Work

FBA: feature barcoding analysis for single cell RNA-Seq

Year: 2021

Type: article

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.

Source: Bioinformatics

Authors: Jialei Duan, Gary C Hon

Institution: The University of Texas Southwestern Medical Center

Language: English

Topic: Single-cell and spatial transcriptomics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: Burroughs Wellcome Fund, Welch Foundation, Cancer Prevention and Research Institute of Texas, National Institutes of Health, National Institute of General Medical Sciences

Awards: G3446404522, G4395170721, G4967353114, Comprehensive Evaluation of Functional Enhancers in Breast Cancer Risk Susceptibility Loci, G9000228567

OpenAlex ID: W3161655331

FWCI: 0.47

Cites: 18

Cited by: 17

Related to: 10