Results 11 to 20 of about 3,101 (214)

Spatial transcriptomics of a giant pilomatricoma

Journal of Cutaneous Pathology, 2023
AbstractPilomatricomas (PMs) are common benign adnexal tumors that show a predilection for the head and neck region and are characterized at the molecular level by activating mutations in the beta‐catenin (CTNNB1) gene. Giant PMs are a rare histopathological variant, according to the World Health Organization, which are defined by a size greater than 4 
Apoorva T. Patil, Daniel D. Bennett, Jin Xu, Paul Weisman, Daniel R. Matson   +4 more
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Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST

Nature Communications, 2023
Advances in spatial transcriptomics technologies have enabled the gene expression profiling of tissues while retaining spatial context. Here the authors present GraphST, a graph self-supervised contrastive learning method that learns informative and ...
Yahui Long, Kok Siong Ang, Mengwei Li, Kian Long Kelvin Chong, Raman Sethi, Chengwei Zhong, Hang Xu, Zhiwei Ong, Karishma Sachaphibulkij, Ao Chen, Li Zeng, Huazhu Fu, Min Wu, Lina Hsiu Kim Lim, Longqi Liu, Jinmiao Chen   +15 more
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Clustering spatial transcriptomics data

Bioinformatics, 2021
AbstractMotivationRecent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types.
Haotian Teng, Ye Yuan, Ziv Bar-Joseph
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Spatial Transcriptomics

The American Journal of Pathology
Dataset of spatial transcriptomics of endometrium and ...
Pierre Isnard, Benjamin D. Humphreys
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Analysis and Visualization of Spatial Transcriptomic Data [PDF]

Frontiers in Genetics, 2022
Human and animal tissues consist of heterogeneous cell types that organize and interact in highly structured manners. Bulk and single-cell sequencing technologies remove cells from their original microenvironments, resulting in a loss of spatial information.
Boxiang Liu, Yanjun Li, Liang Zhang
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Probabilistic embedding, clustering, and alignment for integrating spatial transcriptomics data with PRECAST

Nature Communications, 2023
Spatially resolved transcriptomics involves a set of emerging technologies that enable the transcriptomic profiling of tissues with the physical location of expressions. Although a variety of methods have been developed for data integration, most of them
Wei Liu, Xu Liao, Ziye Luo, Yi Yang, Mai Chan Lau, Yuling Jiao, Xingjie Shi, Weiwei Zhai, Hongkai Ji, Joe Yeong, Jin Liu   +10 more
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Spatial transcriptomics deconvolution at single-cell resolution using Redeconve

Nature Communications, 2023
Computational deconvolution with single-cell RNA sequencing data as reference is pivotal to interpreting spatial transcriptomics data, but the current methods are limited to cell-type resolution.
Zixiang Zhou, Yunshan Zhong, Zemin Zhang, Xianwen Ren   +3 more
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A unified pipeline for FISH spatial transcriptomics [PDF]

Cell Genomics, 2023
Abstract In recent years, high-throughput spatial transcriptomics has emerged as a powerful tool for investigating the spatial distribution of mRNA expression and the effects it may have on cellular function. There is a lack of standardized tools for analyzing spatial transcriptomics data, leading many groups to write
Cecilia Cisar, Nicholas Keener, Mathew Ruffalo, Benedict Paten   +3 more
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Elucidating tumor heterogeneity from spatially resolved transcriptomics data by multi-view graph collaborative learning

Nature Communications, 2022
Multi-view graph approaches could enhance the analysis of tissue heterogeneity in spatial transcriptomics. Here, the authors develop the Spatial Transcriptomics data analysis by Multiple View Collaborative-learning - stMVC - framework, and apply it to ...
Chunman Zuo, Yijian Zhang, Chen Cao, Jinwang Feng, Mingqi Jiao, Luonan Chen   +5 more
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Celloscope: a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data

Genome Biology, 2023
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells.
Agnieszka Geras, Shadi Darvish Shafighi, Kacper Domżał, Igor Filipiuk, Alicja Rączkowska, Paulina Szymczak, Hosein Toosi, Leszek Kaczmarek, Łukasz Koperski, Jens Lagergren, Dominika Nowis, Ewa Szczurek   +11 more
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