popV/tabula_muris_Lung_10x

Popular Vote (popV) model for automated cell type annotation of single-cell RNA-seq data. We provide here pretrained models for plug-in use in your own analysis. Follow our tutorial to learn how to use the model for cell type annotation.

Model description

Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood. To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing—such as senescence, genomic instability and changes in the immune system. This transcriptomic atlas—which we denote Tabula Muris Senis, or ‘Mouse Ageing Cell Atlas’—provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.

Link to CELLxGENE: Link to the data in the CELLxGENE browser for interactive exploration of the data and download of the source data.

Training Code URL: Not provided by uploader.

Metrics

We provide here accuracies for each of the experts and the ensemble model. The validation set accuracies are computed on a 10% random subset of the data that was not used for training.

Cell Type	N cells	celltypist	knn bbknn	knn harmony	knn on scvi	onclass	scanvi	svm	xgboost	Consensus Prediction
classical monocyte	793	0.93	0.93	0.94	0.95	0.00	0.87	0.90	0.92	0.95
bronchial smooth muscle cell	236	0.98	0.98	0.98	0.98	0.00	0.98	0.99	0.97	0.98
intermediate monocyte	177	0.69	0.56	0.73	0.68	0.00	0.68	0.65	0.70	0.78
fibroblast of lung	149	0.98	0.99	0.98	0.95	0.00	0.96	0.98	0.97	0.98
B cell	149	1.00	1.00	0.98	0.99	0.00	0.99	0.99	0.99	1.00
alveolar macrophage	137	0.99	0.99	0.99	0.97	0.00	0.93	0.97	0.99	0.99
natural killer cell	125	0.98	0.99	0.97	0.99	0.00	0.97	0.98	0.99	0.99
lung macrophage	115	0.96	0.91	0.96	0.96	0.00	0.94	0.94	0.93	0.95
non-classical monocyte	97	0.94	0.86	0.89	0.82	0.00	0.90	0.95	0.94	0.96
CD8-positive, alpha-beta T cell	86	0.88	0.89	0.87	0.91	0.00	0.89	0.87	0.89	0.90
neutrophil	65	1.00	0.95	0.98	0.96	0.00	0.92	0.99	1.00	0.98
CD4-positive, alpha-beta T cell	52	0.77	0.83	0.81	0.85	0.00	0.83	0.79	0.82	0.86
adventitial cell	54	0.94	0.96	0.92	0.87	0.00	0.89	0.94	0.93	0.93
mature NK T cell	41	0.87	0.93	0.94	0.93	0.00	0.94	0.93	0.86	0.94
vein endothelial cell	32	0.94	0.90	0.93	0.93	0.00	0.89	0.98	0.81	0.94
T cell	28	0.96	0.98	0.98	0.98	0.00	0.88	0.95	0.93	0.98
myeloid dendritic cell	22	0.80	0.90	0.90	0.87	0.00	0.51	0.70	0.78	0.85
pulmonary interstitial fibroblast	17	0.97	1.00	0.94	0.94	0.00	0.94	0.97	1.00	0.97
basophil	9	0.95	0.71	0.71	0.62	0.00	0.67	0.71	0.75	0.82
pulmonary alveolar type 2 cell	6	0.86	0.86	0.86	0.75	0.00	0.86	0.86	0.86	0.86
regulatory T cell	13	0.00	0.82	0.70	0.87	0.00	0.89	0.88	0.82	0.87
smooth muscle cell of the pulmonary artery	12	0.81	0.86	0.86	0.86	0.00	0.79	0.87	0.75	0.87
plasmacytoid dendritic cell	11	0.86	0.90	0.90	0.90	0.00	0.83	0.95	0.95	0.95
pericyte	6	1.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	1.00
ciliated columnar cell of tracheobronchial tree	9	1.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	1.00
plasma cell	1	1.00	1.00	1.00	0.67	0.00	0.67	0.67	1.00	1.00
dendritic cell	3	0.29	0.40	0.50	0.25	0.00	0.20	0.50	0.29	0.40
endothelial cell of lymphatic vessel	5	1.00	1.00	1.00	1.00	0.00	0.91	1.00	0.89	1.00
club cell	4	0.67	0.00	0.67	0.00	0.00	0.67	0.67	0.67	0.67
lung neuroendocrine cell	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00

The train accuracies are computed on the training data.

