hECA: The cell-centric assembly of a cell atlas

Chen, Sijie; Luo, Ye; Gao, Haoxiang; Li, Fanhong; Chen, Yixin; Li, Jiaqi; You, Renke; Hao, Minsheng; Bian, Haiyang; Xi, Xi; Li, Wenrui; Li, Weiyu; Ye, Mingli; Meng, Qiuchen; Zou, Ziheng; Li, Chen; Li, Haochen; Zhang, Yangyuan; Cui, Yanfei; Wei, Lei; Chen, Fufeng; Wang, Xiaowo; Lv, Hairong; Hua, Kui; Jiang, Rui; Zhang, Xuegong

doi:10.1016/j.isci.2022.104318

Cited by 32 publications

(35 citation statements)

References 95 publications

(126 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the future, we may not need to set control groups. We can use the public single-cell atlas datasets such as hECA 25 to get information of cell clusters instead of using control groups to estimate.…”

Section: Discussionmentioning

confidence: 99%

scDecouple: Decoupling cellular response from infected proportion bias in scCRISPR-seq

Meng

Shi

Wei

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

scCRISPR-seq is an emerging high-throughput CRISPR screening technology that combines CIRPSR screening with single-cell sequencing technologies. It provides rich information on gene regulation. When performing scCRISPR-seq in a population of heterogeneous cells, the observed cellular response in perturbed cells may be caused not only by the perturbation, but also by the infection bias of guide RNAs (gRNAs) mainly contributed by intrinsic differences of cell clusters. The mixing of these effects poisons gene regulation studies. We developed scDecouple to decouple the true cellular response of the perturbation from the influence of infection bias. It models the distribution of perturbed cells and iteratively finds the maximum likelihood of cell cluster proportions as well as the real cellular response for each gRNA. We demonstrated its performance on a series of simulation experiments. By applying scDecouple to real CROP-seq data, we found that scDecouple could enhance biological discovery by detecting perturbation-related genes more critically. It helps to better study gene function and identify disease targets via scCRISPR-seq, especially with heterogeneous samples or complex gRNA libraries.

show abstract

Section: Discussionmentioning

confidence: 99%

scDecouple: Decoupling cellular response from infected proportion bias in scCRISPR-seq

Meng

Shi

Wei

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Single-cell RNA sequencing (scRNA-seq) technology has transformed the field of cell biology and enabled us to understand cell-cell, cell-gene and gene-gene relations at individual cell level (Jovic et al, 2022; Chen et al, 2019). This technique determines the expression levels of thousands of genes in parallel, and has been shown useful in the study of cellular heterogeneity and the identification of unique molecular signatures within a large population (Chen et al, 2022b; Li et al, 2022). This unveiled information is crucial for understanding complex biological systems and disease progression (Jovic et al, 2022; Chen et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

Gong¹,

Hao

Zeng³

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

The advances in high-throughput sequencing technology have led to significant progress in measuring gene expressions in single-cell level. The amount of publicly available single-cell RNA-seq (scRNA- seq) data is already surpassing 50M records for human with each record measuring 20,000 genes. This highlights the need for unsupervised representation learning to fully ingest these data, yet classical transformer architectures are prohibitive to train on such data in terms of both computation and memory. To address this challenge, we propose a novel asymmetric encoder-decoder transformer for scRNA-seq data, called xTrimoGene, which leverages the sparse characteristic of the data to scale up the pre-training. This scalable design of xTrimoGene reduces FLOPs by one to two orders of magnitude compared to classical transformers while maintaining high accuracy, enabling us to train the largest transformer models over the largest scRNA-seq dataset today. Our experiments also show that the performance of xTrimoGene improves as we increase the model sizes, and it also leads to SOTA performance over various downstream tasks, such as cell classification, perturb-seq effect prediction, and drug combination prediction.

show abstract

“…Integrative analyses of such large-scale datasets originating from various samples, different platforms and different institutions globally, offer unprecedented opportunities to establish a comprehensive picture of cell landscape. To this end, various community generated large-scale atlas-level single cell reference data, such as the Human Cell Atlas (HCA) (Regev et al, 2017), Human Tumor Atlas Network (Rozenblatt-Rosen et al, 2020), BRAIN Initiative Cell Census Network (Winnubst and Arber, 2021) , Human Lung Atlas (Travaglini et al, 2020), Human Gut Atlas (Elmentaite et al, 2020), Hu-man BioMolecular Atlas Program (HuBMAP) (Snyder et al, 2019), The Tabula Sapiens (Jones et al, 2022), hECA (Chen et al, 2022a) etc., and recently great achievements has been made in the building of pan-tissue single-cell transcriptome atlases covering more than a million cells, including 500 cell types, across more than 30 human tissues from 68 donors (Domínguez Conde et al, 2022;Eraslan et al, 2022;Jones et al, 2022;Liu and Zhang, 2022;Suo et al, 2022). These references data facilitate the automatically cell type annotations in a supervised way without prior marker gene annotations (Aran et al, 2019;Duan et al, 2020;Kiselev et al, 2018;Liu et al, 2020;Ma and Pellegrini, 2020;Stuart et al, 2019).…”

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

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

hECA: The cell-centric assembly of a cell atlas

Cited by 32 publications

References 95 publications

scDecouple: Decoupling cellular response from infected proportion bias in scCRISPR-seq

scDecouple: Decoupling cellular response from infected proportion bias in scCRISPR-seq

xTrimoGene: An Efficient and Scalable Representation Learner for Single-Cell RNA-Seq Data

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

Contact Info

Product

Resources

About