fba

Tools for single-cell feature barcoding analysis

Science Score: 67.0%

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CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 4 DOI reference(s) in README
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Academic publication links
Links to: pubmed.ncbi, ncbi.nlm.nih.gov
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (9.3%) to scientific vocabulary

Keywords

cell-hashing cell-partitioning cellplex cite-seq crispr eccite-seq feature-barcodes multi-seq phage-atac single-cell targeted-transcripts

Last synced: 6 months ago · JSON representation ·

Repository

Tools for single-cell feature barcoding analysis

Basic Info

Host: GitHub
Owner: jlduan
License: mit
Language: Python
Default Branch: master
Homepage: https://jlduan.github.io/fba/
Size: 54 MB

Statistics

Stars: 5
Watchers: 1
Forks: 3
Open Issues: 1
Releases: 10

Topics

cell-hashing cell-partitioning cellplex cite-seq crispr eccite-seq feature-barcodes multi-seq phage-atac single-cell targeted-transcripts

Created over 5 years ago · Last pushed over 1 year ago

Metadata Files

Readme License Citation

`fba`

Tools for single-cell feature barcoding analysis

Jialei Duan, Gary C Hon, FBA: feature barcoding analysis for single cell RNA-Seq, Bioinformatics, Volume 37, Issue 22, 15 November 2021, Pages 4266–4268. DOI: https://doi.org/10.1093/bioinformatics/btab375. PMID: 33999185.

What is `fba`?

fba is a flexible and streamlined toolbox for quality control, quantification, demultiplexing of various 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.

Installation

fba can be installed with pip:

shell pip install fba

Alternatively, you can install this package with conda:

shell conda install -c bioconda fba

Workflow Example

Usage

``` $ fba

usage: fba [-h] ...

Tools for single-cell feature barcoding analysis

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

```

extract: extract cell and feature barcodes from paired fastq files. For single cell assays, read 1 typically contains cell partitioning and UMI information, while 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, while read 2 contains transcribed regions of enriched/targeted transcripts of interest. BWA (Li, H. 2013) or 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).

Owner

Name: Jialei Duan
Login: jlduan
Kind: user

Twitter: jlduan
Repositories: 3
Profile: https://github.com/jlduan

知君仙骨无寒暑，千载相逢犹旦暮

Citation (CITATION.cff)

cff-version: 1.2.0
message: If you use this software, please cite the article from preferred-citation.
title: FBA
abstract: Tools for feature barcoding analysis
authors:
  - family-names: Duan
    given-names: Jialei
    orcid: "https://orcid.org/0000-0003-4086-7461"
  - family-names: Hon
    given-names: Gary
    orcid: "https://orcid.org/0000-0002-1615-0391"
version: 0.0.11
date-released: 2021-05-17
identifiers:
  - type: doi
    value: 10.5281/zenodo.4642814
    description: This DOI represents all versions of FBA
preferred-citation:
  scope: If you use this software, please cite this publication.
  authors:
    - family-names: Duan
      given-names: Jialei
    - family-names: Hon
      given-names: Gary
  type: article
  title: "FBA: feature barcoding analysis for single cell RNA-Seq"
  volume: 37
  issue: 22
  issue-date: 2021-11-15
  start: 4266
  end: 4268
  year: 2021
  journal: Bioinformatics
  doi: 10.1093/bioinformatics/btab375
license:
  - MIT
repository-code: "https://github.com/jlduan/fba"

GitHub Events

Total

Issues event: 1

Last Year

Issues event: 1

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 289
Total Committers: 1
Avg Commits per committer: 289.0
Development Distribution Score (DDS): 0.0

Top Committers

Name	Email	Commits
jlduan	j**n@u**m	289

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 2
Total pull requests: 0
Average time to close issues: 21 minutes
Average time to close pull requests: N/A
Total issue authors: 2
Total pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 1
Pull requests: 0
Average time to close issues: N/A
Average time to close pull requests: N/A
Issue authors: 1
Pull request authors: 0
Average comments per issue: 0.0
Average comments per pull request: 0
Merged pull requests: 0
Bot issues: 0
Bot pull requests: 0

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Top Authors

Issue Authors

evenlode10 (1)
anniqueclaringbould (1)

Pull Request Authors

Top Labels

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Packages

Total packages: 1
Total downloads:
- pypi 66 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 11
Total maintainers: 1

pypi.org: fba

Tools for single-cell feature barcoding analysis

Homepage: https://github.com/jlduan/fba
Documentation: https://fba.readthedocs.io/
License: MIT License Copyright (c) 2020 Jialei Duan Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Latest release: 0.0.13
published about 3 years ago

Versions: 11
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 66 Last month

Rankings

Dependent packages count: 10.0%

Forks count: 16.8%

Average: 20.7%

Dependent repos count: 21.7%

Stargazers count: 23.1%

Downloads: 31.9%

Maintainers (1)

jduan

Last synced: 6 months ago

Dependencies

docs/requirements.txt pypi

Sphinx >=4.2.0
furo ==2021.10.9
myst-parser >=0.15.2
sphinx-copybutton >=0.4.0
sphinx_rtd_theme >=1.0.0
sphinxext-opengraph *

requirements.txt pypi

dnaio *
hdbscan *
matplotlib >=3.3
numpy *
pandas *
polyleven >=0.5
pyclustering *
pysam >=0.14.0
regex *
scikit-learn *
scipy *
seaborn *
statsmodels *
umap-learn *
umi_tools >=1.0.0

setup.py pypi

i.rstrip *

.github/workflows/ci.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite
codecov/codecov-action v1 composite

.github/workflows/codecov.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite
codecov/codecov-action v1 composite

.github/workflows/fba.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/pypi.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/pypi_test.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

environment.yml pypi

pyproject.toml pypi

fba

Science Score: 67.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

fba

What is fba?

Installation

Workflow Example

Usage

Owner

Citation (CITATION.cff)

GitHub Events

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pypi.org: fba

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Dependencies

`fba`

What is `fba`?