Sonya A. MacParland,
Jeff C. Liu,
Xue-Zhong Ma,
Brendan T. Innes,
Agata M. Bartczak,
Blair K. Gage,
Justin Manuel,
Nicholas Khuu,
Juan Echeverri,
Ivan Linares,
Rahul Gupta,
Michael L. Cheng,
Lewis Y. Liu,
Damra Camat,
Sai W. Chung,
Rebecca K. Seliga,
Zigong Shao,
Elizabeth Lee,
Shinichiro Ogawa,
Mina Ogawa,
Michael D. Wilson,
Jason E. Fish,
Markus Selzner,
Anand Ghanekar,
David Grant,
Paul Greig,
Gonzalo Sapisochin,
Nazia Selzner,
Neil Winegarden,
Oyedele Adeyi,
Gordon Keller,
Gary D. Bader,
Ian D. McGilvray.
Nature Communications, 2018. DOI: 10.1038/s41467-018-06318-7
Data portal by scClustViz.
The liver is the largest solid organ in the body and is critical for metabolic and immune functions. Surprisingly little is known about the cells that make up the human liver and its immune microenvironment. Here we report a map of the cellular landscape of the human liver using single cell RNA sequencing. We carefully fractionated fragile, fresh hepatic tissue from human livers to obtain viable parenchymal and non-parenchymal cells. Our single cell transcriptomics map reveals 20 discrete cell populations, and includes a description of distinct monocyte/macrophage populations in the human liver. We present a comprehensive view of the human liver at single cell resolution that outlines the characteristics of resident cells in the liver, and in particular provides a map of the human hepatic immune microenvironment.
This is an R package used to explore the human liver single-cell RNAseq data presented in this paper. You can install this package in R by running:
install.packages("devtools")
devtools::install_github("BaderLab/HumanLiver")
It takes a while for this command to run, since data files are larger than your usual github code. You only need to run this installation step the first time you use this package on your computer.
If you get an error about not being able to install multtest, try installing it directly from Bioconductor with BiocManager::install("multtest"). This error has occured during Seurat installation, but may be resolved in newer versions.
Then the data can be viewed in the scClustViz Shiny app by running:
library(HumanLiver)
viewHumanLiver()
Installing org.Hs.eg.db from Bioconductor is also suggested for annotation purposes:
source("https://bioconductor.org/biocLite.R")
biocLite("org.Hs.eg.db")
scClustViz is a visualization tool for single-cell RNAseq designed to assess clustering results for biological relevance using a metric based on differential gene expression between clusters. It also has figures designed for the identification of clusters and their marker genes. See the website and paper for more details.