Cell Type	N cells	celltypist	knn bbknn	knn harmony	knn on scvi	onclass	scanvi	svm	xgboost	Consensus Prediction
classical monocyte	7129	0.93	0.93	0.94	0.95	0.00	0.88	0.92	0.92	0.96
bronchial smooth muscle cell	2103	0.98	0.99	0.98	0.99	0.00	0.99	0.99	0.99	0.99
intermediate monocyte	1572	0.69	0.59	0.76	0.72	0.00	0.72	0.71	0.74	0.86
fibroblast of lung	1374	0.98	0.98	0.97	0.96	0.00	0.97	1.00	0.99	0.99
B cell	1352	1.00	1.00	0.98	0.99	0.00	0.99	1.00	1.00	1.00
alveolar macrophage	1229	0.99	0.98	0.98	0.97	0.00	0.95	0.99	0.98	0.99
natural killer cell	1068	0.99	0.99	0.93	0.98	0.00	0.98	1.00	1.00	1.00
lung macrophage	1042	0.96	0.92	0.96	0.95	0.00	0.96	0.96	0.96	0.98
non-classical monocyte	913	0.93	0.86	0.91	0.84	0.00	0.96	0.96	0.97	0.98
CD8-positive, alpha-beta T cell	784	0.87	0.91	0.85	0.90	0.00	0.91	0.97	0.98	0.94
neutrophil	487	0.99	0.97	0.96	0.97	0.00	0.95	0.99	0.99	0.99
CD4-positive, alpha-beta T cell	499	0.75	0.83	0.80	0.83	0.00	0.88	0.97	0.98	0.89
adventitial cell	472	0.93	0.95	0.94	0.91	0.00	0.91	0.99	0.96	0.97
mature NK T cell	379	0.83	0.88	0.82	0.89	0.00	0.94	0.98	0.98	0.95
vein endothelial cell	288	0.94	0.96	0.95	0.92	0.00	0.95	0.97	0.95	0.98
T cell	223	0.94	0.94	0.91	0.94	0.00	0.91	0.99	0.98	0.98
myeloid dendritic cell	223	0.83	0.84	0.90	0.89	0.00	0.70	0.84	0.86	0.90
pulmonary interstitial fibroblast	205	0.97	0.99	0.95	0.97	0.00	0.96	0.98	0.99	0.98
basophil	121	0.96	0.89	0.95	0.87	0.00	0.82	0.98	0.98	1.00
pulmonary alveolar type 2 cell	119	0.99	0.99	0.99	0.97	0.00	0.99	1.00	1.00	0.99
regulatory T cell	110	0.00	0.66	0.59	0.67	0.00	0.88	0.99	0.99	0.86
smooth muscle cell of the pulmonary artery	86	0.86	0.93	0.93	0.91	0.00	0.92	0.97	0.96	0.97
plasmacytoid dendritic cell	64	0.86	0.93	0.85	0.90	0.00	0.91	0.98	0.99	0.98
pericyte	55	0.99	0.99	0.96	0.99	0.00	0.98	0.99	1.00	0.99
ciliated columnar cell of tracheobronchial tree	48	0.99	0.99	0.98	0.99	0.00	0.99	1.00	1.00	0.99
plasma cell	48	0.93	0.98	0.95	0.96	0.00	0.97	1.00	1.00	0.99
dendritic cell	42	0.44	0.77	0.76	0.76	0.00	0.51	0.82	0.88	0.84
endothelial cell of lymphatic vessel	36	0.99	0.97	0.93	0.89	0.00	0.97	1.00	1.00	1.00
club cell	11	0.58	0.42	0.53	0.53	0.00	0.83	1.00	0.96	0.78
lung neuroendocrine cell	4	0.00	0.00	0.00	0.00	0.00	0.89	1.00	1.00	1.00

References

A single-cell transcriptomic atlas characterizes ageing tissues in the mouse, The Tabula Muris Consortium, Nicole Almanzar, Jane Antony, Ankit S. Baghel, Isaac Bakerman, Ishita Bansal, Ben A. Barres, Philip A. Beachy, Daniela Berdnik, Biter Bilen, Douglas Brownfield, Corey Cain, Charles K. F. Chan, Michelle B. Chen, Michael F. Clarke, Stephanie D. Conley, Spyros Darmanis, Aaron Demers, Kubilay Demir, Antoine de Morree, Tessa Divita, Haley du Bois, Hamid Ebadi, F. Hernán Espinoza, Matt Fish, Qiang Gan, Benson M. George, Astrid Gillich, Rafael Gòmez-Sjöberg, Foad Green, Geraldine Genetiano, Xueying Gu, Gunsagar S. Gulati, Oliver Hahn, Michael Seamus Haney, Yan Hang, Lincoln Harris, Mu He, Shayan Hosseinzadeh, Albin Huang, Kerwyn Casey Huang, Tal Iram, Taichi Isobe, Feather Ives, Robert C. Jones, Kevin S. Kao, Jim Karkanias, Guruswamy Karnam, Andreas Keller, Aaron M. Kershner, Nathalie Khoury, Seung K. Kim, Bernhard M. Kiss, William Kong, Mark A. Krasnow, Maya E. Kumar, Christin S. Kuo, Jonathan Lam, Davis P. Lee, Song E. Lee, Benoit Lehallier, Olivia Leventhal, Guang Li, Qingyun Li, Ling Liu, Annie Lo, Wan-Jin Lu, Maria F. Lugo-Fagundo, Anoop Manjunath, Andrew P. May, Ashley Maynard, Aaron McGeever, Marina McKay, M. Windy McNerney, Bryan Merrill, Ross J. Metzger, Marco Mignardi, Dullei Min, Ahmad N. Nabhan, Norma F. Neff, Katharine M. Ng, Patricia K. Nguyen, Joseph Noh, Roel Nusse, Róbert Pálovics, Rasika Patkar, Weng Chuan Peng, Lolita Penland, Angela Oliveira Pisco, Katherine Pollard, Robert Puccinelli, Zhen Qi, Stephen R. Quake, Thomas A. Rando, Eric J. Rulifson, Nicholas Schaum, Joe M. Segal, Shaheen S. Sikandar, Rahul Sinha, Rene V. Sit, Justin Sonnenburg, Daniel Staehli, Krzysztof Szade, Michelle Tan, Weilun Tan, Cristina Tato, Krissie Tellez, Laughing Bear Torrez Dulgeroff, Kyle J. Travaglini, Carolina Tropini, Margaret Tsui, Lucas Waldburger, Bruce M. Wang, Linda J. van Weele, Kenneth Weinberg, Irving L. Weissman, Michael N. Wosczyna, Sean M. Wu, Tony Wyss-Coray, Jinyi Xiang, Soso Xue, Kevin A. Yamauchi, Andrew C. Yang, Lakshmi P. Yerra, Justin Youngyunpipatkul, Brian Yu, Fabio Zanini, Macy E. Zardeneta, Alexander Zee, Chunyu Zhao, Fan Zhang, Hui Zhang, Martin Jinye Zhang, Lu Zhou, James Zou; Nature, doi: https://doi.org/10.1038/s41586-020-2496-1