JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.
JOINT for large-scale single-cell RNA-sequencing analysis via soft-clustering and parallel computing.
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DOI：
10.1186/s12864-020-07302-6
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发表时间：
2021-01-11
期刊：
影响因子：
4.4
通讯作者：
Wang T
中科院分区：
文献类型：
--
作者：
Cui T;Wang T
单细胞RNA测序（scRNA-Seq）为复杂的生物过程提供了单细胞水平的见解。然而，scRNA-Seq数据中基因表达检测失败的高频率使得实现细胞类型和差异表达基因（DEG）的可靠鉴定具有挑战性。此外，随着使用10倍基因组学协议的单细胞数据的爆炸性增长，现有方法由于可扩展性问题将很快达到计算极限。单细胞转录组学领域迫切需要新的工具和框架来促进大规模的单细胞分析。为了提高scRNA-Seq数据处理的准确性、鲁棒性和速度，我们提出了一种广义零膨胀负二项混合模型“JOINT”，它可以同时进行基于概率的细胞类型发现和DEG分析，而无需进行插补。JOINT通过计算单个细胞的概率来执行细胞类型识别的软聚类，即每个细胞可以属于具有不同概率的多个细胞类型。这与现有的硬聚类方法有很大的不同，在现有的硬聚类方法中，每个细胞只能属于一种细胞类型。该算法的软聚类组件显著促进了单细胞分析的准确性和鲁棒性，特别是当scRNA-Seq数据集有噪声并且包含大量脱落事件时。此外，JOINT能够自动确定细胞类型的最佳数量，而不是凭经验指定。该模型是一个无监督的学习问题，使用期望和最大化（EM）算法来解决。EM算法使用TensorFlow深度学习框架实现，通过并行GPU计算大大加快了数据分析的速度。总之，JOINT算法对于通过并行计算进行大规模scRNA-Seq数据分析是准确和高效的。我们开发的Python包可以很容易地应用于帮助基于并行计算的单细胞算法和各种生物和生物医学领域的研究的未来发展。在线版本包含补充材料，可通过10.1186/s12864-020-07302-6获得。
Single-cell RNA-Sequencing (scRNA-Seq) has provided single-cell level insights into complex biological processes. However, the high frequency of gene expression detection failures in scRNA-Seq data make it challenging to achieve reliable identification of cell-types and Differentially Expressed Genes (DEG). Moreover, with the explosive growth of single-cell data using 10x genomics protocol, existing methods will soon reach the computation limit due to scalability issues. The single-cell transcriptomics field desperately need new tools and framework to facilitate large-scale single-cell analysis. In order to improve the accuracy, robustness, and speed of scRNA-Seq data processing, we propose a generalized zero-inflated negative binomial mixture model, “JOINT,” that can perform probability-based cell-type discovery and DEG analysis simultaneously without the need for imputation. JOINT performs soft-clustering for cell-type identification by computing the probability of individual cells, i.e. each cell can belong to multiple cell types with different probabilities. This is drastically different from existing hard-clustering methods where each cell can only belong to one cell type. The soft-clustering component of the algorithm significantly facilitates the accuracy and robustness of single-cell analysis, especially when the scRNA-Seq datasets are noisy and contain a large number of dropout events. Moreover, JOINT is able to determine the optimal number of cell-types automatically rather than specifying it empirically. The proposed model is an unsupervised learning problem which is solved by using the Expectation and Maximization (EM) algorithm. The EM algorithm is implemented using the TensorFlow deep learning framework, dramatically accelerating the speed for data analysis through parallel GPU computing. Taken together, the JOINT algorithm is accurate and efficient for large-scale scRNA-Seq data analysis via parallel computing. The Python package that we have developed can be readily applied to aid future advances in parallel computing-based single-cell algorithms and research in various biological and biomedical fields. The online version contains supplementary material available at 10.1186/s12864-020-07302-6.
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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
单细胞RNA测序，用于研究发育，生理和疾病。
DOI：
10.1038/s41581-018-0021-7
发表时间：
2018-08
期刊：
Nature reviews. Nephrology
影响因子：
--
作者：
Potter SS
通讯作者：
Potter SS
影响因子：
64.5
作者：
van Dijk D;Sharma R;Nainys J;Yim K;Kathail P;Carr AJ;Burdziak C;Moon KR;Chaffer CL;Pattabiraman D;Bierie B;Mazutis L;Wolf G;Krishnaswamy S;Pe'er D
通讯作者：
Pe'er D
影响因子：
48
作者：
Kharchenko, Peter V.;Silberstein, Lev;Scadden, David T.
通讯作者：
Scadden, David T.
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y