A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.
A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics.
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DOI：
10.1101/gr.271874.120
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发表时间：
2021-10
期刊：
影响因子：
7
通讯作者：
Li M
中科院分区：
文献类型：
--
作者：
Lakkis J;Wang D;Zhang Y;Hu G;Wang K;Pan H;Ungar L;Reilly MP;Li X;Li M
单细胞 RNA 测序 (scRNA-seq) 技术的最新发展带来了巨大的生物学发现。随着 scRNA-seq 研究规模的增加，分析中的一个主要挑战是批次效应，这在涉及人体组织的研究中是不可避免的。大多数现有方法消除了低维嵌入空间中的批次效应。尽管对于聚类有用，但批次效应仍然存在于基因表达空间中，使得下游基因水平分析容易受到批次效应的影响。最近的研究表明，基因表达空间中的批量效应校正比嵌入空间中的批量效应校正要困难得多。 Seurat 3.0等方法依靠相互最近邻（MNN）方法来消除基因表达中的批次效应，但MNN一次只能分析两个批次，当批次数量很大时，它在计算上变得不可行。在这里，我们提出了 CarDEC，这是一种联合深度学习模型，它可以同时对 scRNA-seq 数据进行聚类和去噪，同时纠正嵌入和基因表达空间中的批次效应。跨不同物种和组织的综合评估表明，CarDEC 优于 Scanorama、DCA + Combat、scVI 和 MNN。通过 CarDEC 去噪，非高度可变基因可提供与高度可变基因 (HVG) 一样多的聚类信号，这表明 CarDEC 显着提高了 scRNA-seq 中的信息内容。我们还表明，使用 CarDEC 的去噪和批次校正表达作为输入进行轨迹分析，揭示了标记基因和转录因子，否则这些标记基因和转录因子在存在批次效应的情况下会被掩盖。 CarDEC 的计算速度很快，使其成为大规模 scRNA-seq 研究的理想工具。
Recent developments of single-cell RNA-seq (scRNA-seq) technologies have led to enormous biological discoveries. As the scale of scRNA-seq studies increases, a major challenge in analysis is batch effects, which are inevitable in studies involving human tissues. Most existing methods remove batch effects in a low-dimensional embedding space. Although useful for clustering, batch effects are still present in the gene expression space, leaving downstream gene-level analysis susceptible to batch effects. Recent studies have shown that batch effect correction in the gene expression space is much harder than in the embedding space. Methods such as Seurat 3.0 rely on the mutual nearest neighbor (MNN) approach to remove batch effects in gene expression, but MNN can only analyze two batches at a time, and it becomes computationally infeasible when the number of batches is large. Here, we present CarDEC, a joint deep learning model that simultaneously clusters and denoises scRNA-seq data while correcting batch effects both in the embedding and the gene expression space. Comprehensive evaluations spanning different species and tissues showed that CarDEC outperforms Scanorama, DCA + Combat, scVI, and MNN. With CarDEC denoising, non-highly variable genes offer as much signal for clustering as the highly variable genes (HVGs), suggesting that CarDEC substantially boosted information content in scRNA-seq. We also showed that trajectory analysis using CarDEC's denoised and batch-corrected expression as input revealed marker genes and transcription factors that are otherwise obscured in the presence of batch effects. CarDEC is computationally fast, making it a desirable tool for large-scale scRNA-seq studies.
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影响因子：
64.8
作者：
Cao, Junyue;Spielmann, Malte;Shendure, Jay
通讯作者：
Shendure, Jay
DOI：
10.1073/pnas.1721085115
发表时间：
2018-07-10
影响因子：
11.1
作者：
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通讯作者：
Zhang NR
影响因子：
12.3
作者：
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通讯作者：
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影响因子：
48
作者：
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通讯作者：
Kharchenko, Peter V.
影响因子：
64.5
作者：
Peng, Yi-Rong;Shekhar, Karthik;Sanes, Joshua R.
通讯作者：
Sanes, Joshua R.