北京石油化工学院郭前进研究组开发出新方法KanCell，可通过整合单细胞和空间转录组学解析生物组织中的细胞异质性。这一研究成果于2024年11月21日在线发表在国际学术期刊《遗传学报》上。

研究人员表示，KanCell是基于Kolmogorov-Arnold网络（KAN）的深度学习模型，旨在通过整合单细胞RNA测序（scRNA-seq）和空间转录组学（ST）数据来增强细胞异质性分析。ST技术提供了对组织背景下基因表达的见解，并揭示了细胞相互作用和微环境。为了充分利用这种潜力，有效的计算模型至关重要。

研究人员在模拟和真实数据集上评估了KanCell，这些数据集来自STARmap、Slide-seq、Visium和空间转录组学等技术。结果表明，KanCell在PCC、SSIM、COSSIM、RMSE、JSD、ARS和ROC等指标上优于现有方法，在不同的单元数和背景噪声下具有最佳性能。

在人类淋巴结、心脏、黑色素瘤、乳腺癌、背外侧前额叶皮层和小鼠胚胎大脑上的实际应用证实了它的可靠性。与传统方法相比，KanCell有效捕获非线性关系，并通过KAN优化计算效率，为ST提供准确高效的工具。

通过提高数据准确性和解析细胞类型组成，KanCell能够揭示细胞异质性，阐明疾病微环境，确定治疗靶点，并解决复杂的生物学挑战。

附：英文原文

Title: KanCell: dissecting cellular heterogeneity in biological tissues through integrated single-cell and spatial transcriptomics

Author: Qianjin Guo

Issue&Volume: 2024/11/21

Abstract: KanCell is a deep learning model based on Kolmogorov-Arnold networks (KAN) designed to enhance cellular heterogeneity analysis by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data. ST technologies provide insights into gene expression within tissue context, revealing cellular interactions and microenvironments. To fully leverage this potential, effective computational models are crucial. We evaluate KanCell on both simulated and real datasets from technologies such as STARmap, Slide-seq, Visium, and Spatial Transcriptomics. Our results demonstrate that KanCell outperforms existing methods across metrics like PCC, SSIM, COSSIM, RMSE, JSD, ARS, and ROC, with robust performance under varying cell numbers and background noise. Real-world applications on human lymph nodes, hearts, melanoma, breast cancer, dorsolateral prefrontal cortex, and mouse embryo brains confirmed its reliability. Compared to traditional approaches, KanCell effectively captures non-linear relationships and optimizes computational efficiency through KAN, providing an accurate and efficient tool for ST. By improving data accuracy and resolving cell type composition, KanCell reveals cellular heterogeneity, clarifies disease microenvironments, and identifies therapeutic targets, addressing complex biological challenges.

DOI: 10.1016/j.jgg.2024.11.009

Source: https://www.sciencedirect.com/science/article/abs/pii/S1673852724003102