Besca

Besca provides a Python toolkit for processing single-cell RNA sequencing (scRNA-seq) data, including quality control, filtering, clustering, automated cell-type annotation, and deconvolution of bulk RNA-seq to estimate cell type proportions.

Key Features:

• Standardized Workflow: Provides a workflow encompassing quality control, filtering, and clustering of scRNA-seq data.
• Hierarchical Cell Signatures: Uses hierarchical cell-signature structures to identify and annotate cell types.
• Supervised Machine Learning: Applies supervised machine learning models for cell-type classification.
• Harmonized Nomenclature: Implements harmonized nomenclatures for cell annotations to standardize labels across datasets.
• Bulk RNA-seq Data Deconvolution: Deconvolutes bulk RNA-seq by leveraging gene expression profiles derived from scRNA-seq to estimate cell type proportions.
• Implementation: Implemented in Python.

Scientific Applications:

• Translational Research and Disease Biology: Supports analyses to characterize cellular heterogeneity and dynamics in translational research and disease biology.
• Tumor Tissue Analysis: Applied to diverse datasets including highly heterogeneous tumor tissues for cell-type identification and composition analysis.
• Integration of Single-cell and Bulk Transcriptomics: Enables estimation of cellular composition in bulk transcriptomics by transferring scRNA-seq-derived signatures.

Methodology:

Performs quality control, filtering, and clustering of scRNA-seq data; annotates cells using hierarchical cell signatures and supervised machine learning; and deconvolutes bulk RNA-seq by leveraging gene expression profiles derived from scRNA-seq.