Spatial transcriptomics: a new frontier in cancer research

The rapid development of spatial transcriptomics has underscored the importance of identifying spatially variable genes. As a fundamental task in spatial transcriptomic data analysis, spatially variable gene identification has been extensively studied. However, the lack of comprehensive benchmark makes it difficult to validate the effectiveness of various algorithms scattered across a large number of studies with real-world datasets. In response, this article proposes a benchmark framework to evaluate algorithms for identifying spatially variable genes through the analysis of synthesized and real-world datasets, aiming to identify the best algorithms and their corresponding application scenarios. This framework can assist medical and life scientists in selecting suitable algorithms for their research, while also aid bioinformatics scientists in developing more powerful and efficient computational methods in spatial transcriptomic research.

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Interpretable high-resolution dimension reduction of spatial transcriptomics data by SpaHDmap

Tang,

Chen,

Qian

et al. 2024

Preprint

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Spatial transcriptomics (ST) technologies have transformed our ability to study tissue architecture by capturing gene expression profiles along with their spatial context. However, the high-dimensional ST data often have limited spatial resolution and exhibit considerable noise and sparsity, thus posing significant challenges for deciphering subtle spatial patterns. To address these challenges, we introduce DeepFuseNMF, a novel multi-modal dimensionality reduction framework that enhances spatial resolution by integrating low-resolution ST data with high-resolution histology images. DeepFuseNMF incorporates nonnegative matrix factorization into a neural network architecture for interpretable high-resolution embedding learning and spatial domain detection. Furthermore, DeepFuseNMF seamlessly handles multiple samples simultaneously and is compatible with various types of histology images. Extensive evaluations using synthetic datasets and real ST datasets from various technologies and tissue types demonstrate DeepFuseNMF’s ability to produce highly interpretable high-resolution embeddings and to detect refined spatial structures. DeepFuseNMF represents a powerful multi-modal data integration approach to fully leverage the rich information in ST data and associated high-resolution image data, paving the way for understanding the organization and function of complex tissue structures.

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Spatial transcriptomics: a new frontier in cancer research

Cited by 4 publications

References 189 publications

Nanopore sequencing: flourishing in its teenage years

Nanopore sequencing: flourishing in its teenage years

Benchmarking algorithms for spatially variable gene identification in spatial transcriptomics

Interpretable high-resolution dimension reduction of spatial transcriptomics data by SpaHDmap

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