SCALE method for single-cell ATAC-seq analysis via latent feature extraction

Lei Xiong, Kui Xu, Kang Tian, Yanqiu Shao, Lei Tang, Ge Gao, Michael Zhang, Tao Jiang and Qiangfeng Cliff Zhang ()
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Lei Xiong: Tsinghua University
Kui Xu: Tsinghua University
Kang Tian: Tsinghua University
Yanqiu Shao: Tsinghua University
Lei Tang: Tsinghua University
Ge Gao: Peking University
Michael Zhang: Tsinghua University
Tao Jiang: University of California
Qiangfeng Cliff Zhang: Tsinghua University

Nature Communications, 2019, vol. 10, issue 1, 1-10

Abstract: Abstract Single-cell ATAC-seq (scATAC-seq) profiles the chromatin accessibility landscape at single cell level, thus revealing cell-to-cell variability in gene regulation. However, the high dimensionality and sparsity of scATAC-seq data often complicate the analysis. Here, we introduce a method for analyzing scATAC-seq data, called Single-Cell ATAC-seq analysis via Latent feature Extraction (SCALE). SCALE combines a deep generative framework and a probabilistic Gaussian Mixture Model to learn latent features that accurately characterize scATAC-seq data. We validate SCALE on datasets generated on different platforms with different protocols, and having different overall data qualities. SCALE substantially outperforms the other tools in all aspects of scATAC-seq data analysis, including visualization, clustering, and denoising and imputation. Importantly, SCALE also generates interpretable features that directly link to cell populations, and can potentially reveal batch effects in scATAC-seq experiments.

Date: 2019
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DOI: 10.1038/s41467-019-12630-7

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