Journal Article10.1016/j.xgen.2024.100565
Complete spatially resolved gene expression is not necessary for identifying spatial domains.
Senlin Lin,Yan Cui,Fangyuan Zhao,Zhidong Yang,Jiangning Song,Yu Zhao,Bin-Zhi Qian,Yi Zhao,Zhiyuan Yuan +8 more
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TL;DR: It is suggested that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.
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Abstract: Spatially resolved transcriptomics (SRT) technologies have revolutionized the study of tissue organization. We introduce a graph convolutional network with an attention and positive emphasis mechanism, termed BINARY, relying exclusively on binarized SRT data to accurately delineate spatial domains. BINARY outperforms existing methods across various SRT data types while using significantly less input information. Our study suggests that precise gene expression quantification may not always be essential, inspiring further exploration of the broader applications of spatially resolved binarized gene expression data.
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Citations
Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep Learning
TL;DR: Researchers propose River, an interpretable deep learning method, to identify differential spatial expression patterns across multiple tissue slices, conditions, and developmental stages, showcasing its scalability and versatility in analyzing complex tissue architectures.
References
Graph Attention Networks
Petar Veličković,Guillem Cucurull,Arantxa Casanova,Adriana Romero,Pietro Liò,Yoshua Bengio +5 more
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TL;DR: Graph Attention Networks (GATs) as mentioned in this paper leverage masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
SCANPY: large-scale single-cell gene expression data analysis
TL;DR: This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.
Three-dimensional intact-tissue sequencing of single-cell transcriptional states.
Xiao Wang,William E. Allen,Matthew Wright,Emily L. Sylwestrak,Nikolay Samusik,Sam Vesuna,Kathryn E. Evans,Cindy Zang Liu,Charu Ramakrishnan,Jia Liu,Garry P. Nolan,Felice-Alessio Bava,Karl Deisseroth,Karl Deisseroth +13 more
TL;DR: An efficient sequencing approach with hydrogel-tissue chemistry was combined to develop a multidisciplinary technology for three-dimensional (3D) intact-tissues RNA sequencing and widespread up-regulation of activity-regulated genes was observed in response to visual stimulation.
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Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2.
Robert R. Stickels,Robert R. Stickels,Evan Murray,Pawan Kumar,Jilong Li,Jamie L. Marshall,Daniela J. Di Bella,Paola Arlotta,Evan Z. Macosko,Evan Z. Macosko,Fei Chen,Fei Chen +11 more
TL;DR: Slide-seqV2, a technology that enables transcriptome-wide detection of RNAs with a spatial resolution of 10 μm, is reported, which combines improvements in library generation, bead synthesis and array indexing to reach an RNA capture efficiency ~50% that of single-cell RNA-seq data (~10-fold greater than Slide-seq).
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Exploring tissue architecture using spatial transcriptomics.
TL;DR: Spatial transcriptomics can also be used for hypothesis testing using experimental designs that compare time points or conditions, including genetic or environmental perturbations as mentioned in this paper, and is naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.
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