Toward a unified information framework for cell atlas assembly

Chen, Sijie; Luo, Ye; Gao, Haoxiang; Li, Fanhong; Li, Jiaqi; Chen, Yixin; You, Renke; Lv, Hairong; Hua, Kui; Jiang, Rui; Zhang, Xuegong

doi:10.1093/nsr/nwab179

Cited by 10 publications

(18 citation statements)

References 10 publications

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“…The overall conceptual structure of hECA is illustrated in Figure 1. It is an instance of the ideal unified information framework that are required for cell atlas assembly (Chen et al, 2021b). It composes of three key components: a unified Giant Table (uGT), a unified Hierarchical Annotation Framework (uHAF) and an API ECAUGT for retrieving data.…”

Section: Resultsmentioning

confidence: 99%

“…Assembling data of massive amount cells from multiple sources into an ensemble atlas, rather than just collecting and indexing the data files from the sources, has many technical and conceptual challenges (Chen et al, 2021b). Firstly, single-cell omics data describes the abundances and occurrences of a large variety of molecules and molecular events in many single cells.…”

Section: Introductionmentioning

confidence: 99%

“…A standard vocabulary system for fine-grained cell identity annotation is still lacking. A unified information framework is needed to tackle these challenges (Chen et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

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hECA: the cell-centric assembly of a cell atlas

Chen

Luo

Gao

et al. 2021

Preprint

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The significance of building atlases of human cells as references for future biological and medical studies of human in health or disease has been well recognized. Comparing to the rapidly accumulation of single-cell data, there has been fewer published work on the information structure to assemble cell atlases, or on methods for using reference atlases once they are ready. Most existing cell atlas work organize single-cell gene expression data as a collection of individual files, allowing users to download selected data sheets, or to annotate query cells using models pretrained with the collected data. These features are useful as the basic use of cell atlases. More comprehensive uses of global cell atlases can be developed once data of cells from multiple organs across different studies can be assembled into one orchestrated data repository rather than a collection of data files. For this purpose, we presented a unified giant table or uGT to store and organize single-cell data from multiple studies into a single huge data repository, and a unified hierarchical annotation framework or uHAF to annotate cells from uncoordinated studies. Based on these technologies, we developed a system that enables users to design complex rules to recruit from the atlas cells that meet certain conditions, such as with desired expression range of a gene or multiple genes and with required organ, tissue or developmental origins, across multiple datasets that were otherwise unconnected. The conditions can be expressed as sophisticated logic criteria to pinpoint specific cells that cannot be easily spotted in traditional in vivo or in vitro cell sorting or in traditional searching in published data. We call this technology as in data cell sorting from cell atlases. With the increasing coverage of the cell atlas, this in data experiment paradigm will facilitate scientists to conduct investigations in the data space beyond the restrictions in traditional in vivo and in vitro experiments. In the current work, we collected scRNA-seq data of more than 1 million human cells from scattered studies and assembled them as a human Ensemble Cell Atlas or hECA using the proposed information structure, and provided comprehensive tools for in data experiments based on the atlas. Case examples on agile construction of atlases of particular cell types and on studying the side effects of targeted immune therapy drugs showed that in data cell sorting is an efficient and effective way for comprehensive discoveries. hECA provides a powerful platform for assembling massive scattered single-cell data into a unified atlas, and can serve as a prototype for building future cell atlases.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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hECA: the cell-centric assembly of a cell atlas

Chen

Luo

Gao

et al. 2021

Preprint

Self Cite

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“…Assembling data of massive amounts of cells from multiple sources into an ensemble atlas have many technical and conceptual challenges (S. Chen et al., 2022b ). Firstly, single-cell omics data describe the abundances and occurrences of a large variety of molecules and molecular events in many single cells.…”

Section: Introductionmentioning

confidence: 99%

“…A standard vocabulary system for fine-grained cell identity annotation is still lacking. A unified informatics framework is needed to tackle these challenges ( Börner et al., 2021 ; S. Chen et al., 2022b ; Osumi-Sutherland et al., 2021 ).…”

Section: Introductionmentioning

confidence: 99%

hECA: The cell-centric assembly of a cell atlas

et al. 2022

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Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy

Chen

Duan

Zhu

et al. 2022

Sci. China Life Sci.

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The rapid accumulation of large-scale single-cell RNA-seq datasets from multiple institutions presents remarkable opportunities for automatically cell annotations through integrative analyses. However, the privacy issue has existed but being ignored, since we are limited to access and utilize all the reference datasets distributed in different institutions globally due to the prohibited data transmission across institutions by data regulation laws. To this end, we present scPrivacy , which is the first and generalized automatically single-cell type identification prototype to facilitate single cell annotations in a data privacy-preserving collaboration manner. We evaluated scPrivacy on a comprehensive set of publicly available benchmark datasets for single-cell type identification to stimulate the scenario that the reference datasets are rapidly generated and distributed in multiple institutions, while they are prohibited to be integrated directly or exposed to each other due to the data privacy regulations, demonstrating its effectiveness, time efficiency and robustness for privacy-preserving integration of multiple institutional datasets in single cell annotations. Supporting Information The supporting information is available online at 10.1007/s11427-022-2224-4. The supporting materials are published as submitted, without typesetting or editing. The responsibility for scientific accuracy and content remains entirely with the authors.

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Toward a unified information framework for cell atlas assembly

Abstract: This perspective discusses the need and directions for developing a unified information framework to enable assembling cell atlases and revolutionizing medical research on the virtual body of assembled cell systems.

Cited by 10 publications

References 10 publications

hECA: the cell-centric assembly of a cell atlas

hECA: the cell-centric assembly of a cell atlas

hECA: The cell-centric assembly of a cell atlas

Privacy-preserving integration of multiple institutional data for single-cell type identification with scPrivacy

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