From experience, SCTransform does not perform well unless the majority of the cells are of the same type. It will force true unique populations together with a heavy hand, whereas MNN is much more orthogonal in its changes. Seurat even has a wrapper around fastMNN.
Haven't tried the other options though, so can't speak to them.
So far, MNN is the best (but still very limit) algorithm for general batch effect correction method. But based on the recent paper (https://www.nature.com/articles/s41587-019-0113-3 ), in some situations, it just exhibits minor improvement than doing nothing. It's all depends on how good your data are.
thank you all for your suggestions ! if I may ask for another suggestion please regarding scRNA-seq analysis:
shall we have 2 scRNA-seq samples that do not align too well by using either CCA (in Seurat 2) or Seurat 3 methods (with batch correction in Harmony, Liger, Conos, etc, as we have discussed above), the functions that compute the CONSERVED MARKERS (FindConservedMarkers) or DIFFERENTIAL MARKERS (FindMarkers) likely fail on the cell clusters that DO NOT ALIGN.
how could I still compute the CONSERVED or DIFFERENTIAL MARKERS on the cell clusters that DO align (in some extent) ? If anyone has the experience and would like to share it please. Many thanks for your suggestions; be safe, stay healthy,
ps : 've posted a similar question on Seurat github web page, and i have not heard from Seurat's authors about it for a while.