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popV/
tabula_muris_Bone_marrow_10x

Name: tabula_muris_Bone_marrow_10x
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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	scanvi	svm	xgboost	Consensus Prediction
granulocyte	1103	0.94	0.97	0.95	0.96	0.94	0.93	0.94	0.97
granulocytopoietic cell	746	0.92	0.92	0.93	0.93	0.89	0.87	0.90	0.94
proerythroblast	366	0.85	0.91	0.86	0.86	0.83	0.86	0.85	0.91
promonocyte	279	0.93	0.96	0.94	0.95	0.94	0.95	0.94	0.96
monocyte	239	0.94	0.97	0.95	0.95	0.94	0.95	0.95	0.96
erythroblast	262	0.90	0.91	0.92	0.94	0.89	0.90	0.91	0.93
hematopoietic precursor cell	199	0.93	0.95	0.96	0.95	0.92	0.95	0.95	0.96
megakaryocyte-erythroid progenitor cell	180	0.81	0.88	0.86	0.88	0.83	0.76	0.81	0.88
naive T cell	166	0.90	0.93	0.94	0.93	0.92	0.93	0.95	0.95
macrophage	91	0.82	0.91	0.90	0.89	0.85	0.87	0.89	0.90
precursor B cell	97	0.94	0.98	0.98	0.96	0.97	0.98	0.97	0.97
immature B cell	86	0.82	0.82	0.78	0.93	0.91	0.68	0.89	0.95
late pro-B cell	57	0.97	0.96	0.97	0.97	0.96	0.97	0.96	0.97
plasma cell	43	0.94	0.99	0.98	0.94	0.88	0.95	0.92	0.97
erythroid progenitor cell, mammalian	44	1.00	0.17	0.77	0.72	0.72	0.98	0.93	0.99
naive B cell	42	0.98	0.98	1.00	0.95	0.91	0.98	0.98	1.00
natural killer cell	14	0.79	0.86	0.89	0.71	0.60	0.81	0.79	0.84
basophil	8	0.94	0.50	0.77	0.88	0.67	0.94	0.93	1.00

The train accuracies are computed on the training data.

Cell Type	N cells	celltypist	knn bbknn	knn harmony	knn on scvi	scanvi	svm	xgboost	Consensus Prediction
granulocyte	9953	0.93	0.97	0.95	0.96	0.93	0.92	0.94	0.96
granulocytopoietic cell	6853	0.91	0.93	0.94	0.94	0.89	0.89	0.92	0.95
proerythroblast	3247	0.85	0.91	0.84	0.87	0.84	0.83	0.86	0.90
promonocyte	2548	0.91	0.95	0.93	0.93	0.92	0.94	0.94	0.95
monocyte	2154	0.92	0.97	0.94	0.94	0.95	0.95	0.95	0.96
erythroblast	2098	0.90	0.93	0.93	0.93	0.89	0.91	0.92	0.94
hematopoietic precursor cell	1945	0.94	0.97	0.96	0.97	0.95	0.97	0.97	0.97
megakaryocyte-erythroid progenitor cell	1694	0.81	0.90	0.88	0.88	0.84	0.81	0.85	0.90
naive T cell	1402	0.90	0.95	0.93	0.94	0.92	0.95	0.95	0.96
macrophage	897	0.79	0.91	0.88	0.87	0.86	0.91	0.90	0.92
precursor B cell	818	0.95	0.99	0.99	0.98	0.97	0.99	0.98	0.99
immature B cell	740	0.85	0.83	0.84	0.95	0.95	0.73	0.93	0.97
late pro-B cell	475	0.99	0.99	0.97	0.99	0.97	0.99	0.99	1.00
plasma cell	402	0.93	0.94	0.93	0.88	0.92	0.97	0.93	0.98
erythroid progenitor cell, mammalian	365	0.96	0.22	0.79	0.82	0.73	0.93	0.90	0.96
naive B cell	356	0.96	0.98	0.97	0.97	0.94	0.99	0.99	0.99
natural killer cell	135	0.77	0.91	0.88	0.77	0.69	0.89	0.94	0.95
basophil	116	0.87	0.53	0.78	0.85	0.78	0.85	0.95	0.94

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

popV/tabula_muris_Bone_marrow_10x

Model description

Metrics

References

popV/
tabula_muris_Bone_marrow_10x