hECA: the cell-centric assembly of a cell atlas

Abstract

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.

inClust: a general framework for clustering that integrates data from multiple sources

“…We selected Donor 1, 2, and H3 for analysis. Then, according to the method of Chen et al (Chen, et al, 2022), we obtained 43,878 genes. We selected variable genes with Seurat V4.1 (Hao, et al, 2021).…”

STEM: A Method for Mapping Single-cell and Spatial Transcriptomics Data with Transfer Learning

“…The most commonly-used ST protocols aggregate multiple cells into one spot, and the provided data contain both spatial coordinates and in-situ gene expressions with limited resolution and gene coverage [12]. On the contrary, single-cell RNA sequencing (SC) captures exact single-cell level transcripts with a higher throughput of gene species [13] but cannot detect any spatial information. Computationally integrating SC and ST data can build an informative single-cell level spatial landscape and benefits studies of both sides [14].…”

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

“…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 6 ), Human Tumor Atlas Network 7 , BRAIN Initiative Cell Census Network 8 , Human Lung Atlas 9 , Human Gut Atlas 10 , Human BioMolecular Atlas Program (HuBMAP 11 ), The Tabula Sapiens 12 , hECA 13 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 12,[14][15][16][17] .These references data facilitate the automatically cell type annotations in a supervised way without prior marker gene annotations [18][19][20][21][22][23][24] . It is obviously that integrating more reference datasets or combining these atlas-level data will improve the cell type annotations 18,19,23,25 , and various integration methods for single cell annotations have been presented 18,26,27,28 .…”