SC3

SC3 performs consensus-based unsupervised clustering of single-cell RNA-seq data to identify cell types and subpopulations from global transcriptome profiles.

Key Features:

• Unsupervised Clustering: Performs unsupervised clustering of single-cell RNA-seq data to group cells without prior labels.
• Consensus Approach: Integrates multiple clustering solutions via a consensus methodology to combine results from different clustering perspectives.
• High Accuracy and Robustness: Produces robust clustering outcomes with improved accuracy through consensus integration of multiple solutions.
• Identification of Subclones: Enables identification of subclones in neoplastic, including patient-derived, cells by resolving subtle transcriptomic differences.

Scientific Applications:

• Transcriptome Profiling: Quantitative characterization of cell types and states based on global transcriptome profiles from single-cell RNA-seq data.
• Cancer Research: Analysis of tumor heterogeneity through identification of subclones in neoplastic cells to support studies of cancer biology.

Methodology:

Performs unsupervised clustering and applies a consensus clustering framework that integrates multiple clustering results to produce a final solution with enhanced accuracy and robustness for single-cell RNA-seq transcriptome data.