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Article summary:

1. Single-cell ATAC-seq (scATAC-seq) is a method to probe genome-wide open chromatin sites at the single-cell level, but suffers from missing data due to low sequencing depth.

2. Existing computational approaches for scRNA-seq analysis may not be suitable for scATAC-seq data due to its close-to-binary nature and increased sparsity.

3. SCALE (Single-Cell ATAC-seq analysis via Latent feature Extraction), a method that combines the VAE framework with the Gaussian Mixture Model, can effectively extract latent features, cluster cell mixtures into subpopulations, and denoise/impute missing values in scATAC-seq data. It outperforms other widely-used dimensionality reduction techniques and state-of-art scRNA-seq and scATAC-seq analysis tools.

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另外,文章中提出了使用Gaussian Mixture Model (GMM)作为先验分布来改进VAE模型的性能。然而,该主张缺乏足够的证据支持,并且未探索任何反驳观点。