FBA: feature barcoding analysis for single cell RNA-Seq

Jialei Duan, Gary C. Hon

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)4266-4268
Number of pages3
JournalBioinformatics
Volume37
Issue number22
DOIs
StatePublished - Nov 15 2021

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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