Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks

Li, Ying; Lu, Yuejing; Kang, Chen; Li, Peiluan; Chen, Luonan

doi:10.34133/research.0228

Cited by 3 publications

(1 citation statement)

References 51 publications

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“…The detection of critical signals for abrupt deterioration is pivotal in the majority of complex diseases. However, the application of existing critical-state detection methods to bulk RNA-sequencing data with limited sample sizes or single-cell data is constrained by the substantial noise inherent in the data [ 36 ]. In this research, we present a robust computational method at the specific sample level, called SCNE, that is capable of constructing a sample-specific causality network for each individual and efficiently identifying critical points or pre-deterioration states associated with disease deterioration.…”

Section: Discussionmentioning

confidence: 99%

Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE)

Zhong,

Tang,

Huang

et al. 2024

Research

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Complex diseases do not always follow gradual progressions. Instead, they may experience sudden shifts known as critical states or tipping points, where a marked qualitative change occurs. Detecting such a pivotal transition or pre-deterioration state holds paramount importance due to its association with severe disease deterioration. Nevertheless, the task of pinpointing the pre-deterioration state for complex diseases remains an obstacle, especially in scenarios involving high-dimensional data with limited samples, where conventional statistical methods frequently prove inadequate. In this study, we introduce an innovative quantitative approach termed sample-specific causality network entropy (SCNE), which infers a sample-specific causality network for each individual and effectively quantifies the dynamic alterations in causal relations among molecules, thereby capturing critical points or pre-deterioration states of complex diseases. We substantiated the accuracy and efficacy of our approach via numerical simulations and by examining various real-world datasets, including single-cell data of epithelial cell deterioration (EPCD) in colorectal cancer, influenza infection data, and three different tumor cases from The Cancer Genome Atlas (TCGA) repositories. Compared to other existing six single-sample methods, our proposed approach exhibits superior performance in identifying critical signals or pre-deterioration states. Additionally, the efficacy of computational findings is underscored by analyzing the functionality of signaling biomarkers.

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Section: Discussionmentioning

confidence: 99%

Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE)

Zhong,

Tang,

Huang

et al. 2024

Research

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MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning

Wang,

2024

Preprint

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Spatially resolved transcriptomics simultaneously measure the spatial location, histology images, and transcriptional profiles of the same cells or regions in undissociated tissues. Integrative analysis of multi-modal spatially resolved data holds immense potential for understanding the mechanisms of biology. Here we present a flexible multi-modal contrastive learning for the integration of spatially resolved transcriptomics (MuCST), which jointly perform denoising, elimination of heterogeneity, and compatible feature learning. We demonstrate that MuCST robustly and accurately identifies tissue subpopulations from simulated data with various types of perturbations. In cancer-related tissues, MuCST precisely identifies tumor-associated domains, reveals gene biomarkers for tumor regions, and exposes intra-tumoral heterogeneity. We also validate that MuCST is applicable to diverse datasets generated from various platforms, such as STARmap, Visium, and omsFISH for spatial transcriptomics, and hematoxylin and eosin or fluorescence microscopy for images. Overall, MuCST not only facilitates the integration of multi-modal spatially resolved data, but also serves as pre-processing for data restoration (Python software is available athttps://github.com/xkmaxidian/MuCST).

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

Hong,

Tong,

Tang

et al. 2024

Research

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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.

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Revealing Tissue Heterogeneity and Spatial Dark Genes from Spatially Resolved Transcriptomics by Multiview Graph Networks

Cited by 3 publications

References 51 publications

Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE)

Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE)

MuCST: restoring and integrating heterogeneous morphology images and spatial transcriptomics data with contrastive learning

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

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