scAWMV

scAWMV integrates parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data using adaptively weighted multi-view learning to produce unified low-dimensional representations that mitigate sparsity and noise for joint transcriptomic and epigenomic analysis.

Key Features:

• Adaptive Weighting: Employs an adaptive weighting mechanism to account for varying importance of scRNA-seq and scATAC-seq modalities during integration.
• Multi-View Learning: Leverages multi-view learning to capture both distinct characteristics and shared biological connections between transcriptomic and epigenomic profiles.
• Unsupervised Dimensionality Reduction: Generates biologically meaningful low-dimensional representations of both scRNA-seq and scATAC-seq data via unsupervised learning.
• Robustness to Sparsity and Noise: Specifically addresses the inherent sparsity and noise of single-cell multi-omics datasets to improve integration reliability.

Scientific Applications:

• Cellular Heterogeneity Analysis: Enables joint analysis of scRNA-seq and scATAC-seq from the same cells to reveal detailed cellular heterogeneity at single-cell resolution.
• Multi-Omics Data Integration: Facilitates integration of transcriptomic and epigenomic data to support studies of gene regulation and cellular function.

Methodology:

Adaptive weighting is used to assess modality importance, multi-view learning captures distinct and shared signals, and unsupervised dimensionality reduction produces unified low-dimensional representations from scRNA-seq and scATAC-seq data.