Guided construction of single cell reference for human and mouse lung.
Guided construction of single cell reference for human and mouse lung.
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DOI：
10.1038/s41467-023-40173-5
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发表时间：
2023-07-29
影响因子：
16.6
通讯作者：
Xu, Yan
中科院分区：
文献类型：
--
作者：
Guo, Minzhe;Morley, Michael P.;Jiang, Cheng;Wu, Yixin;Li, Guangyuan;Du, Yina;Zhao, Shuyang;Wagner, Andrew;Cakar, Adnan Cihan;Kouril, Michal A.;Jin, Kang;Gaddis, Nathan C.;Kitzmiller, Joseph A. M.;Stewart, Kathleen;Basil, Maria C.;Lin, Susan M. A.;Ying, Yun;Babu, Apoorva P.;Wikenheiser-Brokamp, Kathryn A.;Mun, Kyu Shik N.;Naren, Anjaparavanda P. S.;Clair, Geremy S.;Adkins, Joshua N. J.;Pryhuber, Gloria S. L.;Misra, Ravi S.;Aronow, Bruce J.;Tickle, Timothy L. E.;Salomonis, Nathan A.;Sun, Xin;Morrisey, Edward E.;Whitsett, Jeffrey A.;Xu, Yan
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. Accurate cell-type identification is vital for single-cell analysis. Here, the authors develop a computational pipeline called “LungMAP CellRef” for efficient, automated cell-type annotation of normal and disease human and mouse lung single-cell datasets.
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影响因子：
7.7
作者：
Miller JA;Gouwens NW;Tasic B;Collman F;van Velthoven CT;Bakken TE;Hawrylycz MJ;Zeng H;Lein ES;Bernard A
通讯作者：
Bernard A
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
16.6
作者：
Li, Guangyuan;Song, Baobao;Singh, Harinder;Surya Prasath, V. B.;Leighton Grimes, H.;Salomonis, Nathan
通讯作者：
Salomonis, Nathan
影响因子：
12.3
作者：
Abdelaal, Tamim;Michielsen, Lieke;Mahfouz, Ahmed
通讯作者：
Mahfouz, Ahmed
DOI：
10.1164/rccm.201911-2199oc
发表时间：
2020-12-15
影响因子：
24.7
作者：
Deprez, Marie;Zaragosi, Laure-Emmanuelle;Barbry, Pascal
通讯作者：
Barbry, Pascal