOWN
Description: 
        [![PyPI](https://img.shields.io/pypi/v/fba?logo=pypi&style=flat-square)](https://pypi.org/project/fba/) [![PyPI - License](https://img.shields.io/pypi/l/fba?style=flat-square)](https://github.com/jlduan/fba/blob/master/LICENSE)
        
        # fba
        
        Tools for feature barcoding analysis
        
        <br>
        
        ## Installation
        
        ```shell
        $ pip install fba
        ```
        
        <br>
        
        ## Usage
        
        ```
        $ fba
        
        usage: fba [-h]  ...
        
        Tools for feature barcoding analyses
        Version: 0.0.6
        
        optional arguments:
          -h, --help        show this help message and exit
        
        functions:
        
            extract         extract cell and feature barcodes
            map             map enriched transcripts
            filter          filter extracted barcodes
            count           count feature barcodes per cell
            demultiplex     demultiplex cells based on feature abundance
            qc              quality control of feature barcoding assay
            kallisto_wrapper
                            deploy kallisto/bustools for feature barcoding
                            quantification
        ```
        
        <br>
        
        - __extract__: extract cell and feature barcodes from paired fastq files. For single cell assays, read 1 usually contains cell partitioning and UMI information, and read 2 contains feature information.
        
        - __map__: quantify enriched transcripts (through hybridization or PCR amplification) from parent single cell libraries. Read 1 contains cell partitioning and UMI information, and read 2 contains transcribed regions of enriched/targeted transcripts of interest. Bowtie2 (Langmead, B., et al. 2012) is used for read 2 alignment. The quantification (UMI deduplication) of enriched/targeted transcripts is powered by UMI-tools (Smith, T., et al. 2017).
        
        - __filter__: filter extracted cell and feature barcodes (output of `extract` or `qc`). Additional fragment filter/selection can be applied through `-cb_seq` and/or `-fb_seq`.
        
        - __count__: count UMIs per feature per cell (UMI deduplication), powered by UMI-tools (Smith, T., et al. 2017). Take the output of `extract` or `filter` as input.
        
        - __demultiplex__: demultiplex cells based on the abundance of features (matrix generated by `count` as input).
        
        - __qc__: generate diagnostic information. If `-1` is omitted, bulk mode is enabled and only read 2 will be analyzed.
        
        - __kallisto_wrapper__: deploy kallisto/bustools for feature barcoding quantification (just a wrapper) (Bray, N.L., et al. 2016).
        
        
        <br>
        
        ## Example workflow
        
        - Cell surface protein labeling
            - [CITE-Seq; 8k cord blood mononuclear cells with 13 antibodies](https://github.com/jlduan/fba/blob/master/examples/cell_surface_protein_labeling/PRJNA393315/tutorial.md)
            - [1k Human PBMCs Stained with a Panel of TotalSeq B Antibodies, Dual Indexed](https://github.com/jlduan/fba/blob/master/examples/cell_surface_protein_labeling/SC3_v3_NextGem_DI_PBMC_CSP_1K/tutorial.md)
        
        - Cell hashing
            - [Peripheral blood mononuclear cells with 8 antibodies](https://github.com/jlduan/fba/blob/master/examples/cell_hashing/PRJNA423077/tutorial.md)
        
        - CRISPR screening
            - [10k A375 Cells Transduced with (1) Non-Target and (1) Target sgRNA, Dual Indexed](https://github.com/jlduan/fba/blob/master/examples/crispr_screening/SC3_v3_NextGem_DI_CRISPR_10K/tutorial.md)
        
        - Targeted transcript enrichment
            - [Hodgkin's Lymphoma, Dissociated Tumor: Targeted, Gene Signature Panel](https://github.com/jlduan/fba/blob/master/examples/targeted_transcript_enrichment/Targeted_NGSC3_DI_HodgkinsLymphoma_GeneSignature/tutorial.md)
        
        - Bulk
            - [10k A375 Cells Transduced with (1) Non-Target and (1) Target sgRNA, Dual Indexed](https://github.com/jlduan/fba/blob/master/examples/bulk/SC3_v3_NextGem_DI_CRISPR_10K/tutorial.md)
        
        <br>
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.6
Description-Content-Type: text/markdown
