scStateDynamics

scStateDynamics infers tumor cell state dynamics and identifies common drug effects by modeling single-cell level gene expression changes.

Key Features:

• Dynamic inference from static data: Catenates static single-cell snapshots to characterize dynamic drug responses.
• Subcluster identification: Identifies subtle cell subclusters with different drug responses beyond static transcriptome similarity.
• Mechanism disentanglement: Disentangles drug action mechanisms from cell-level expression changes.
• Validation: Validated on both simulated and lineage tracing data.

Scientific Applications:

• Tumor heterogeneity analysis: Analyzes tumor cell heterogeneity and plasticity to overcome drug resistance.
• Drug response characterization: Characterizes dynamic drug responses in real tumor drug treatment datasets.

Methodology:

scStateDynamics models single-cell level gene expression changes to infer cell state dynamics.