Multi-batch single-cell comparative atlas construction by deep learning disentanglement.
Multi-batch single-cell comparative atlas construction by deep learning disentanglement.
复制标题
通过深度学习分离的多批量单细胞比较地图集的结构。
DOI：
10.1038/s41467-023-39494-2
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发表时间：
2023-07-12
影响因子：
16.6
通讯作者：
Meyer, Clifford A.
中科院分区：
文献类型：
--
作者：
Lynch, Allen W.;Brown, Myles;Meyer, Clifford A.
通过单细胞RNA-seq和ATAC-seq分析构建的细胞状态图谱是分析遗传和药物治疗诱导的扰动对复杂细胞系统影响的有力工具。对这些图谱的比较分析可以对细胞状态和轨迹变化产生新的见解。扰动实验通常需要在多个批次中进行单细胞测定，这可能会引入混淆不同批次之间生物量比较的技术失真。在这里，我们提出了CODAL，变分自动编码器为基础的统计模型，它使用了互信息正则化技术，明确解开相关的技术和生物效应的因素。我们证明了CODAL的能力批混淆细胞类型发现时，应用于模拟数据集和胚胎发育图谱与基因敲除。CODAL改进了RNA-seq和ATAC-seq模式的表示，产生了生物变异的可解释模块，并使其他基于计数的生成模型能够推广到多批次数据。由于技术缺陷，比较来自多个批次的单细胞RNA-seq和ATAC-seq数据具有挑战性。在这里，作者提出了一种方法，解开技术和生物学效应，促进批次混淆染色质和基因表达状态的发现，并加强对细胞群的扰动效应的分析。
Cell state atlases constructed through single-cell RNA-seq and ATAC-seq analysis are powerful tools for analyzing the effects of genetic and drug treatment-induced perturbations on complex cell systems. Comparative analysis of such atlases can yield new insights into cell state and trajectory alterations. Perturbation experiments often require that single-cell assays be carried out in multiple batches, which can introduce technical distortions that confound the comparison of biological quantities between different batches. Here we propose CODAL, a variational autoencoder-based statistical model which uses a mutual information regularization technique to explicitly disentangle factors related to technical and biological effects. We demonstrate CODAL’s capacity for batch-confounded cell type discovery when applied to simulated datasets and embryonic development atlases with gene knockouts. CODAL improves the representation of RNA-seq and ATAC-seq modalities, yields interpretable modules of biological variation, and enables the generalization of other count-based generative models to multi-batched data. Comparing single-cell RNA-seq and ATAC-seq data from multiple batches is challenging due to technical artifacts. Here, the authors propose a method that disentangles technical and biological effects, facilitating batch-confounded chromatin and gene expression state discovery and enhancing the analysis of perturbation effects on cell populations.
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影响因子：
12.3
作者：
Gustafsson J;Robinson J;Nielsen J;Pachter L
通讯作者：
Pachter L
影响因子：
48
作者：
Amodio, Matthew;van Dijk, David;Srinivasan, Krishnan;Chen, William S.;Mohsen, Hussein;Moon, Kevin R.;Campbell, Allison;Zhao, Yujiao;Wang, Xiaomei;Venkataswamy, Manjunatha;Desai, Anita;Ravi, V.;Kumar, Priti;Montgomery, Ruth;Wolf, Guy;Krishnaswamy, Smita
通讯作者：
Krishnaswamy, Smita
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
6
作者：
Blei, DM;Ng, AY;Jordan, MI
通讯作者：
Jordan, MI
影响因子：
46.9
作者：
Haghverdi L;Lun ATL;Morgan MD;Marioni JC
通讯作者：
Marioni JC