Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method
Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method
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DOI：
10.1002/jcb.26507
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发表时间：
2018-04-01
影响因子：
4
通讯作者：
Cai, Yu-Dong
中科院分区：
文献类型：
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作者：
Chen, Lei;Li, JiaRui;Cai, Yu-Dong
成体神经干细胞（NSCs）是一类多能、自我更新的祖细胞，可促进新神经元和少突胶质细胞的产生。根据神经干细胞谱系的阶段，可以分离出三种神经干细胞亚型，包括静止神经干细胞（qNSC）、激活神经干细胞（aNSC）和神经祖细胞（NPC）。虽然人们普遍认为这三组NSC在神经系统的发育中发挥不同的作用，但对其分子特征知之甚少。在这项研究中，我们应用蒙特-卡罗特征选择（MCFS）方法来识别基因表达标签，它可以产生一个马修斯相关系数（MCC）值为0.918的支持向量机评估的10倍交叉验证。此外，还报道了MCFS程序对上述三种亚型的分类规则。我们的研究结果不仅证明了高的分类能力和亚型特异性基因表达模式，而且定量地反映了整个NSC谱系的基因表达水平的模式，为破译NSC分化的分子基础提供了见解。
Adult neural stem cells (NSCs) are a group of multi-potent, self-renewing progenitor cells that contribute to the generation of new neurons and oligodendrocytes. Three subtypes of NSCs can be isolated based on the stages of the NSC lineage, including quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs). Although it is widely accepted that these three groups of NSCs play different roles in the development of the nervous system, their molecular signatures are poorly understood. In this study, we applied the Monte-Carlo Feature Selection (MCFS) method to identify the gene expression signatures, which can yield a Matthews correlation coefficient (MCC) value of 0.918 with a support vector machine evaluated by ten-fold cross-validation. In addition, some classification rules yielded by the MCFS program for distinguishing above three subtypes were reported. Our results not only demonstrate a high classification capacity and subtype-specific gene expression patterns but also quantitatively reflect the pattern of the gene expression levels across the NSC lineage, providing insight into deciphering the molecular basis of NSC differentiation.