Fast and precise single-cell data analysis using a hierarchical autoencoder.
Fast and precise single-cell data analysis using a hierarchical autoencoder.
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使用分层自动编码器进行快速、精确的单细胞数据分析。
DOI：
10.1038/s41467-021-21312-2
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发表时间：
2021-02-15
影响因子：
16.6
通讯作者：
Nguyen T
中科院分区：
文献类型：
--
作者：
Tran D;Nguyen H;Tran B;La Vecchia C;Luu HN;Nguyen T
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A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical Autoencoder (scDHA), that reliably extracts representative information of each cell. The scDHA pipeline consists of two core modules. The first module is a non-negative kernel autoencoder able to remove genes or components that have insignificant contributions to the part-based representation of the data. The second module is a stacked Bayesian autoencoder that projects the data onto a low-dimensional space (compressed). To diminish the tendency to overfit of neural networks, we repeatedly perturb the compressed space to learn a more generalized representation of the data. In an extensive analysis, we demonstrate that scDHA outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference. Accurate analysis of single-cell RNA sequencing (scRNA-seq) data is affected by issues including technical noise and high dropout rate. Here, the authors develop a hierarchical autoencoder, scDHA, which outperforms existing methods in scRNA-seq analyses such as cell segregation and classification.
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影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
影响因子：
64.5
作者：
Goolam M;Scialdone A;Graham SJL;Macaulay IC;Jedrusik A;Hupalowska A;Voet T;Marioni JC;Zernicka-Goetz M
通讯作者：
Zernicka-Goetz M
影响因子：
64.5
作者：
Davie K;Janssens J;Koldere D;De Waegeneer M;Pech U;Kreft Ł;Aibar S;Makhzami S;Christiaens V;Bravo González-Blas C;Poovathingal S;Hulselmans G;Spanier KI;Moerman T;Vanspauwen B;Geurs S;Voet T;Lammertyn J;Thienpont B;Liu S;Konstantinides N;Fiers M;Verstreken P;Aerts S
通讯作者：
Aerts S
DOI：
10.1073/pnas.1507125112
发表时间：
2015-06-09
影响因子：
11.1
作者：
Darmanis S;Sloan SA;Zhang Y;Enge M;Caneda C;Shuer LM;Hayden Gephart MG;Barres BA;Quake SR
通讯作者：
Quake SR