eMCI: An Explainable Multimodal Correlation Integration Model for Unveiling Spatial Transcriptomics and Intercellular Signaling.

IF 11 1区 综合性期刊 Q1 Multidisciplinary
Research Pub Date : 2024-11-01 eCollection Date: 2024-01-01 DOI:10.34133/research.0522
Renhao Hong, Yuyan Tong, Hui Tang, Tao Zeng, Rui Liu
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引用次数: 0

Abstract

Current integration methods for single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data are typically designed for specific tasks, such as deconvolution of cell types or spatial distribution prediction of RNA transcripts. These methods usually only offer a partial analysis of ST data, neglecting the complex relationship between spatial expression patterns underlying cell-type specificity and intercellular cross-talk. Here, we present eMCI, an explainable multimodal correlation integration model based on deep neural network framework. eMCI leverages the fusion of scRNA-seq and ST data using different spot-cell correlations to integrate multiple synthetic analysis tasks of ST data at cellular level. First, eMCI can achieve better or comparable accuracy in cell-type classification and deconvolution according to wide evaluations and comparisons with state-of-the-art methods on both simulated and real ST datasets. Second, eMCI can identify key components across spatial domains responsible for different cell types and elucidate the spatial expression patterns underlying cell-type specificity and intercellular communication, by employing an attribution algorithm to dissect the visual input. Especially, eMCI has been applied to 3 cross-species datasets, including zebrafish melanomas, soybean nodule maturation, and human embryonic lung, which accurately and efficiently estimate per-spot cell composition and infer proximal and distal cellular interactions within the spatial and temporal context. In summary, eMCI serves as an integrative analytical framework to better resolve the spatial transcriptome based on existing single-cell datasets and elucidate proximal and distal intercellular signal transduction mechanisms over spatial domains without requirement of biological prior reference. This approach is expected to facilitate the discovery of spatial expression patterns of potential biomolecules with cell type and cell-cell communication specificity.

eMCI:用于揭示空间转录组学和细胞间信号转导的可解释多模态相关整合模型
目前的单细胞 RNA 测序(scRNA-seq)数据和空间转录组学(ST)数据整合方法通常是针对特定任务而设计的,如细胞类型的解卷积或 RNA 转录本的空间分布预测。这些方法通常只能对空间转录组学数据进行部分分析,而忽略了细胞类型特异性和细胞间交叉作用所依赖的空间表达模式之间的复杂关系。在此,我们提出了基于深度神经网络框架的可解释多模态相关性整合模型eMCI。eMCI利用不同的点-细胞相关性融合scRNA-seq和ST数据,在细胞水平上整合ST数据的多种合成分析任务。首先,根据在模拟和真实 ST 数据集上与最先进方法的广泛评估和比较,eMCI 在细胞类型分类和解卷积方面能达到更好或相当的准确性。其次,eMCI 通过采用归因算法来剖析视觉输入,可识别不同空间域中负责不同细胞类型的关键成分,并阐明细胞类型特异性和细胞间通信的空间表达模式。特别是,eMCI 已应用于 3 个跨物种数据集,包括斑马鱼黑色素瘤、大豆结节成熟和人类胚胎肺,准确有效地估计了每个点的细胞组成,并推断了空间和时间背景下的近端和远端细胞相互作用。总之,eMCI 作为一种综合分析框架,可在现有单细胞数据集的基础上更好地解析空间转录组,并在无需生物先验参照的情况下阐明空间域的近端和远端细胞间信号转导机制。这种方法有望促进发现具有细胞类型和细胞间通讯特异性的潜在生物分子的空间表达模式。
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来源期刊
Research
Research Multidisciplinary-Multidisciplinary
CiteScore
13.40
自引率
3.60%
发文量
0
审稿时长
14 weeks
期刊介绍: Research serves as a global platform for academic exchange, collaboration, and technological advancements. This journal welcomes high-quality research contributions from any domain, with open arms to authors from around the globe. Comprising fundamental research in the life and physical sciences, Research also highlights significant findings and issues in engineering and applied science. The journal proudly features original research articles, reviews, perspectives, and editorials, fostering a diverse and dynamic scholarly environment.
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