Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach
Abstract Motivation Spatial 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. C...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s12859-025-06054-y |
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| author | Meng Zhang Joel Parker Lingling An Yiwen Liu Xiaoxiao Sun |
| author_facet | Meng Zhang Joel Parker Lingling An Yiwen Liu Xiaoxiao Sun |
| author_sort | Meng Zhang |
| collection | DOAJ |
| description | Abstract Motivation Spatial 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. Results We 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. |
| format | Article |
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| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
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| spelling | doaj-art-4bdfc17ea13a4b9fa2f37b49f7a552d52025-02-02T12:45:02ZengBMCBMC Bioinformatics1471-21052025-01-0126111410.1186/s12859-025-06054-yFlexible analysis of spatial transcriptomics data (FAST): a deconvolution approachMeng Zhang0Joel Parker1Lingling An2Yiwen Liu3Xiaoxiao Sun4Department of Mathematics, University of ArizonaDepartment of Epidemiology and Biostatistics, University of ArizonaDepartment of Agricultural and Biosystems Engineering, University of ArizonaDepartment of Epidemiology and Biostatistics, University of ArizonaDepartment of Epidemiology and Biostatistics, University of ArizonaAbstract Motivation Spatial 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. Results We 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.https://doi.org/10.1186/s12859-025-06054-ySpatial transcriptomicsReference-freeDeconvolutionNon-negative matrix factorization |
| spellingShingle | Meng Zhang Joel Parker Lingling An Yiwen Liu Xiaoxiao Sun Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach BMC Bioinformatics Spatial transcriptomics Reference-free Deconvolution Non-negative matrix factorization |
| title | Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach |
| title_full | Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach |
| title_fullStr | Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach |
| title_full_unstemmed | Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach |
| title_short | Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach |
| title_sort | flexible analysis of spatial transcriptomics data fast a deconvolution approach |
| topic | Spatial transcriptomics Reference-free Deconvolution Non-negative matrix factorization |
| url | https://doi.org/10.1186/s12859-025-06054-y |
| work_keys_str_mv | AT mengzhang flexibleanalysisofspatialtranscriptomicsdatafastadeconvolutionapproach AT joelparker flexibleanalysisofspatialtranscriptomicsdatafastadeconvolutionapproach AT linglingan flexibleanalysisofspatialtranscriptomicsdatafastadeconvolutionapproach AT yiwenliu flexibleanalysisofspatialtranscriptomicsdatafastadeconvolutionapproach AT xiaoxiaosun flexibleanalysisofspatialtranscriptomicsdatafastadeconvolutionapproach |