Dylan Cable, Ph.D.
Assistant Professor of Biostatistics, University of Michigan

Abstract

An essential first step in the analysis of spatial transcriptomics data is to assign cell types to each spatial location. This process is complicated by the presence of cell type mixtures on individual spatial locations. The best performing cell type identification algorithms are based on supervised methods that rely on a reference dataset to estimate cell type expression profiles. However, finding a high quality annotated single-cell RNA-seq (scRNA-seq) reference dataset is difficult and often impossible. Here, we address this challenge by developing an unsupervised factor-based statistical method for identifying cell types in spatial transcriptomics datasets, which we call Reference-free Inference of Cell types and Expression (RICE). We model gene expression as a linear mixture of cell type-specific gene expression profiles, and both cell type proportions and cell type-specific gene expression are estimated via maximum likelihood within our probabilistic model. We demonstrate, in several Slide-seq and MERFISH spatial transcriptomics datasets, RICE’s accuracy in estimating both cell type proportions and cell type-specific gene expression. We show that RICE achieves comparable accuracy to state of the art supervised methods when a scRNA-seq reference is available, while it can outperform these methods when the reference is less reliable due to cell type-specific platform effects. We further show that our sparse factor modeling approach outperforms existing non-sparseunsupervised factor-base methods. We distribute RICE within the R package https://github.com/dmcable/spacexr.

Short Bio

Dylan Cable is an assistant professor in biostatistics at the University of Michigan. His research involves developing rigorous statistical modeling approaches for emerging high-throughput genomics technologies, such as spatial transcriptomics and single-cell RNA-sequencing. Dr. Cablecompleted his PhD in computer science at the Massachusetts Institute of Technology and a bachelors degree in mathematics at Stanford University. Dr. Cable is interested in the application of high-throughput genomics technologies to better understand human health and disease, as well as integration with clinical settings and drug discovery pipelines.

Publications of Interest

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