<i>FICTURE:</i>Scalable segmentation-free analysis of submicron resolution spatial transcriptomics
Abstract 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..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 10. Nov. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Si, Yichen [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.11.04.565621 |
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| PPN (Katalog-ID): |
XBI041433688 |
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| 520 | |a Abstract 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 Biology |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 570 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Lee, ChangHee |4 aut | |
| 700 | 1 | |a Hwang, Yongha |4 aut | |
| 700 | 1 | |a Yun, Jeong H. |0 (orcid)0000-0002-4361-8295 |4 aut | |
| 700 | 1 | |a Cheng, Weiqiu |4 aut | |
| 700 | 1 | |a Cho, Chun-Seok |4 aut | |
| 700 | 1 | |a Quiros, Miguel |4 aut | |
| 700 | 1 | |a Nusrat, Asma |4 aut | |
| 700 | 1 | |a Zhang, Weizhou |4 aut | |
| 700 | 1 | |a Jun, Goo |0 (orcid)0000-0003-0891-0204 |4 aut | |
| 700 | 1 | |a Zöllner, Sebastian |4 aut | |
| 700 | 1 | |a Lee, Jun Hee |4 aut | |
| 700 | 1 | |a Kang, Hyun Min |0 (orcid)0000-0002-3631-3979 |4 aut | |
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