CCPE

CCPE estimates cell cycle pseudotime and identifies cell cycle phases from single-cell RNA-seq (scRNA-seq) data to characterize timing and progression of cellular events.

Key Features:

• Pseudotime Estimation: Maps continuous progression of cells through the cell cycle using pseudotime analysis on scRNA-seq data.
• Discriminative Helix Model: Represents the circular nature of the cell cycle with a discriminative helix model to estimate cyclic pseudotime.
• Robustness to Dropout Events: Maintains performance in the presence of scRNA-seq dropout events to support phase identification from sparse data.
• Identification of Cell Cycle Marker Genes: Identifies marker genes associated with distinct cell cycle stages for annotation of cell states.
• Performance Evaluation: Evaluated on simulated and real scRNA-seq datasets to assess accuracy and competitiveness with existing methods.

Scientific Applications:

• Cell cycle effect correction: Enables removal of cell cycle–associated variation from scRNA-seq data to reveal other biological signals.
• Temporal dynamics analysis: Characterizes timing of gene expression in developmental biology, cancer progression, and stem cell differentiation studies.
• Cell state annotation: Supports assignment of cell cycle phases and annotation of cell states via detected marker genes.

Methodology:

Discriminative helix model for circular representation of the cell cycle; pseudotime estimation of cellular progression; robustness to scRNA-seq dropout events; evaluation on simulated and real scRNA-seq datasets.