Guided construction of single cell reference for human and mouse lung
Minzhe Guo, Michael P. Morley, Cheng Jiang, Yixin Wu, Guangyuan Li, Yina Du, Shuyang Zhao, Andrew Wagner, Adnan Cihan Cakar, Michal Kouril, Kang Jin, Nathan Gaddis, Joseph A. Kitzmiller, Kathleen M. Stewart, Maria C. Basil, Susan M. Lin, Yun Ying, Apoorva Babu, Kathryn A. Wikenheiser‐Brokamp, Kyu Shik Mun, Anjaparavanda P. Naren, Gérémy Clair, Joshua Adkins, Gloria Pryhuber, Ravi Misra, Bruce J. Aronow, Timothy L. Tickle, Nathan Salomonis, Xin Sun, Edward E. Morrisey, Jeffrey A. Whitsett, Sara Lin, Yan Xu
Abstract
Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.