sincell

sincell reconstructs cell-state hierarchies from single-cell RNA sequencing (scRNA-seq) data to characterize differentiation processes and intermediate cellular states.

Key Features:

• Flexible workflow framework: Provides metrics to evaluate cell-to-cell similarities with or without dimensionality reduction and graph-building algorithms that can optionally incorporate cell clustering.
• Novel algorithms: Implements algorithms to construct cell-state hierarchies while accounting for stochastic variation inherent to single-cell data.
• Statistical support: Offers statistical methods to assess hierarchy stability and distinguish noisy from stable hierarchical structures.
• Graphical representations: Produces visual depictions of cellular hierarchies for interpretation of relationships between cell states.
• Functional association tests: Performs tests to link hierarchy-defined cell states with biological functions.

Scientific Applications:

• Cell-state hierarchy analysis: Captures hierarchical intermediate cell states from scRNA-seq to characterize transitions during differentiation.
• Developmental biology: Dissects differentiation pathways and intermediate states in developmental processes.
• Disease progression: Analyzes cellular state changes relevant to disease onset and progression.
• Regenerative medicine: Informs identification of transitional states relevant to cell reprogramming and regeneration.

Methodology:

Assessing cell-to-cell similarities using customizable metrics (with or without dimensionality reduction), constructing hierarchical graphs via graph-building algorithms that can include clustering, applying novel algorithms to account for stochastic variation in single-cell data, and using statistical methods and functional association tests to validate and interpret hierarchies.