popV/tabula_muris_Kidney_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
kidney proximal convoluted tubule epithelial cell	433	0.91	0.89	0.85	0.85	0.00	0.83	0.90	0.91	0.91
B cell	296	0.97	0.97	0.96	0.98	0.00	0.98	0.97	0.97	0.97
epithelial cell of proximal tubule	318	0.87	0.89	0.78	0.75	0.00	0.79	0.86	0.87	0.88
kidney loop of Henle thick ascending limb epithelial cell	137	0.91	0.94	0.92	0.90	0.00	0.93	0.91	0.92	0.94
lymphocyte	167	0.95	0.84	0.94	0.96	0.00	0.94	0.96	0.95	0.97
macrophage	147	0.98	0.99	0.97	0.98	0.00	0.98	0.97	0.97	0.99
T cell	125	0.99	0.98	0.98	0.96	0.00	0.97	0.98	0.98	0.98
kidney collecting duct principal cell	82	0.93	0.98	0.96	0.96	0.00	0.94	0.96	0.96	0.97
kidney distal convoluted tubule epithelial cell	92	0.95	0.97	0.95	0.94	0.00	0.96	0.96	0.95	0.96
plasmatocyte	44	0.91	0.98	0.98	0.95	0.00	0.97	0.96	0.95	0.99
brush cell	47	0.92	0.95	0.91	0.92	0.00	0.95	0.91	0.95	0.95
kidney cortex artery cell	37	0.78	0.83	0.76	0.62	0.00	0.80	0.87	0.78	0.83
plasma cell	32	0.71	0.40	0.70	0.68	0.00	0.75	0.72	0.77	0.72
mesangial cell	31	0.89	0.98	0.93	0.95	0.00	0.92	0.98	0.97	0.98
kidney loop of Henle ascending limb epithelial cell	19	0.00	0.55	0.22	0.00	0.00	0.58	0.53	0.62	0.44
kidney capillary endothelial cell	18	0.26	0.49	0.24	0.16	0.00	0.24	0.27	0.27	0.27
fibroblast	13	1.00	1.00	1.00	1.00	0.00	1.00	1.00	1.00	1.00
kidney proximal straight tubule epithelial cell	10	0.00	0.00	0.00	0.00	0.00	0.17	0.38	0.41	0.18
natural killer cell	7	1.00	0.92	0.92	0.25	0.00	0.80	1.00	1.00	1.00
kidney collecting duct epithelial cell	0	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
leukocyte	1	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00
kidney 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
kidney proximal convoluted tubule epithelial cell	4027	0.92	0.90	0.89	0.89	0.00	0.85	0.92	0.94	0.95
B cell	2828	0.98	0.98	0.99	0.99	0.00	0.99	0.99	0.99	0.99
epithelial cell of proximal tubule	2735	0.89	0.90	0.83	0.83	0.00	0.82	0.89	0.91	0.92
kidney loop of Henle thick ascending limb epithelial cell	1417	0.92	0.93	0.95	0.93	0.00	0.96	0.97	0.98	0.97
lymphocyte	1369	0.95	0.78	0.97	0.97	0.00	0.96	0.98	0.98	0.98
macrophage	1260	0.98	0.98	0.98	0.98	0.00	0.97	0.98	0.98	0.98
T cell	1234	0.98	0.98	0.98	0.97	0.00	0.98	0.99	0.99	0.99
kidney collecting duct principal cell	707	0.92	0.97	0.96	0.96	0.00	0.98	0.99	0.99	0.98
kidney distal convoluted tubule epithelial cell	652	0.89	0.94	0.94	0.93	0.00	0.97	0.99	0.99	0.96
plasmatocyte	452	0.89	0.94	0.94	0.94	0.00	0.96	0.96	0.98	0.97
brush cell	367	0.90	0.92	0.94	0.95	0.00	0.94	0.95	0.97	0.96
kidney cortex artery cell	344	0.80	0.84	0.82	0.67	0.00	0.84	0.85	0.81	0.84
plasma cell	293	0.85	0.62	0.91	0.90	0.00	0.89	0.95	0.93	0.96
mesangial cell	230	0.88	0.96	0.97	0.94	0.00	0.99	0.99	0.99	0.99
kidney loop of Henle ascending limb epithelial cell	182	0.00	0.36	0.49	0.30	0.00	0.80	0.80	0.88	0.71
kidney capillary endothelial cell	143	0.24	0.41	0.25	0.23	0.00	0.26	0.27	0.28	0.27
fibroblast	148	0.98	0.99	0.97	0.96	0.00	0.99	1.00	1.00	0.99
kidney proximal straight tubule epithelial cell	85	0.00	0.00	0.00	0.00	0.00	0.68	0.66	0.58	0.69
natural killer cell	59	0.79	0.80	0.85	0.63	0.00	0.83	0.92	0.93	0.88
kidney collecting duct epithelial cell	25	0.00	0.07	0.08	0.19	0.00	0.89	1.00	1.00	0.39
leukocyte	4	0.00	0.00	0.00	0.00	0.00	0.73	1.00	1.00	0.00
kidney cell	3	0.00	0.00	0.00	0.00	0.00	0.50	1.00	1.00	0.50

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