sc-MAESTRO

sc-MAESTRO integrates single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data to jointly analyze gene expression and chromatin accessibility and to model gene regulatory potential at single-cell resolution.

Key Features:

• Comprehensive Workflow: Provides end-to-end processing for scRNA-seq and scATAC-seq including pre-processing, alignment, quality control, quantification, clustering, differential analysis, and annotation.
• Integration of Multi-Omics Data: Integrates scRNA-seq and scATAC-seq datasets by modeling gene regulatory potential from single-cell chromatin accessibility profiles.
• Advanced Clustering and Annotation: Enhances integration of cell clusters between scRNA-seq and scATAC-seq and supports automatic cell-type annotation using predefined marker genes.
• Identification of Regulatory Elements: Identifies driver regulators by analyzing differential expression from scRNA-seq and differential accessibility of scATAC-seq peaks.
• Benchmarking and Evaluation: Includes benchmark codes for evaluation of scATAC-seq clustering, automatic cell-type annotation, and integration methods.

Scientific Applications:

• Cellular Heterogeneity: Characterizing diverse cell populations and subpopulations within complex tissues at single-cell resolution.
• Gene Regulation: Investigating how chromatin accessibility influences transcriptional activity and regulatory potential in single cells.
• Disease Mechanisms: Studying changes in cellular states and regulatory networks associated with diseases such as cancer.

Methodology:

Implements model-based analyses of transcriptome and regulome data, performs pre-processing, alignment, quality control, quantification, clustering, differential analysis, and annotation, uses snakemake for workflow management, and provides benchmark codes for evaluating scATAC-seq clustering, automatic cell-type annotation, and integration methods.