spacedeconv: deconvolution of tissue architecture from spatial transcriptomics

Investigating tissue architecture is key to understanding tissue function in health and disease. While spatial omics technologies enable the study of cell transcriptomes within their native context, they often lack single-cell resolution. Deconvolution methods can computationally infer tissue composition from spatial transcriptomics data, but differences in their workflows complicate their use and comparison. We developed spacedeconv, a unified interface to different deconvolution methods that additionally supports data preprocessing, visualization, and analysis of cell communication and multimodal data. Here, we demonstrate how spacedeconv streamlines the investigation of the cellular and molecular underpinnings of tissue architecture in different organisms and tissue contexts.