Abstract

   Uncovering the tissue molecular architecture at single-cell resolution
   could help better understand organisms’ biological and pathological
   processes. However, bulk RNA-seq can only measure gene expression in
   cell mixtures, without revealing the transcriptional heterogeneity and
   spatial patterns of single cells. Herein, we introduce Bulk2Space
   ([62]https://github.com/ZJUFanLab/bulk2space), a deep learning
   framework-based spatial deconvolution algorithm that can simultaneously
   disclose the spatial and cellular heterogeneity of bulk RNA-seq data
   using existing single-cell and spatial transcriptomics references. The