Chen Hao, Nguyen Nam D, Ruffalo Matthew, Bar-Joseph Ziv
Ray and Stephanie Lane Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA.
Department of Computer Science, University of Illinois Chicago, Chicago, Illinois 60607, USA.
Genome Res. 2025 May 2;35(5):1219-1233. doi: 10.1101/gr.279631.124.
Recent efforts to generate atlas-scale single-cell data provide opportunities for joint analysis across tissues and modalities. Existing methods use cells as the reference unit, hindering downstream gene-based analysis and removing genuine biological variation. Here we present GIANT, an integration method designed for atlas-scale gene analysis across cell types and tissues. GIANT converts data sets into gene graphs and recursively embeds genes without additional alignment. Applying GIANT to two recent atlas data sets yields unified gene-embedding spaces across human tissues and data modalities. Further evaluations demonstrate GIANT's usefulness in discovering diverse gene functions and underlying gene regulation in cells from different tissues.
近期生成图谱规模单细胞数据的努力为跨组织和模态的联合分析提供了机会。现有方法以细胞作为参考单元,这阻碍了下游基于基因的分析,并消除了真正的生物学变异。在此,我们提出了GIANT,一种专为跨细胞类型和组织的图谱规模基因分析而设计的整合方法。GIANT将数据集转换为基因图谱,并在无需额外比对的情况下递归地嵌入基因。将GIANT应用于两个近期的图谱数据集,可在人类组织和数据模态中产生统一的基因嵌入空间。进一步的评估证明了GIANT在发现不同组织细胞中多样的基因功能和潜在基因调控方面的有用性。