Flexible analysis of spatial transcriptomics data (FAST): a deconvolution approach

作者：Meng Zhang, Joel S. Parker, Lingling An, Yiwen Liu, Xiaoxiao Sun · 发表于：BMC Bioinformatics · 年份：2025 · DOI：10.1186/s12859-025-06054-y · 被引用次数：5 · 研究领域：Single-cell and spatial transcriptomics、Gut microbiota and health、Gene expression and cancer classification

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 ...