Intratumoral heterogeneity and the presence of cancer stem cells are challenging issues in cancer therapy. An appropriate quantification of the stemness of individual cells for assessing the potential for self-renewal and differentiation from the cell of origin can define a measurement for quantifying different cell states, which is important in understanding the dynamics of cancer evolution, and might further provide possible targeted therapies aimed at tumor stem cells. Nevertheless, it is usually difficult to quantify the stemness of a cell based on molecular information associated with the cell. In this study, we proposed a stemness definition method with one-class Hadamard kernel support vector machine (OCHSVM) based on single-cell RNA sequencing (scRNA-seq) data. Applications of the proposed OCHSVM stemness are assessed by various data sets, including preimplantation embryo cells, induced pluripotent stem cells, or tumor cells. We further compared the OCHSVM model with state-of-the-art methods CytoTRACE, one-class logistic regression, or one-class SVM methods with different kernels. The computational results demonstrate that the OCHSVM method is more suitable for stemness identification using scRNA-seq data.

中文翻译：

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单细胞 RNA 测序数据中干性的定量建模：一种非线性一类支持向量机方法。


肿瘤内异质性和癌症干细胞的存在是癌症治疗中具有挑战性的问题。对单个细胞的干性进行适当的量化，以评估自我更新和从起源细胞分化的潜力，可以定义量化不同细胞状态的测量方法，这对于理解癌症进化的动态非常重要，并且可能进一步提供可能针对肿瘤干细胞的靶向治疗。然而，通常很难根据与细胞相关的分子信息来量化细胞的干性。在本研究中，我们提出了一种基于单细胞RNA测序（scRNA-seq）数据的单类Hadamard核支持向量机（OCHSVM）的干性定义方法。所提出的 OCHSVM 干性的应用通过各种数据集进行评估，包括植入前胚胎细胞、诱导多能干细胞或肿瘤细胞。我们进一步将 OCHSVM 模型与最先进的方法 CytoTRACE、一类逻辑回归或具有不同内核的一类 SVM 方法进行比较。计算结果表明OCHSVM方法更适合使用scRNA-seq数据进行干性识别。