MetaCell

MetaCell partitions single-cell RNA sequencing (scRNA-seq) datasets into metacells to distinguish sampling effects from biological variance and enable accurate quantitative transcriptional mapping.

Key Features:

• Metacell Partitioning: Partitions scRNA-seq profiles into disjoint, homogeneous metacells that represent granular groups of profiles that could be resampled from the same cell.
• Avoidance of Data Smoothing: Preserves original single-cell measurements without data smoothing to retain quantitative gene expression signals.
• Building Blocks for Transcriptional Maps: Generates metacells that serve as fundamental units for constructing detailed quantitative transcriptional maps.
• Implementation in R/C++: Implements the underlying algorithms in R and C++.

Scientific Applications:

• Detailed Gene Expression Analysis: Enables high-precision analysis of gene expression patterns to detect subtle differences between cell types and states.
• Studying Cellular Heterogeneity: Facilitates characterization of cellular diversity in contexts such as developmental biology, cancer research, and immunology.
• Quantitative Transcriptional Mapping: Supports construction of quantitative maps for modeling cellular processes and interactions at granular resolution.

Methodology:

Partitions scRNA-seq profiles into disjoint homogeneous metacells, avoids data smoothing, and is implemented in R and C++.