SpatialDWLS: accurate deconvolution of spatial transcriptomic data

Abstract

Recent development of spatial transcriptomic technologies has made it possible to systematically characterize cellular heterogeneity while preserving spatial information, which greatly enables the investigation of structural organization of a tissue and its impact on modulating cellular behavior. On the other hand, the technology often does not have sufficient resolution to distinguish neighboring cells which may belong to different cell types, therefore it is difficult to identify cell-type distribution directly from the data. To overcome this challenge, we have developed a computational method, called spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmarked the performance of spatialDWLS by comparing with a number of existing deconvolution methods using both real and simulated datasets, and we found that spatialDWLS outperformed the other methods in terms of accuracy and speed. By applying spatialDWLS to analyze a human developmental heart dataset, we observed striking spatial-temporal changes of cell-type composition which becomes increasing spatially coherent during development. As such, spatialDWLS provides a valuable computational tool for faithfully extracting biological information from spatial transcriptomic data.