SpatialMap: Spatial Mapping of Unmeasured Gene Expression Profiles in Spatial Transcriptomic Data Using Generalized Linear Spatial Models - Gao, Dalong; Ning, Jin; Liu, Gang; Sun, Shiquan; Dang, Xiaoqian (Frontiers in Genetics, 2022)
Recent advances of various single-cell RNA sequencing (scRNA-seq) technologies have enabled profiling the gene expression level with the whole transcriptome at a single-cell resolution. However, it lacks the spatial context of tissues. The image-based transcriptomics in situ studies (e.g., MERFISH, seqFISH, etc.) do maintain the cell spatial context at individual cell levels but can only measure a limited number of genes or transcripts (up to roughly 1,000 genes). Therefore, integrating scRNA-seq data and image-based transcriptomics data can potentially gain the complementary benefits of both. Here, we develop a computational method, SpatialMap to bridge thus a gap, which primarily facilitates spatial mapping of unmeasured gene profiles in spatial transcriptomic data via integrating with scRNA-seq data from the same tissue. SpatialMap directly models the count nature of spatial gene expression data through generalized linear spatial models, which accounts for the spatial correlation among spatial locations using conditional autoregressive (CAR) prior. With a newly developed computationally efficient penalized quasi-likelihood (PQL) based algorithm, SpatialMap can scale up to performing large-scale spatial mapping analysis. Finally, we applied the SpatialMap on four publicly available paired studies (i.e., scRNA-seq studies and image-based transcriptomics studies). The results demonstrate that the proposed method can accurately predict unmeasured gene expression profiles across various spatial and scRNA-seq data set pairs of different species and technologies.