计算机科学
判别式
聚类分析
推论
人工智能
标杆管理
无监督学习
表达式(计算机科学)
模式识别(心理学)
机器学习
数据挖掘
营销
业务
程序设计语言
作者
Chao Zhang,Lin Liu,Ying Zhang,Mei Li,Shuangsang Fang,Qiang Kang,Ao Chen,Xun Xu,Yong Zhang,Yuxiang Li
出处
期刊:GigaScience
[Oxford University Press]
日期:2024-01-01
卷期号:13
被引量:2
标识
DOI:10.1093/gigascience/giae042
摘要
Abstract Background Integrative analysis of spatially resolved transcriptomics datasets empowers a deeper understanding of complex biological systems. However, integrating multiple tissue sections presents challenges for batch effect removal, particularly when the sections are measured by various technologies or collected at different times. Findings We propose spatiAlign, an unsupervised contrastive learning model that employs the expression of all measured genes and the spatial location of cells, to integrate multiple tissue sections. It enables the joint downstream analysis of multiple datasets not only in low-dimensional embeddings but also in the reconstructed full expression space. Conclusions In benchmarking analysis, spatiAlign outperforms state-of-the-art methods in learning joint and discriminative representations for tissue sections, each potentially characterized by complex batch effects or distinct biological characteristics. Furthermore, we demonstrate the benefits of spatiAlign for the integrative analysis of time-series brain sections, including spatial clustering, differential expression analysis, and particularly trajectory inference that requires a corrected gene expression matrix.
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