uSORT

uSORT infers intrinsic cell progression paths from single-cell RNA-seq (scRNA-seq) data to characterize cellular dynamics and differentiation.

Key Features:

• Data pre-processing: Performs quality control and preprocessing of raw single-cell RNA-seq data to prepare inputs for downstream analysis.
• Preliminary PCA gene selection: Uses Principal Component Analysis (PCA) to identify a subset of genes that capture the most variance in the dataset.
• Preliminary cell ordering: Establishes an initial ordering of cells to propose candidate progression trajectories.
• Feature selection: Selects genes and markers most relevant to the inferred biological processes to refine trajectory signals.
• Refined cell ordering: Produces an improved cell ordering to generate a more accurate depiction of cell progression paths.
• Post-analysis interpretation and visualization: Generates visualizations and interpretation outputs to represent inferred cellular trajectories and selected features.

Scientific Applications:

• Cell differentiation and development: Characterizes progression paths and state transitions from scRNA-seq data to study differentiation and development.
• Regulatory gene and pathway identification: Supports identification of key genes and pathways associated with inferred progression trajectories.
• Translational research in disease and regeneration: Applies inferred cellular dynamics to research areas such as developmental biology, oncology, and regenerative medicine.

Methodology:

Implements data pre-processing, PCA-based gene selection, preliminary and refined cell ordering, feature selection, and visualization within R using the Bioconductor framework.