MoTRUST: adaptive semantic protection for mosaic single-cell integration and molecular recovery
Liu, Q.; Xu, Y.
Show abstract
Single-cell multi-omics reveals complementary aspects of cellular identity, but heterogeneous assays produce incomplete modality combinations. Integrating these mosaic measurements while preserving biological structure and recovering missing molecular information remains challenging. We present MoTRUST, combining neural representations, adaptive semantic protection, molecular prediction and conditional residual diffusion. MoTRUST ranked first in overall integration across 30 tasks spanning six scenarios and surpassed the strongest evaluated controls on six of eleven development recovery endpoints. Conditional residual diffusion reduced aggregate marginal prediction scores by 4.4-5.1% across three ATAC-to-RNA data sources while preserving point predictions. Together, these capabilities enable accurate mosaic integration and molecular recovery for multimodal characterization of cellular heterogeneity.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Foundation Model Attributions Reveal Shared Inflammatory Program Across Diseases 94%
- scJoint: transfer learning for data integration of atlas-scale single-cell RNA-seq and ATAC-seq 94%
- Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning 94%
Similar papers in this journal
- Single-Cell Trajectory Inference for Detecting Transient Events in Biological Processes 97%
- LCA robustly reveals subtle diversity in large-scale single-cell RNA-seq data 96%
- CSsingle: A Unified Tool for Robust Decomposition of Bulk and Spatial Transcriptomic Data Across Diverse Single-Cell References 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.