IEEE/ACM Trans Comput Biol Bioinform. 2021 Nov-Dec;18(6):2445-2458. doi: 10.1109/TCBB.2020.2971993. Epub 2021 Dec 8.
Transcriptomic profiling plays an important role in post-genomic analysis. Especially, the single-cell RNA-seq technology has advanced our understanding of gene expression from cell population level into individual cell level. Many computational methods have been proposed to decipher transcriptomic profiles from those RNA-seq data. However, most of the related algorithms suffer from realistic restrictions such as high dimensionality and premature convergence. In this paper, we propose and formulate an evolutionary multiobjective blind compressed sensing (EMOBCS) to address those problems for evolving transcriptomic profiles from single-cell RNA-seq data. In the proposed framework, to characterize various gene expression profile models, two objective functions including chi-squared kernel score and euclidean distance of different gene expression profiles are formulated. After that, multiobjective blind compressed sensing based on artificial bee colony is designed to optimize the two objective functions on single-cell RNA-seq data by proposing a rank probability model and two new search strategies into the cooperative convolution framework in an unbiased manner. To demonstrate its effectiveness, extensive experiments have been conducted, comparing the proposed algorithm with 14 algorithms including eight state-of-the-art algorithms and six different EMOBCS algorithms under different search strategies on 10 single-cell RNA-seq datasets and one case study. The experimental results reveal that the proposed algorithm is better than or comparable with those compared algorithms. Furthermore, we also conduct the time complexity analysis, convergence analysis, and parameter analysis to demonstrate various properties of EMOBCS.
转录组谱分析在基因组分析后起着重要作用。特别是，单细胞 RNA-seq 技术使我们能够从细胞群体水平深入了解基因表达到单个细胞水平。已经提出了许多计算方法来从这些 RNA-seq 数据中破译转录组谱。然而，大多数相关算法都受到现实限制的影响，例如高维性和过早收敛。在本文中，我们提出并制定了一种进化多目标盲压缩感知（EMOBCS）方法，以解决从单细胞 RNA-seq 数据中进化转录组谱的这些问题。在提出的框架中，为了描述各种基因表达谱模型，我们制定了两个目标函数，包括卡方核得分和不同基因表达谱的欧几里得距离。之后，基于人工蜂群设计了多目标盲压缩感知，通过在合作卷积框架中提出秩概率模型和两种新的搜索策略，以无偏的方式优化单细胞 RNA-seq 数据上的两个目标函数。为了证明其有效性，我们在 10 个单细胞 RNA-seq 数据集和一个案例研究上，针对 14 种算法（包括 8 种最先进的算法和 6 种不同的 EMOBCS 算法）进行了广泛的实验，比较了所提出的算法与这些算法的性能。实验结果表明，所提出的算法优于或与比较算法相当。此外，我们还进行了时间复杂度分析、收敛分析和参数分析，以展示 EMOBCS 的各种性质。
