title: Single nucleus transcriptomics data integration recapitulates the major cell types in human liver
organism: Homo sapiens
description: Aim: We aimed at exploring the effect of the integration of data from single nucleus transcriptomics profiling in identifying cell populations. Methods: We generated single nucleus RNA-seq (snRNA-seq) data from 10X genomics and Drop-seq for a human liver sample, and we integrated the data into a common space while accounting for known and unknown confounding factors. Results: Both 10X and Drop-seq picked up the major liver cell types, while the integrated set obtained enough power to identify a small population of inactive hepatic stellate cells (iHSC) that was not identified in either of the former ones. Conclusions: Integration of droplet-based single nucleus transcriptomics enabled identification of a small cluster of iHSC that underlines the potential of our approach. We suggest snRNA-seq integrative approaches could be utilized to design larger and cost-effective studies.