Deciphering cell type heterogeneity is crucial for systematically
understanding tissue homeostasis and its dysregulation in diseases.
Computational deconvolution is an efficient approach to estimate 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 from availability of
the reference data, generation of simulation 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