Construction of a human cell landscape at single-cell level

By Xiaoping Han, Ziming Zhou, Lijiang Fei, Huiyu Sun, Renying Wang, Yao Chen, Haide Chen, Jingjing Wang, Huanna Tang, Wenhao Ge and 29 more. Nature, 2020

910 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d890abf9b9ccf58d70b075b92547ffdc807afca3

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Citations in the corpus

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 ).”
UCell: Robust and scalable single-cell gene signature scoring (2021, methodology): “have used single-cell RNA sequencing to quantify cell type heterogeneity in different tissues and to define gene signatures for >100 human and murine cell types [3].”
Predicting cellular responses to complex perturbations in high‐throughput screens (2023, background): “Single-cell RNA-sequencing (scRNA-seq) profiles gene expression in millions of cells across tissues (The Tabula Muris Consortium, 2019; Domcke et al , 2020) and species (Han et al , 2020).”
Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, neutral): “For example, the Human Cell Atlas, Human Biomolecular Atlas Project, Tabula Sapiens 68 and Human Cell Landscape 69 have all released scRNA-seq references spanning hundreds of thousands of cells for multiple human tissues.”
The protein expression profile of ACE2 in human tissues (2020, neutral): “…nine publicly available scRNA-seq datasets from ileum (Wang et al , 2020b), kidney (Liao et al , 2020), testis (Guo et al , 2018), lung (Reyfman et al , 2019; Viera Braga et al , 2019; Han et al , 2020), bronchus (Viera Braga et al , 2019; Lukassen et al , 2020), and nasal mucosa…”
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, methodology): “First, we prepared the most comprehensive single-cell transcriptomics atlases, namely a human cell landscape (HCL) (31) and a mouse cell atlas (MCA) (32), as the underlying training set (Supplementary Table S1).”
Cross-tissue immune cell analysis reveals tissue-specific features in humans (2022, background): “However, despite the effort to assemble murine (21) and human (22, 23) multi-tissue atlases, large-scale crossDomínguez Conde et al.”
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans (2021, methodology): “Having multi-organ data from individual donors may facilitate development of methods to compare diverse datasets and yield understanding of technical artifacts from various approaches (8, 9, 34, 35).”