Mapping the Mouse Cell Atlas by Microwell-Seq.

By Xiaoping Han, Renying Wang, Yincong Zhou, Lijiang Fei, Huiyu Sun, Shujing Lai, A. Saadatpour, Ziming Zhou, Haide Chen, Fang Ye and 18 more. 2018

1,446 citations recorded by the source corpus.

Subfields: Biophysics, Immunology, Molecular Biology

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Citations in the corpus

The art of using t-SNE for single-cell transcriptomics (2018, background): “…has become possible to obtain gene expression data from tens of thousands of cells using full-length sequencing (Tasic et al., 2017; The Tabula Muris Consortium, 2018) and from hundreds of thousands of cells using tag-based protocols (Zeisel et al., 2018; Han et al., 2018; Saunders et al., 2018).”
Making AI Forget You: Data Deletion in Machine Learning (2019, background): “Datasets We run our experiments on ﬁve real, publicly available datasets: Celltype ( N =12 , 009 , D = 10 , K = 4 ) [42], Covtype ( N = 15 , 120 , D = 52 , K = 7 ) [12], MNIST ( N = 60 , 000 , D = 784 , K =10 ) [51], Postures ( N =74 , 975 , D =15 , K =5 ) [35, 34] , Botnet ( N =1 , 018 , 298 , D…”
Molecular Architecture of the Mouse Nervous System (2018, background): “, 2015), and initiatives are underway to create atlases of both human and model organisms (Han et al., 2018; Regev et al., 2017).”
WebGestalt 2024: faster gene set analysis and new support for metabolomics and multi-omics (2024, methodology): “To facilitate the identification of cell types involved in various biological states and processes, we have curated gene signatures for 63 human and 52 mouse cell types, derived from two landmark cell landscape studies ( 22 ,23 ).”
Clustering single-cell RNA-seq data with a model-based deep learning approach (2019, background): “The recent advances in sequencing platforms have enabled a dramatic increase in the throughput to thousands of cells 6–9 .”
The single cell transcriptional landscape of mammalian organogenesis (2019, neutral): “With a new inter-study cross-matching method, we matched 96 MCA cell types to 58 MOCA subtypes (Methods; Extended Data Fig.”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, neutral): “Additionally, Exon+Intron counting may help extracting as much information as possible from low coverage data as generated in the context of high-throughput cell atlas e orts (eg 10,000-20,000 reads/cell [38, 39].”
Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction (2018, neutral): “…of Timd4 and Lyve1 expression in resident macrophages from other tissues suggests that they may represent important resident macrophage markers 38, 39, 40 , and specifically within the myocardium – TIMD4 and LYVE1 may be used to identify resident macrophages in the absence of dedicated tracking…”
Two distinct interstitial macrophage populations coexist across tissues in specific subtissular niches (2019, methodology): “Finally, we took advantage of the recently published mouse cell atlas single-cell database (23), projecting our lung IM subset gene signatures onto bladder, lung, mammary gland, muscle, pancreas, and uterus atlas single-cell macrophages (fig.”