FBA

FBA implements Python-based analysis of feature barcoding assays in single-cell RNA sequencing (scRNA-Seq) to quantify and process cell surface proteins, genomic perturbations, and unique sequence barcodes.

Key Features:

• Quality Control: Provides quality-control measures to assess integrity and reliability of feature barcoding assay data.
• Quantification: Performs accurate quantification of molecular and cellular features from feature barcoding reads.
• Demultiplexing: Distinguishes and assigns unique sequence barcodes to their originating cells or features.
• Multiplet Detection: Identifies multiplets where multiple cells are captured in a single droplet or well.
• Clustering and Visualization: Supports clustering of feature-derived measurements and generates visualizations to explore cell populations.

Scientific Applications:

• Cellular profiling and heterogeneity: Integrates protein expression and barcode-derived perturbation information with scRNA-Seq to resolve cellular heterogeneity.
• Developmental biology: Enables single-cell resolution analysis of protein and perturbation features during development.
• Cancer research: Facilitates profiling of tumor cell populations by combining transcriptomic, protein, and perturbation barcode data.
• Immunology: Supports characterization of immune cell subsets via combined feature barcoding and scRNA-Seq measurements.

Methodology:

Implemented in Python as a modular workflow that performs quality control, quantification, demultiplexing, multiplet detection, clustering, and visualization of feature barcoding scRNA-Seq data.