One challenging aspect of modeling scRNA-seq data is data sparsity, that is, scRNA-seq measurements typically suffer from large fractions of observed zeros (i.e. dropouts), where a given gene in a given cell has no unique molecular identifiers or reads mapping to it .
What would be an appropriate dataset to benchmark supervised learning algorithms that operate on single-cell RNA-seq data? In particular, I am looking for challenging datasets (preferably with labels, i.e. cell type classification) where existing methods struggle due to the sparsity of the data. Additionally, does it ever happen that the dropouts are confounders of the labels? (i.e. for a given cell type A the dropout probability of a gene X is higher than for another cell type B).
Any pointers are welcome.