Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.

Recent advances in 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 and seqFISH) 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 the 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 to four publicly available tissue-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 dataset pairs of different species and technologies.