用于揭示细胞异质性的单细胞染色质可及性测序数据的离散潜在嵌入。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang
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引用次数: 0

摘要

单细胞表观基因组数据正以前所未有的速度持续增长,但其高维性和稀疏性等特点给下游分析带来了巨大挑战。虽然深度学习模型--尤其是变异自动编码器--已被广泛用于捕捉低维特征嵌入,但流行的高斯假设与实际数据有些不符,而且这些模型往往难以纳入来自丰富细胞图谱的参考信息。在这里,我们提出了 CASTLE,一种基于向量量化变异自动编码器框架的深度生成模型,用于提取离散的潜在嵌入,以解释单细胞染色质可及性测序数据的特征。与最先进的方法相比,我们验证了 CASTLE 在准确识别细胞类型和合理可视化方面的性能和稳健性。我们证明了 CASTLE 以弱监督或监督方式有效整合现有海量参考数据集的优势。我们进一步证明了 CASTLE 能够直观地提炼出细胞类型特异性特征谱,从而定量地揭示细胞的异质性和生物学意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity
Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models—especially variational autoencoders—have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE’s capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively. A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.
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CiteScore
11.70
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