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FBA: feature barcoding analysis for single cell RNA-Seq.

ABSTRACT:

Motivation

Single cell RNA-Seq (scRNA-Seq) has broadened our understanding of cellular heterogeneity and provided valuable insights into cellular functions. Recent experimental strategies extend scRNA-Seq readouts to include additional features, including cell surface proteins and genomic perturbations. These 'feature barcoding' strategies rely on converting molecular and cellular features to unique sequence barcodes, which are then detected with the transcriptome.

Results

Here, we introduce FBA, a flexible and streamlined package to perform quality control, quantification, demultiplexing, multiplet detection, clustering and visualization of feature barcoding assays.

Availabilityand implementation

FBA is available on PyPi at https://pypi.org/project/fba and on GitHub at https://github.com/jlduan/fba.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Duan J 

PROVIDER: S-EPMC9502162 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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FBA: feature barcoding analysis for single cell RNA-Seq.

Duan Jialei J   Hon Gary C GC  

Bioinformatics (Oxford, England) 20211101 22

<h4>Motivation</h4>Single cell RNA-Seq (scRNA-Seq) has broadened our understanding of cellular heterogeneity and provided valuable insights into cellular functions. Recent experimental strategies extend scRNA-Seq readouts to include additional features, including cell surface proteins and genomic perturbations. These 'feature barcoding' strategies rely on converting molecular and cellular features to unique sequence barcodes, which are then detected with the transcriptome.<h4>Results</h4>Here, w  ...[more]

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