Partitioning the Full Transcriptome Profile Within and Beyond Cells in Spatial Transcriptomics

Music City Center 

Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of biological systems, yet it fails to capture crucial components of the tissue transcriptome, such as neurite-localized transcripts and extracellular RNA. Spatial transcriptomics (ST) technologies offer an alternative by capturing transcript locations without tissue dissociation. However, existing approaches—such as cell type deconvolution and cell segmentation—primarily aim to recover single-cell-level information, overlooking the residual transcriptome: mRNAs that are either not captured by scRNA-seq or not assigned to any segmented cells in ST data. To address these limitations, we introduce RESCUE, a novel statistical framework that fully partitions gene expression data into contributions from known reference factors and the residual transcriptome. We formulate the problem as a penalized robust regression with a sparse mean-shift parameterization. To account for gene-specific variability, we employ iteratively reweighted adaptive Lasso-type weights. An efficient simulation-based surrogate matching pursuit algorithm is developed for the tuning procedure. Our results demonstrate that RESCUE outperforms existing methods in accurately decomposing ST data and recovers biologically meaningful signals that were previously overlooked. By fully leveraging the unbiased nature of ST data, RESCUE provides a more comprehensive view of transcriptomic organization both within and beyond cell bodies.

Spatial transcriptomics

Single-cell RNA sequencing

Sparse recovery

Robust estimation

Regularized multivariate regression 

Section on Statistics in Genomics and Genetics