ESCO

ESCO simulates single-cell RNA sequencing (scRNA-seq) data while modeling gene co-expression networks (GCNs) to enable benchmarking and evaluation of methods that recover gene-gene interactions.

Key Features:

• Gene Co-expression Simulation: Imposes gene-gene dependencies using copula-based methods while preserving marginal gene expression distributions.
• Benchmarking and Assessment: Generates datasets with tunable sparsity to benchmark imputation and denoising methods for recovery of GCNs.
• Performance Evaluation: Simulation studies report that imputation improves recovery of GCNs at moderate sparsity, that ensemble imputation performs well among tested approaches, and that simple data aggregation is preferable when zero counts are pervasive.
• Implementation: Implemented as an R package and integrated with the Splatter simulation framework.

Scientific Applications:

• Method Development: Provides controlled simulated data for developing and comparing imputation and denoising algorithms for scRNA-seq.
• Data Analysis Validation: Enables validation of analytical pipelines by supplying realistic scRNA-seq datasets with known co-expression patterns.
• Biological Insight Exploration: Facilitates investigation of cellular heterogeneity and regulatory relationships by simulating explicit gene co-expression networks.

Methodology:

Applies copula theory to impose realistic gene co-expression patterns on simulated scRNA-seq expression while building on existing simulation frameworks and allowing control over dataset sparsity.