SPsimSeq

SPsimSeq constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk) using a specially designed exponential family for density estimation, and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes.

Key Features:

• Density Estimation: Uses a specially designed exponential family for density estimation to construct the distribution of gene expression levels from real RNA sequencing data.
• Dependence Retention: Simulates new datasets from estimated marginal distributions using Gaussian-copulas to retain the dependence between genes.
• Flexible Simulation Design: Allows simulation of multiple groups and batches with any required sample size and library size.
• Data Type Compatibility: Supports both single-cell and bulk RNA sequencing data.

Scientific Applications:

• RNA Sequencing Data Simulation: Generates simulated RNA sequencing datasets that preserve gene expression distributions and inter-gene dependence structures for method evaluation and benchmarking.
• Study Design Planning: Enables researchers to simulate multiple groups and batches with specified sample sizes and library sizes to assess statistical power and experimental design.

Methodology:

SPsimSeq uses a specially designed exponential family for density estimation to construct the distribution of gene expression levels from a given real RNA sequencing data, then simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes.