uniLIVER: a Human Liver Cell Atlas for Data-Driven Cellular State Mapping

Abstract

The liver performs several vital functions such as metabolism, toxin removal and glucose storage through the coordination of various cell types. The cell type compositions and cellular states undergo significant changes in abnormal conditions such as fatty liver, cirrhosis and liver cancer. As the recent breakthrough of the single-cell/single-nucleus RNA-seq (sc/snRNA-seq) techniques, there is a great opportunity to establish a reference cell map of liver at single cell resolution with transcriptome-wise features. In this study, we build a unified liver cell atlas uniLIVER by integrative analyzing a large-scale sc/snRNA-seq data collection of normal human liver with 331,125 cells and 79 samples from 6 datasets. Besides the hierarchical cell type annotations, uniLIVER also proposed a novel data-driven strategy to map any query dataset to the normal reference map by developing a machine learning based framework named LiverCT. Applying LiverCT on the datasets from multiple abnormal conditions (1,867,641 cells and 439 samples from 12 datasets), the alterations of cell type compositions and cellular states were systematically investigated in liver cancer.