Challenging benchmarks for supervised learning on sparse scRNA-seq data 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 [1].
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.
You could try the PBMC 3k data from the Satija lab:
https://satijalab.org/seurat/articles/pbmc3k_tutorial.html
For well-annotated data, there's the Single-cell proteo-genomic reference map:
https://www.biorxiv.org/content/10.1101/2021.03.18.435922v1
Data for that are available here.
If these datasets are too large (i.e. too good), you can use the existing counts as weighting for a random removal of reads. Genes with high expression are likely to be present regardless of sequencing depth, hence the count-adjusted removal.
FWIW, I don't think that supervised learning is a good fit for dealing with data sparsity in cDNA read counts. There are plenty of existing attempts to solve the issues by adjusting the assumed models, including research by the authors of commonly-used bulk differential expression tools (e.g. here).