Millard Nghia, Chen Jonathan H, Palshikar Mukta G, Pelka Karin, Spurrell Maxwell, Price Colles, He Jiang, Hacohen Nir, Raychaudhuri Soumya, Korsunsky Ilya
Division of Rheumatology, Inflammation and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
Genome Biol. 2025 Feb 25;26(1):36. doi: 10.1186/s13059-025-03479-9.
Spatial transcriptomics facilitates gene expression analysis of cells in their spatial anatomical context. Batch effects hinder visualization of gene spatial patterns across samples. We present the Crescendo algorithm to correct for batch effects at the gene expression level and enable accurate visualization of gene expression patterns across multiple samples. We show Crescendo's utility and scalability across three datasets ranging from 170,000 to 7 million single cells across spatial and single-cell RNA sequencing technologies. By correcting for batch effects, Crescendo enhances spatial transcriptomics analyses to detect gene colocalization and ligand-receptor interactions and enables cross-technology information transfer.
空间转录组学有助于在细胞的空间解剖背景下进行基因表达分析。批次效应阻碍了跨样本基因空间模式的可视化。我们提出了Crescendo算法，以在基因表达水平上校正批次效应，并能够准确可视化多个样本中的基因表达模式。我们展示了Crescendo在三个数据集上的实用性和可扩展性，这些数据集涵盖了从170,000到700万个单细胞，涉及空间和单细胞RNA测序技术。通过校正批次效应，Crescendo增强了空间转录组学分析，以检测基因共定位和配体-受体相互作用，并实现跨技术信息传递。
