constclust

constclust identifies robust clusters in single-cell RNA sequencing (scRNA-seq) data by performing meta-clustering across multiple parameter settings to detect consistent cell-type partitions.

Key Features:

• Meta-clustering: Reconciles clustering results across multiple parameter settings to identify clusters that consistently appear.
• Parameter robustness: Detects locally robust clusters by examining solutions over a range of parameters rather than relying on a single parameter choice.
• Multi-level cellular identity: Allows cells to be associated with multiple partitions to capture discrete groups at different levels of cellular identity.
• Computational efficiency: Reduces computational resource requirements relative to conventional consensus clustering approaches.

Scientific Applications:

• Cell type identification: Identifies reproducible cell-type partitions from scRNA-seq datasets.
• Cellular heterogeneity studies: Characterizes heterogeneous populations by finding stable subgroups across parameter spaces.
• Developmental biology: Resolves multi-level cellular identities relevant to developmental trajectories.
• Cancer research: Detects robust tumor and microenvironment cell subpopulations in single-cell transcriptomic analyses.

Methodology:

Integrates clustering solutions derived under varying parameters using a meta-clustering strategy to pinpoint regions of parameter space where consistent clusters emerge and to associate cells with multiple partitions.