FICTURE : Scalable segmentation-free analysis of submicron resolution spatial transcriptomics
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.
Medienart: |
E-Artikel |
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Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - year:2023 |
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Enthalten in: |
bioRxiv : the preprint server for biology - (2023) vom: 07. Nov. |
Sprache: |
Englisch |
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Beteiligte Personen: |
Si, Yichen [VerfasserIn] |
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Anmerkungen: |
Date Revised 10.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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doi: |
10.1101/2023.11.04.565621 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM364533374 |
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520 | |a Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST | ||
650 | 4 | |a Preprint | |
700 | 1 | |a Lee, ChangHee |e verfasserin |4 aut | |
700 | 1 | |a Hwang, Yongha |e verfasserin |4 aut | |
700 | 1 | |a Yun, Jeong H |e verfasserin |4 aut | |
700 | 1 | |a Cheng, Weiqiu |e verfasserin |4 aut | |
700 | 1 | |a Cho, Chun-Seok |e verfasserin |4 aut | |
700 | 1 | |a Quiros, Miguel |e verfasserin |4 aut | |
700 | 1 | |a Nusrat, Asma |e verfasserin |4 aut | |
700 | 1 | |a Zhang, Weizhou |e verfasserin |4 aut | |
700 | 1 | |a Jun, Goo |e verfasserin |4 aut | |
700 | 1 | |a Zöllner, Sebastian |e verfasserin |4 aut | |
700 | 1 | |a Lee, Jun Hee |e verfasserin |4 aut | |
700 | 1 | |a Kang, Hyun Min |e verfasserin |4 aut | |
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