scMARK an 'MNIST' like benchmark to evaluate and optimize models for unifying scRNA data

Here we present a novel benchmark dataset (scMARK.v2), that consists of 11 published cancer scRNA-seq studies, for which we standardized  cell-type author labels and gene identifiers. scMARK.v2 can be used to ask how well models integrate data from different scRNA studies. We also provide a 12th standardized study (Wu et al 2021) that we held-out for evaluation of alignment of data "never seen" before, and a 13th study of newly generated in-vitro scRNA-seq data from cancer and fibroblast cells.

• Data is provided as aData *h5ad files that can be read with Python's library Scanpy.
• Studies inclided in scMARK.v2 were downsampled to 10,000 cells per study.
• The difference between scMARK.v1 and scMARK.v2, is that in v2, we provide at least two studies for each cancer type and each cell type; whereas in v1 a handfull of cell types were present only in one study.