SC1CC

SC1CC clusters and orders single-cell RNA-Seq (scRNA-Seq) transcriptional profiles by cell cycle phase to mitigate cell cycle–related variation in gene expression and improve downstream biological interpretation.

Key Features:

• Cell cycle-based clustering and ordering: Clusters and orders single-cell transcriptional profiles according to progression through cell cycle phases.
• Gene Smoothness Score (GSS): Computes the GSS, a quantitative metric to assess the correctness and smoothness of cell ordering by cell cycle phase.
• Mitigation of cell cycle confounding: Reduces interference from cell cycle variation to improve cell type identification and functional analyses in scRNA-Seq data.
• Transcriptional-profile analysis: Applies computational analysis of single-cell transcriptional profiles to account for cell cycle effects.

Scientific Applications:

• Cell type identification: Improves separation of cell types by reducing cell cycle-driven expression variation in scRNA-Seq datasets.
• Functional analysis: Enables more accurate functional and pathway analyses by accounting for cell cycle phase differences.
• Developmental biology: Supports analysis of cellular heterogeneity and dynamics during development where cell cycle state is relevant.
• Cancer research: Facilitates investigation of proliferation-related transcriptional programs and tumor heterogeneity.
• Stem cell studies: Assists characterization of stem cell states and differentiation trajectories influenced by cell cycle.

Methodology:

Performs clustering and ordering of single-cell transcriptional profiles and computes the Gene Smoothness Score (GSS) as a quantitative measure to assess and account for cell cycle phase-related variation.