scFlow

scFlow performs automated, scalable, and reproducible analyses of single-cell RNA sequencing (scRNA-seq) and single-nuclei RNA sequencing (snRNA-seq) data.

Key Features:

• Integration with nf-core/scflow Nextflow pipeline: Serves as the analytical backbone for the nf-core/scflow Nextflow pipeline within the nf-core framework.
• Higher-level abstraction: Provides a higher-level abstraction over popular single-cell analysis packages within the R ecosystem.
• Modular and extensible design: Implements a modular and extensible architecture that allows customization and extension of analysis components.
• Containerization: Uses containerization technologies to encapsulate software environments for consistent execution.
• Cloud computing: Supports deployment on cloud computing platforms to scale analyses.
• Scalability: Scales to very large datasets, including datasets exceeding a million cells.
• Reproducibility: Enables reproducible analyses across different computational environments.
• Sparse-matrix quality control: Performs quality control on sparse-count matrices for single-cell and single-nuclei data.
• Normalization: Includes normalization methods for scRNA-seq and snRNA-seq data.
• Dimensionality reduction: Provides dimensionality-reduction approaches for visualization and downstream analysis.
• Clustering: Supports clustering for cell-type or subpopulation identification.
• Differential expression analysis: Performs differential expression analysis for contrasts between conditions or clusters.

Scientific Applications:

• Large-scale single-cell analysis: Analysis of large-scale scRNA-seq and snRNA-seq datasets, including datasets exceeding a million cells.
• Quality control and preprocessing: Quality control and preprocessing of sparse-count matrices for downstream analyses.
• Cell-type and state identification: Normalization, dimensionality reduction, and clustering to identify cell types and cellular states.
• Differential expression and biomarker discovery: Differential expression analyses to identify markers and generate biological insights.
• Biological and disease studies: Application across biological contexts from basic cellular biology to complex disease modeling.

Methodology:

The pipeline begins with sparse-matrix quality control and progresses through multiple stages of data processing and analysis, integrates with the nf-core/scflow Nextflow pipeline, uses containerization to maintain consistent environments, and supports deployment on cloud computing platforms for scalability.