Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method

4 years ago

Identification of gene expression signatures across different types of neural stem cells with the Monte-Carlo feature selection method

Lei Chen, JiaRui Li, Xiangyin Kong, YunHua Zhang, ShaoPeng Wang, Yu-Dong Cai, KaiYan Feng, Tao Huang, Yu-Hang Zhang

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. This article is protected by copyright. All rights reserved

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/jcb.26507