通过基于 ZINB 的图增强自动编码器灵活整合空间和表达信息,实现精确的光斑嵌入。

IF 5.2 1区 生物学 Q1 BIOLOGY
Jiacheng Leng, Jiating Yu, Ling-Yun Wu, Hongyang Chen
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引用次数: 0

摘要

区域识别是空间解决转录组学数据分析中的一个关键问题,其目的是识别组织内保持空间连续性和表达一致性的不同空间区域。在不同的数据集中,表达数据与空间信息的耦合程度往往差异很大。一些区域边界完整清晰,而另一些区域边界模糊,域内表达相似度高。然而,大多数领域识别方法没有充分整合表达信息和空间信息,以灵活地识别不同类型的领域。为了解决这些问题,我们引入了Spot2vector,这是一个计算框架,利用图增强的自编码器集成零膨胀负二项分布建模,结合图卷积网络和图注意网络来提取点的潜在嵌入。Spot2vector对空间和表达信息进行编码和整合,能够在不同平台生成的空间解析转录组学数据中有效识别具有不同空间模式的域。解码器使我们能够破译数据的分布和生成机制,同时通过去噪提高表达质量。大量的验证和分析表明,Spot2vector在提高域识别精度、有效降低数据维数、改善表达恢复和去噪以及精确捕获空间基因表达模式方面表现出色。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder.

Domain identification is a critical problem in spatially resolved transcriptomics data analysis, which aims to identify distinct spatial domains within a tissue that maintain both spatial continuity and expression consistency. The degree of coupling between expression data and spatial information in different datasets often varies significantly. Some regions have intact and clear boundaries, while others exhibit blurred boundaries with high intra-domain expression similarity. However, most domain identification methods do not adequately integrate expression and spatial information to flexibly identify different types of domains. To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. Spot2vector encodes and integrates spatial and expression information, enabling effective identification of domains with diverse spatial patterns across spatially resolved transcriptomics data generated by different platforms. The decoders enable us to decipher the distribution and generation mechanisms of data while improving expression quality through denoising. Extensive validation and analyses demonstrate that Spot2vector excels in enhancing domain identification accuracy, effectively reducing data dimensionality, improving expression recovery and denoising, and precisely capturing spatial gene expression patterns.

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来源期刊
Communications Biology
Communications Biology Medicine-Medicine (miscellaneous)
CiteScore
8.60
自引率
1.70%
发文量
1233
审稿时长
13 weeks
期刊介绍: Communications Biology is an open access journal from Nature Research publishing high-quality research, reviews and commentary in all areas of the biological sciences. Research papers published by the journal represent significant advances bringing new biological insight to a specialized area of research.
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