Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA

Chen, Yixin; Hao, Minsheng; Gao, Haoxiang; Li, Jiaqi; Chen, Sijie; Li, Fanhong; Wei, Lei; Zhang, Xuegong

doi:10.1016/j.xpro.2022.101589

Cited by 4 publications

(1 citation statement)

References 5 publications

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“…8f). Moreover, The cell sorting module is adapted from Chen et al 54 that allows the users to view a slice of the samples from the BCA by ltering with the cell type, region or attributes from the metadata and view the expression pro les of the ltered samples (Supplementary Fig. 8g, h).…”

Section: Web Portal Of the Bcamentioning

confidence: 99%

Brain Cell Atlas: An Integrative Ensemble of Cell Transcriptomes Across Human Brain Regions

Miao

Chen²,

Yin³

et al. 2023

Preprint

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Single-cell technologies greatly accelerated our understanding of the human brain cell types and their functions. But most studies focused on only a single or a couple of brain regions in a limited number of donors. Integration of atlas-level single-cell data can offer opportunities in revealing the cell type difference among brain regions, thus rendering explanations to the functional divergence. Here we present the Brain Cell Atlas (BCA), a comprehensive reference atlas of human brain cells, by assembling single-cell data from 70 studies of the human brain throughout major developmental stages across brain regions, covering over 11.3 million cells/nuclei from both healthy and disease. Using machine-learning based algorithms, BCA provides a consensus cell type annotation, while it showcases the discovery of a rare cell population of PCDH9high microglia cells. We demonstrate the gene regulatory difference of this cell type between hippocampus and prefrontal cortex as well as the cell-cell communications related to cell functions. In short, the BCA presents an atlas-level integrative resource for comparing brain cells in different environments and conditions within the Human Cell Atlas.

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Section: Web Portal Of the Bcamentioning

confidence: 99%

Brain Cell Atlas: An Integrative Ensemble of Cell Transcriptomes Across Human Brain Regions

Miao

Chen²,

Yin³

et al. 2023

Preprint

Self Cite

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Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

Gao,

Hua,

et al. 2024

Commun Biol

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A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

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Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

Gao

Hua

Chen

et al. 2021

Preprint

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The goal of big projects like Human Cell Atlas (HCA) and Human BioMedical Atlas Program (HuBMAP) is to build maps that comprehensively define and describe all cell types and their molecular features in a healthy human being. Just like geographical maps must have coordinates, a key task in building cell maps is to provide coordinate systems for cells. A well-designed coordinate system helps better understand the highly orchestrated function and organization of different cells. Cells could be depicted by external information like their spatial locations in the body and organ, the sex and race of the donor, and multiple endogenous attributes of cells such as their types, states, functions, developing trajectory, etc. These heterogeneities are encoded in or can be predicted with transcriptomics and other omics data. Cell heterogeneities are multifaceted, including three major types: continuous values or scores, categorical groups and structured annotations. Here we propose to a unified multidimensional coordinate system UniCoord to represent the multifaceted heterogeneities of cells. It is based on a general deep learning framework, with a supervised VAE structure to learn the mapping relationship between gene expressions and the generated coordinates in a low-dimensional space that encode multiple cell attributes of the three types. Experiment results on several datasets showed that UniCoord was able to represent a variety of cell heterogeneous properties that are discrete, continuous or of hierarchical structures. The trained UniCoord model can be used to automatically label attributes of cells and generate the corresponding expression data. Experiments showed that UniCoord is a feasible coordinates framework for representing multifaceted cell heterogeneity in comprehensive cell atlases.

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Protocol for profiling cell-centric assembled single-cell human transcriptome data in hECA

Cited by 4 publications

References 5 publications

Brain Cell Atlas: An Integrative Ensemble of Cell Transcriptomes Across Human Brain Regions

Brain Cell Atlas: An Integrative Ensemble of Cell Transcriptomes Across Human Brain Regions

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

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