reproduce

reproduce evaluates dimensionality reduction methods for single-cell RNA sequencing (scRNA-seq) to compare their effectiveness at preserving neighborhood structure, supporting cell clustering and lineage reconstruction, and assessing computational scalability.

Key Features:

• Systematic benchmarking: Evaluates 18 dimensionality reduction techniques across multiple datasets to enable direct comparison of methods.
• Dataset scope: Uses 30 publicly accessible scRNA-seq datasets encompassing a variety of sequencing technologies and sample sizes.
• Neighborhood preservation: Assesses how well reduced representations recover features from the original expression matrix.
• Cell clustering and lineage reconstruction: Measures accuracy and robustness of methods for clustering cells and reconstructing developmental lineages.
• Computational scalability: Records and compares the computational cost associated with each dimensionality reduction method.

Scientific Applications:

• Method selection for scRNA-seq: Guides selection of dimensionality reduction approaches tailored to specific scRNA-seq datasets and analysis goals.
• Benchmarking comparative analyses: Provides quantitative comparisons of trade-offs between neighborhood preservation, downstream analysis performance, and computational cost.
• Support for downstream inference: Informs choice of representations for downstream tasks such as cell clustering and lineage reconstruction.

Methodology:

Systematic evaluation of 18 dimensionality reduction techniques across 30 publicly accessible scRNA-seq datasets measuring neighborhood preservation, clustering and lineage reconstruction performance, and computational cost.