Comparison of methods for cell type deconvolution in spatially resolved transcriptomic data

Music City Center 

Recent technological advancements have made it possible to perform spatially resolved transcriptomic (SRT) profiling, which enhances our understanding of cell-cell communication within the context of tissues. However, current techniques require a compromise between experimental throughput and spatial resolution. Sequencing based technologies prioritize higher experimental throughput, resulting in multicellular pixel data. These datasets necessitate innovative computational methods to deconvolute cell types and avoid potential confounding issues within each pixel. Topic modeling methods, such as Latent Dirichlet Allocation (LDA), spatial LDA, and other statistical frameworks, provide a way to identify cell type composition from multicellular pixels. In this study, we evaluate several deconvolution approaches, assessing their effectiveness in capturing cell type distribution per pixel and gene expression distribution per cell type. Our analysis highlights the strengths and limitations of existing methods, offering guidance on best practices for analyzing multicellular pixel SRT data.

Spatially resolved transcriptomic data

Multicellular pixel data

Cell type deconvolution

Topic mode

Latent Dirichlet Allocation

Cell-cell communication 

Section on Statistics in Genomics and Genetics