An interpretable framework for clustering single-cell RNA-Seq datasets.
An interpretable framework for clustering single-cell RNA-Seq datasets.
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DOI：
10.1186/s12859-018-2092-7
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发表时间：
2018-03-09
影响因子：
3
通讯作者：
Tse DN
中科院分区：
文献类型：
--
作者：
Zhang JM;Fan J;Fan HC;Rosenfeld D;Tse DN
随着最近单细胞 RNA 测序实验的激增，已经开发了几种方法来对所得数据集进行无监督分析。这些方法通常依赖于不直观的超参数，并且没有明确解决与聚类相关的主观性。在这项工作中，我们提出了 DendroSplit，一个用于分析单细胞 RNA-Seq 数据集的可解释框架，它解决了聚类可解释性和聚类主观性问题。 DendroSplit 为受“细胞类型”定义启发的单细胞 RNA-Seq 聚类问题提供了新颖的视角，使我们能够使用特征选择进行聚类，以揭示数据中具有生物学意义的群体的多个级别。我们分析了几个具有里程碑意义的单细胞数据集，证明了该方法的功效和计算效率。 DendroSplit 提供了一个聚类框架，在准确性和速度方面与现有方法相当，但在强调可解释性方面是新颖的。我们在 https://github.com/jessemzhang/dendrosplit 提供完整的 DendroSplit 软件包。本文的在线版本 (10.1186/s12859-018-2092-7) 包含补充材料，可供授权用户使用。
With the recent proliferation of single-cell RNA-Seq experiments, several methods have been developed for unsupervised analysis of the resulting datasets. These methods often rely on unintuitive hyperparameters and do not explicitly address the subjectivity associated with clustering. In this work, we present DendroSplit, an interpretable framework for analyzing single-cell RNA-Seq datasets that addresses both the clustering interpretability and clustering subjectivity issues. DendroSplit offers a novel perspective on the single-cell RNA-Seq clustering problem motivated by the definition of “cell type”, allowing us to cluster using feature selection to uncover multiple levels of biologically meaningful populations in the data. We analyze several landmark single-cell datasets, demonstrating both the method’s efficacy and computational efficiency. DendroSplit offers a clustering framework that is comparable to existing methods in terms of accuracy and speed but is novel in its emphasis on interpretabilty. We provide the full DendroSplit software package at https://github.com/jessemzhang/dendrosplit. The online version of this article (10.1186/s12859-018-2092-7) contains supplementary material, which is available to authorized users.
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