Mouse Liver Data

Introduction

This is a mouse liver single-cell data comprising of 4 different experiments with 3 different protocols.

Integration challenge

Prior to integration, there is a strong separation effect by batches.
The liver cell developments should have a clear trajectory, this biological process was not clear from the pre-integrated data. In the scMerge-integrated, this trajectory was estimated with higher degree of biological interpretability.

Name	ID	Author	DOI or URL	Protocol	Organism	Tissue	# of cell types	# of cells
Liver	GSE87795	Su	10.1186/s12864-017-4342-x	SMARTer/C1	Mouse	Liver	6	1236
	GSE90047	Yang	10.1002/hep.29353	Smart-Seq2			2
	GSE87038	Dong	10.1186/s13059-018-1416-2	STRT-seq			3
	GSE96981	Camp	10.1038/nature22796	SMARTer/C1			3

Some remarks:

To combine the four liver scRNA-seq datasets, we used a semi-supervised approach by considering the developmental stages as the wanted variation to identify the pseudo-replicates. Note that the cells of the four liver datasets are from different fetal mouse liver developmental stages (E9.5-E17.5). To identify the pseudo-replicates corresponding to the hepatoblasts, hepatocytes and cholangiocytes, we used three known markers of hepatoblasts and cholangiocytes, Alb, Afp and Epcam, to guide the scMerge algorithm. The sets of pseudo-replicates that are highly expressed the markers are further split according to the developmental stages.
In addition to integrating 4 scRNA-Seq datasets, scMerge revealed a novel result: We constructed the cell trajectories with cells corresponding to the E17.5 time point of GSE90047 removed. We found that the trajectory associated with scMerge is most consistent with the full Liver data collection and agrees with current literature, while other methods tended to generate extraneous branches with the subset of the Liver data collection.
We further performed SC3 on the scMerge integrated data (k=9). We found that the SC3 clustering results have high concordance with the original cell types.