Single-cell transcriptomics of murine mural cells reveals cellular heterogeneity

Guan, Ya‐Na; Li, Yue; Roosan, Moom R.; Jing, Qing

doi:10.1007/s11427-020-1823-2

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…Single-cell transcriptomics is indispensable for understanding cellular mechanisms of complex tissues and organisms (Guan et al, 2021;Jiang et al, 2021;Plass et al, 2018;Xie et al, 2021;Zhao et al, 2022). As single-cell technologies rapidly developed over recent years, its experimental throughput increased substantially, allowing to profile increasingly complex and diverse samples, and accumulating vast numbers of datasets over time.…”

Section: Introductionmentioning

confidence: 99%

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Chen

Duan

Zhu

et al. 2022

Sci. China Life Sci.

View full text Add to dashboard Cite

show abstract

“…Single-cell transcriptomics is indispensable for understanding cellular mechanisms of complex tissues and organisms [1][2][3][4][5] . As single-cell technologies rapidly developed over recent years, its experimental throughput increased substantially, allowing to profile increasingly complex and diverse samples, and accumulating vast numbers of datasets over time.…”

Section: Introductionmentioning

confidence: 99%

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

Chen

Duan

Zhu

et al. 2022

Preprint

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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 existed but being ignored, since we are limited to access 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 and time efficiency for privacy-preserving integration of multiple institutional datasets in single cell annotations.

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Recent advances in droplet microfluidics for single-cell analysis

Jiang¹,

Shi²,

Tang³

et al. 2023

TrAC Trends in Analytical Chemistry

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Single-cell transcriptomics of murine mural cells reveals cellular heterogeneity

Cited by 3 publications

References 62 publications

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

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

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

Recent advances in droplet microfluidics for single-cell analysis

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