recipe fba

Tools for single-cell feature barcoding analysis. Citation: Duan, et al (2021) <doi:10.1093/bioinformatics/btab375>.







'fba is a flexible and streamlined toolbox for quality control, quantification, demultiplexing of various single-cell feature barcoding assays. It can be applied to customized feature barcoding specifications, including different CRISPR constructs or targeted enriched transcripts. fba allows users to customize a wide range of parameters for the quantification and demultiplexing process. fba also has a user-friendly quality control module, which is helpful in troubleshooting feature barcoding experiments.'

package fba

(downloads) docker_fba



depends dnaio:


depends hdbscan:


depends matplotlib-base:


depends numpy:


depends pandas:


depends polyleven:


depends pyclustering:


depends pysam:


depends python:


depends regex:

depends scikit-learn:


depends scipy:


depends seaborn:


depends statsmodels:


depends umap-learn:

depends umi_tools:




You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install fba

and update with::

   mamba update fba

To create a new environment, run:

mamba create --name myenvname fba

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

(see `fba/tags`_ for valid values for ``<tag>``)

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