  Deciphering cell type heterogeneity is crucial for systematically
understanding tissue homeostasis and its dysregulation in diseases.
Computational deconvolution is an efficient approach estimating cell type
abundances from a variety of omics data. Despite significant methodological
progress in computational deconvolution in recent years, challenges are still
outstanding. Here we enlist four significant challenges related to
computational deconvolution, from the quality of the reference data, generation
of ground truth data, limitations of computational methodologies, and
benchmarking design and implementation. Finally, we make recommendations on
reference data generation, new directions of computational methodologies and
strategies to promote rigorous benchmarking.
