BSDE

BSDE applies nonparametric optimal-transportation methods to identify differentially expressed genes between case and control groups in single-cell RNA sequencing datasets.

Key Features:

• Nonparametric optimal-transportation methodology: BSDE leverages optimal transport to aggregate gene expression distributions and compute distances without relying on parametric model assumptions.
• Case-Control study focus: BSDE compares aggregated distributions across individuals to identify DEGs between case and control groups rather than comparing cell types within individuals.
• Accuracy and error control: Simulation studies report that BSDE can detect a wide range of differential expressions while maintaining the type-I error rate at a prescribed level.

Scientific Applications:

• Pulmonary fibrosis single-cell RNA-seq: Identified 1,345 cell type-specific DEGs in a pulmonary fibrosis dataset, with findings corroborated by existing literature.
• Multiple sclerosis single-cell RNA-seq: Identified 1,568 DEGs in a multiple sclerosis dataset, with findings corroborated by existing literature.

Methodology:

BSDE uses optimal transportation to aggregate gene-expression distributions and compute distances between aggregated case and control distributions, implementing a nonparametric approach that avoids parametric mixed-effect modeling assumptions.