Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach
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| الحاوية / القاعدة: | BMC Bioinformatics vol. 26 (2025), p. 1 |
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| المؤلف الرئيسي: | |
| مؤلفون آخرون: | , , , |
| منشور في: |
Springer Nature B.V.
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| الموضوعات: | |
| الوصول للمادة أونلاين: | Citation/Abstract Full Text Full Text - PDF |
| الوسوم: |
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MARC
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| 022 | |a 1471-2105 | ||
| 024 | 7 | |a 10.1186/s12859-025-06054-y |2 doi | |
| 035 | |a 3165418133 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 58459 |2 nlm | ||
| 100 | 1 | |a Zhang, Meng | |
| 245 | 1 | |a Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach | |
| 260 | |b Springer Nature B.V. |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a MotivationSpatial transcriptomics is a state-of-art technique that allows researchers to study gene expression patterns in tissues over the spatial domain. As a result of technical limitations, the majority of spatial transcriptomics techniques provide bulk data for each sequencing spot. Consequently, in order to obtain high-resolution spatial transcriptomics data, performing deconvolution becomes essential. Most existing deconvolution methods rely on reference data (e.g., single-cell data), which may not be available in real applications. Current reference-free methods encounter limitations due to their dependence on distribution assumptions, reliance on marker genes, or the absence of leveraging histology and spatial information. Consequently, there is a critical need for the development of highly flexible, robust, and user-friendly reference-free deconvolution methods capable of unifying or leveraging case-specific information in the analysis of spatial transcriptomics data.ResultsWe propose a novel reference-free method based on regularized non-negative matrix factorization (NMF), named Flexible Analysis of Spatial Transcriptomics (FAST), that can effectively incorporate gene expression data, spatial, and histology information into a unified deconvolution framework. Compared to existing methods, FAST imposes fewer distribution assumptions, utilizes the spatial structure information of tissues, and encourages interpretable factorization results. These features enable greater flexibility and accuracy, making FAST an effective tool for deciphering the complex cell-type composition of tissues and advancing our understanding of various biological processes and diseases. Extensive simulation studies have shown that FAST outperforms other existing reference-free methods. In real data applications, FAST is able to uncover the underlying tissue structures and identify the corresponding marker genes. | |
| 653 | |a Data analysis | ||
| 653 | |a Spatial analysis | ||
| 653 | |a Gene expression | ||
| 653 | |a Spatial distribution | ||
| 653 | |a Histology | ||
| 653 | |a Spatial data | ||
| 653 | |a Genes | ||
| 653 | |a Gene sequencing | ||
| 653 | |a Biological effects | ||
| 653 | |a Deconvolution | ||
| 653 | |a Biomarkers | ||
| 653 | |a Composition effects | ||
| 653 | |a Methods | ||
| 653 | |a Transcriptomics | ||
| 653 | |a Information processing | ||
| 653 | |a Euclidean space | ||
| 653 | |a Algorithms | ||
| 653 | |a Biological activity | ||
| 653 | |a Factorization | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Parker, Joel | |
| 700 | 1 | |a An, Lingling | |
| 700 | 1 | |a Liu, Yiwen | |
| 700 | 1 | |a Sun, Xiaoxiao | |
| 773 | 0 | |t BMC Bioinformatics |g vol. 26 (2025), p. 1 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3165418133/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3165418133/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3165418133/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |