We always have the desire to combine data from different experiments to bump up our dataset size, but we have to worry about batch effects when we do that. This study compared seven widely-used methods for batch correction of scRNA-seq datasets.
Out of MNN, SCVI, LIGER, Combat, BBKNN, Seurat and Harmony the authors found that Harmony was the only one that consistently performed well in their tests (kNN metrics, cluster concordance and differential expression looking at two batches randomly split from the same dataset).
My impression: more studies needed, but a useful exercise!