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.

中文翻译：

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使用自然启发的多目标优化从单细胞 RNA-Seq 数据演变转录组图谱

转录组分析在后基因组分析中起着重要作用。尤其是单细胞 RNA-seq 技术，将我们对基因表达的理解从细胞群体水平提升到了单个细胞水平。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，例如高维和过早收敛。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)，以解决从单细胞 RNA-seq 数据进化转录组谱的问题。在提出的框架中，为了表征各种基因表达谱模型，制定了两个目标函数，包括卡方核分数和不同基因表达谱的欧几里得距离。之后，设计了基于人工蜂群的多目标盲压缩感知，通过在协同卷积框架中无偏地提出秩概率模型和两种新的搜索策略，优化单细胞 RNA-seq 数据的两个目标函数。为了证明其有效性，已经进行了广泛的实验，将所提出的算法与 14 种算法进行比较，其中包括 8 种最先进的算法和 6 种不同的 EMOBCS 算法，在 10 个单细胞 RNA-seq 数据集和一个案例研究的不同搜索策略下. 实验结果表明，所提出的算法优于或可与那些比较算法相媲美。