SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration.
SAILER: scalable and accurate invariant representation learning for single-cell ATAC-seq processing and integration.
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DOI：
10.1093/bioinformatics/btab303
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发表时间：
2021-07-12
期刊：
影响因子：
--
通讯作者：
Xie X
中科院分区：
文献类型：
--
作者：
Cao Y;Fu L;Wu J;Peng Q;Nie Q;Zhang J;Xie X
转座酶可及染色质的单细胞测序分析(scATAC-seq)为剖析表观基因组的异质性和阐明转录调控机制提供了新的机会。然而，SCATAC-SEQ数据的计算建模具有挑战性，因为它具有高维、极端稀疏、复杂的依赖关系和对来自各种来源的混杂因素的高度敏感性。在这里，我们提出了一个新的深层生成模型框架，称为SALER，用于分析SCATAC-SEQ数据。赛勒的目标是学习每个细胞的低维非线性潜在表示，它定义了每个细胞的内在染色质状态，不受阅读深度和批次效应等外部混杂因素的影响。Sailer采用传统的编解码器框架来学习潜在的表示，但施加了额外的约束以确保学习的表示独立于混杂因素。在模拟和真实的scATAC-seq数据集上的实验结果表明，Sailer比其他方法学习更好的细胞表示，并且在生物学上更有意义。它的无噪声单元嵌入在下游分析中带来了显著的好处：基于Sailer的聚类法和归因法分别比现有方法提高了6.9%和18.5%。此外，由于不涉及矩阵分解，Sailer可以轻松扩展以处理数百万个细胞。我们将Sailer实施到一个软件包中，所有人都可以免费使用该软件包进行大规模的SCATAC-SEQ数据分析。该软件在https://github.com/uci-cbcl/SAILER.上公开发售补充数据可在生物信息学在线上获得。
Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) provides new opportunities to dissect epigenomic heterogeneity and elucidate transcriptional regulatory mechanisms. However, computational modeling of scATAC-seq data is challenging due to its high dimension, extreme sparsity, complex dependencies and high sensitivity to confounding factors from various sources. Here, we propose a new deep generative model framework, named SAILER, for analyzing scATAC-seq data. SAILER aims to learn a low-dimensional nonlinear latent representation of each cell that defines its intrinsic chromatin state, invariant to extrinsic confounding factors like read depth and batch effects. SAILER adopts the conventional encoder-decoder framework to learn the latent representation but imposes additional constraints to ensure the independence of the learned representations from the confounding factors. Experimental results on both simulated and real scATAC-seq datasets demonstrate that SAILER learns better and biologically more meaningful representations of cells than other methods. Its noise-free cell embeddings bring in significant benefits in downstream analyses: clustering and imputation based on SAILER result in 6.9% and 18.5% improvements over existing methods, respectively. Moreover, because no matrix factorization is involved, SAILER can easily scale to process millions of cells. We implemented SAILER into a software package, freely available to all for large-scale scATAC-seq data analysis. The software is publicly available at https://github.com/uci-cbcl/SAILER. Supplementary data are available at Bioinformatics online.
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单细胞染色质可及性揭示了调节变化的原理。
影响因子：
64.8
作者：
Buenrostro JD;Wu B;Litzenburger UM;Ruff D;Gonzales ML;Snyder MP;Chang HY;Greenleaf WJ
通讯作者：
Greenleaf WJ
影响因子：
48
作者：
Korsunsky, Ilya;Millard, Nghia;Raychaudhuri, Soumya
通讯作者：
Raychaudhuri, Soumya
DOI：
10.1088/1742-5468/2008/10/p10008
发表时间：
2008-10-01
影响因子：
2.4
作者：
Blondel, Vincent D.;Guillaume, Jean-Loup;Lefebvre, Etienne
通讯作者：
Lefebvre, Etienne
影响因子：
7.7
作者：
Regev, Aviv;Teichmann, Sarah A;Yosef, Nir
通讯作者：
Yosef, Nir
影响因子：
48
作者：
Gonzalez-Blas, Carmen Bravo;Minnoye, Liesbeth;Aerts, Stein
通讯作者：
Aerts, Stein