DECONVOLUTION ANALYSIS OF SPATIAL TRANSCRIPTOMICS BY MULTIPLICATIVE-ADDITIVE POISSON-GAMMA MODELS

• 成果类型: Article
• 署名作者: Luo, Yutong; Bailey-Wilson, Joan E.; Albanese, Christopher; Fan, Ruzong
• 署名单位: Georgetown University; National Institutes of Health (NIH) - USA; NIH National Human Genome Research Institute (NHGRI); Georgetown University; National Institutes of Health (NIH) - USA; NIH National Human Genome Research Institute (NHGRI)
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/24-AOAS1953
• 发表日期: 2024
• 页码: 3570-3595
• 关键词: gene-expression; cell; regression; atlas

摘要

Understanding cell type composition and gene expression of spatial transcriptomic data is crucial for comprehending phenotypic variability and detecting key factors that influence disease susceptibility of complex traits. Detecting cell type specific expression patterns from spatial transcriptome profiles is important in studying the cellular components and gene expression of individual cell classes and structural architecture. In this paper we develop mixed effect multiplicative-additive Poisson-gamma models to anpressions in single cell RNA-sequenceing (scRNA-seq) data. To build the mixed effect multiplicative-additive Poisson-gamma models, the gene expression counts of spatial transcriptomics data are treated as dependent variables, and the mean and variance parameters of scRNA-seq data are used to construct independent variables to explain the dependent variables on the basis of Poisson-gamma mixture. One novelty of the proposed mixed models is that the variance parameters of scRNA-seq are used to describe the within-celltype variations or stochasticity. We develop iteratively analytical formulae to estimate the cell type proportions and dispersion parameters. To address the important research problems and help with intensive spatial transcriptomics data analysis, a readily available software, MAPS, is developed to implement the proposed methods. By simulation study and real data analysis, MAPS is found to perform better than or similar to robust cell type decomtional autoregressive-based deconvolution (CARD), and a Spatially weighted pOissoN-gAmma Regression model (SONAR). Computationally, MAPS is significantly faster than RCTD and SpatialDWLS. MAPS provides a novel way for mapping spatial tissue architecture.