The functional or regulatory processes within the cell are explicitly governed by the expression levels of a subset of its genes. Gene expression time series captures activities of individual genes over time and aids revealing underlying cellular dynamics. An important step in high-throughput gene expression time series experiment is clustering genes based on their temporal expression patterns and is conventionally achieved by unsupervised machine learning techniques. However, most of the clustering techniques either suffer from the short length of gene expression time series or ignore temporal structure of the data. In this work, we propose DeepTrust, a novel deep learning-based framework for gene expression time series clustering which can overcome these issues. DeepTrust initially transforms time series data into images to obtain richer data representations. Afterwards, a deep convolutional clustering algorithm is applied on the constructed images. Analyses on both simulated and biological data sets exhibit the efficiency of this new framework, compared to widely used clustering techniques. We also utilize enrichment analyses to illustrate the biological plausibility of the clusters detected by DeepTrust. Our code and data are available from http://github.com/tanlab/DeepTrust .

中文翻译：

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基因表达时间序列的卷积深度聚类框架

细胞内的功能或调节过程明确地受其基因子集的表达水平控制。基因表达时间序列捕获单个基因随时间的活动，并有助于揭示潜在的细胞动力学。高通量基因表达时间序列实验的一个重要步骤是根据基因的时间表达模式对基因进行聚类，通常通过无监督机器学习技术来实现。然而，大多数聚类技术要么受到基因表达时间序列长度短的影响，要么忽略数据的时间结构。在这项工作中，我们提出了 DeepTrust，这是一种新的基于深度学习的基因表达时间序列聚类框架，可以克服这些问题。DeepTrust 最初将时间序列数据转换为图像以获得更丰富的数据表示。之后，对构建的图像应用深度卷积聚类算法。与广泛使用的聚类技术相比，对模拟数据集和生物数据集的分析展示了这种新框架的效率。我们还利用富集分析来说明 DeepTrust 检测到的集群的生物学合理性。我们的代码和数据可从http://github.com/tanlab/DeepTrust .