机器学习重塑疾病生物标志物的发现；Bonsai用树状地图重绘单细胞高维世界；RNA速率分析方向校正；单细胞让AML风险分层和用药决策更精准等

TL;DR - This research roundup covers new computational and single-cell methods for biomarker discovery, high-dimensional visualization, RNA-velocity quantification, and disease profiling. Together, they improve the reliability and clinical relevance of analyses across complex cellular datasets.

• Machine-learning models can identify multigene disease biomarkers from scRNA-seq data, but require patient-level validation, biological interpretation, and standardized workflows.
• Bonsai represents high-dimensional single-cell relationships as trees, preserving distances and developmental hierarchies more faithfully than conventional 2D embeddings such as UMAP or t-SNE.
• The tidesurf tool corrects strand-orientation errors in 10x 5′ RNA-velocity analysis that can misassign reads and reverse inferred cell-state transitions.
• Large multi-omic atlases—including a four-million-cell pancreas map—reveal disease-associated cell states, regulatory programs, and potential cellular plasticity relevant to diabetes, regeneration, and cancer.